Speaker:
Professor Meng Li
Assistant Professor
Institute for Artificial Intelligence, School of Integrated Circuits, Peking University

Abstract:
Recent years have witnessed the fast evolution of AI and deep learning (DL) in the field computer vision, natural language processing, etc. Though promising, DL faces serious challenges due to the exponential network scaling and network heterogeneity. In this talk, I will discuss some of our recent works that leverage network/hardware co-design and co-optimization to improve the efficiency for DL. I will cover our recent works on tiny language model for MCUs, memory-aware scheduling, and hardware accelerator designs based on a new computing paradigm, i.e., stochastic computing. I will also discuss interesting future directions to further improve the efficiency and security for efficient AI.

Biography:
Prof. Meng Li is currently a tenure-track assistant professor in Peking University, jointly affiliated with Institute for Artificial Intelligence and School of Integrated Circuits. Before joining Peking University, he was staff research scientist and tech lead in Meta Reality Lab, the world’s largest social media company, focusing on research and productization of efficient AI algorithms and hardware/systems for next generation AR/VR devices. Dr. Li received his Ph.D. degree from the University of Texas at Austin in 2018 and his bachelor degree from Peking University in 2013.

Prof. Meng Li’s research interests lie in the field of efficient and secure multi-modal AI acceleration algorithms and hardware. He has published more than 60 papers and received two best paper awards from HOST 2017 and GLSVLSI 2018. He also receives EDAA Outstanding Dissertation Award, First Place in ACM Student Research Competition Grand Final (Graduate Category), Best Poster Awards in ASPDAC Student Research Forum, etc.

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )

Speaker:
Mr. Junyang Lin
Staff Engineer, Leader of Qwen Team,
Alibaba Group

Abstract:
This talk introduces the large language and multimodal model series Qwen, which stands for Tongyi Qianwen (通义千问), published and opensourced by Alibaba Group. The Qwen models have achieved competitive performance against both opensource and proprietary LLMs and LMMs in both benchmark evaluation and human evaluation. This talk provides a brief overview of the model series, and then delves into details about building the LLMs and LMMs, including pretraining, alignment, multimodal extension, as well as the opensource. Additionally, it points out the limitations, and discusses the future work for both research community and industry in this field.

Biography:
Mr. Junyang Lin is a staff engineer of Alibaba Group, and he is now a leader of Qwen Team. He has been doing research in natural language processing and multimodal representation learning, with a focus on large-scale pretraining, and he has around 3000 citations. Recently his team released and opensourced the Qwen series, including large language model Qwen, large vision-language model Qwen-VL, and large audio-language model Qwen-Audio. Previously, he focused on building large-scale pretraining with a focus on multimodal pretraining, and developed opensourced models OFA, Chinese-CLIP, etc.

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )

Speaker:
Professor Andrej Bogdanov
Professor
School of Electrical Engineering and Computer Science, University of Ottawa

Abstract:
A distinguisher is an algorithm that tells whether its input was sampled from one distribution or from another. The computational complexity of distinguishers is important for much of cryptography, pseudorandomness, and statistical inference.

We study the relative advantage of classical and quantum distinguishers of bounded query complexity over n-bit strings. Our focus is on a single quantum query, which is already quite powerful: Aaronson and Ambainis (STOC 2015) constructed a pair of distributions that is 𝜀-distinguishable by a one-query quantum algorithm, but O(𝜀k/√n)-indistinguishable by any non-adaptive k-query classical algorithm.

We show that every pair of distributions that is 𝜀-distinguishable by a one-query quantum algorithm is distinguishable with k classical queries and (1) advantage min{𝛺(𝜀√(k/n)), 𝛺(𝜀^2k^2/n)} non-adaptively (i.e., in one round), and (2) advantage 𝛺(𝜀^2k/√(n log n)) in two rounds. The second bound is tight in k and n up to a (log n) factor.

Based on joint work with Tsun Ming Cheung (McGill), Krishnamoorthy Dinesh (IIT Palakkad), and John C.S. Lui (CUHK)

Biography:
Prof. Andrej Bogdanov is a professor in the School of Electrical Engineering and Computer Science at the University of Ottawa. He is interested in cryptography, pseudorandomness, and computational complexity.  Andrej obtained his Ph.D. from UC Berkeley. Before joining uOttawa he taught at the Chinese University of Hong Kong.  He was a visiting professor at the Tokyo Institute of Technology in 2013 and at the Simons Institute for the Theory of Computing in 2017 and 2021.

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )

Speaker:
Professor Hau Chan
Assistant Professor
School of Computing, University of Nebraska-Lincoln

Abstract:

In the last few decades, game theory has become a prominent construct for modeling and predicting outcomes of strategic interactions of rational agents in various real-world environments, ranging from adversarial (e.g., attacker-defender in the security domain) to collaborative (e.g., public good contributions). In terms, these predicted outcomes can be used to facilitate, inform, and improve agents’ and policymakers’ decision-making. Unfortunately, because of the domain characteristics in real-world environments, classical game-theoretic modeling and computational approaches (for predicting outcomes) can often take exponential space and time.

In this talk, I will discuss compact AI representations for strategic interactions (or games) to provide efficient approaches for a wide range of applications. I will demonstrate how they can be used to model and predict outcomes in scenarios we examined previously such as attacker-defenders, resource congestions, residential segregations, and public project contributions.

More specifically, I will first present aggregate games, a compact AI representation of games where each agent’s utility function depends on their own actions and the aggregation or summarization of the actions of all agents, and resource graph games, a compact AI representation of games where agents have exponential numbers of actions. For these games, I will then present our computational results for determining and computing Nash Equilibria (NE), a fundamental solution concept to specify predicted outcomes in games, and their related problems.

Biography:

Prof. Hau Chan is an assistant professor in the School of Computing at the University of Nebraska-Lincoln. He received his Ph.D. in Computer Science from Stony Brook University in 2015 and completed three years of Postdoctoral Fellowships, including at the Laboratory for Innovation Science at Harvard University in 2018. His main research areas focus on modeling and algorithmic aspects of AI and multi-agent interactions (e.g., via game theory, mechanism design, and applied machine learning), addressing several cross-disciplinary societal problems and applications. His recent application areas include improving accessibility to public facilities, reducing substance usage, and making fair collective decisions. His research has been supported by NSF, NIH, and USCYBERCOM. He has received several Best Paper Awards at SDM and AAMAS and distinguished/outstanding SPC/PC member recognitions at IJCAI and WSDM. He has given tutorials and talks on computational game theory and mechanism design at venues such as AAMAS and IJCAI, including an Early Career Spotlight at IJCAI 2022. He has served as a co-chair for Demonstrations, Doctoral Consortium, Scholarships, and Diversity & Inclusion Activities at AAMAS and IJCAI.

Join Zoom Meeting:
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Meeting ID: 938 3693 9970
Passcode: 202300

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )

Speaker:
Professor Zhizhen Zhao
William L. Everitt Fellow and Associate Professor
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

Abstract:

Cryo-electron microscopy (EM) single particle reconstruction is an entirely general technique for 3D structure determination of macromolecular complexes. This talk focuses on the algorithms for 2D class averaging and 3D reconstruction for the single-particle images, assuming no conformation changes of the macromolecules. In the first part, I will introduce the multi-frequency vector diffusion maps to improve the efficiency and accuracy of cryo-EM 2D image classification and denoising. This framework incorporates different irreducible representations of the estimated alignment between similar images. In addition, we use a graph filtering scheme to denoise the images using the eigenvalues and eigenvectors of the MFVDM matrices. In the second part, I will present a 3D reconstruction approach, which follows a line of works starting from Kam (1977) that employs the autocorrelation analysis for the single particle reconstruction. Our approach does not require per image pose estimation and imposes spatial non-negativity constraint. At the end of the talk, I will briefly review the challenges and existing approaches for addressing the continuous heterogeneity in cryo-EM data.

Biography:

Prof. Zhizhen Zhao is an Associate Professor and William L. Everitt Fellow in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. She joined University of Illinois in 2016. From 2014 to 2016, she was a Courant Instructor at the Courant Institute of Mathematical Sciences, New York University. She received the B.A. and M.Sc. degrees in physics from Trinity College, Cambridge University in 2008, and the Ph.D. degree in physics from Princeton University in 2013. She is a recipient of Alfred P. Sloan Research Fellowship (2020). Her research interests include computational imaging, data science, and machine learning.

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )

Speaker:
Dr. Lauren Pick
Postdoctoral Researcher
Department of Computer Sciences, University of Wisconsin-Madison and Department of Electrical Engineering and Computer Sciences, University of California, Berkeley

Abstract:

Given the critical role of software systems in society, it is important that we understand how such systems behave and interact. Formal specifications can help us in this task by providing rigorous and unambiguous descriptions of system behaviors. Automated verification can be applied to automate the process of proving formal specifications hold for software systems, making it easier to ensure that the underlying systems function as intended. Unfortunately, the application of automated verification to real-world systems remains hindered by scalability limitations. In this talk, I describe my work on addressing these limitations by leveraging the problem-specific structure of specifications and systems. I specifically illustrate my approach for handling concrete problems in security and distributed domains, where taking advantage of structure enables scalable verification.

Biography:
Dr. Lauren Pick is a postdoctoral researcher at the University of California, Berkeley and the University of Wisconsin-Madison. She received her Ph.D. from Princeton University in January 2022. Her research focuses on developing techniques for automated verification and synthesis, with the goal of enabling formal reasoning about real-world systems. To this end, she has developed techniques that take advantage of structural aspects of target systems and their desired properties to enable efficient verification and synthesis. She is a Computing Innovation fellow and was a recipient of the NSF GRFP Fellowship.

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )

Speaker:
Professor Mingqiang Wei
Professor
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA)

Abstract:

Large aircraft, as one of the most complex high-end equipment in modern society, is the culmination of interdisciplinary and cross-domain advanced technologies, occupying the top of the manufacturing industry’s technology and value chains. With the emergence of a batch of national key equipment such as the Y-20, C919, and Jiaolong-600, China has made breakthrough progress in large aircraft manufacturing and gradually established a relatively complete production and development system. However, due to insufficient technological foundation and compared with international aerospace manufacturing giants, Chinese aviation enterprises have not yet achieved integrated manufacturing and measurement capabilities or effective precision control capabilities. The “high-precision rapid 3D scanning analysis and quality control technology” has become an important factor affecting the development process of large aircraft in China. Geometric deep learning, with its powerful ability to learn geometric features, has shown great potential in the analysis of large aircraft shapes. However, existing network structures lack domain-specific expertise in aviation, there is no publicly available large-scale aircraft 3D dataset, and the latest machine learning technologies have not been deeply integrated into the field of geometric deep learning, making it difficult to comprehensively and efficiently analyze the complex features and stringent accuracy requirements of large aircraft shapes. This report will introduce the interdisciplinary technical issues involved in the analysis of large aircraft shapes.

Biography:

Prof. Mingqiang Wei received his Ph.D. degree (2014) in Computer Science and Engineering from the Chinese University of Hong Kong (CUHK). He is a professor at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA). He was the recipient of Excellent Youth Fund Project of the National Natural Science Foundation of China in 2023. Before joining NUAA, he served as an assistant professor at Hefei University of Technology, and a postdoctoral fellow at CUHK. He was a recipient of the CUHK Young Scholar Thesis Awards in 2014. He is now an Associate Editor for ACM TOMM, The Visual Computer Journal, Journal of Electronic Imaging, and a Guest Editor for IEEE Transactions on Multimedia. His research interests focus on 3D vision, computer graphics, and deep learning.

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )

Speaker:
Professor Alexander Schwing
Associate Professor
Department of Electrical and Computer Engineering & Department of Computer Science, University of Illinois at Urbana-Champaign

Abstract:

Our goal is to develop methods which anticipate. For this, four foundational questions need to be answered: (1) How can methods accurately forecast high-dimensional observations?; (2) How can algorithms holistically understand objects, e.g., when reasoning about occluded parts?; (3) How can accurate probabilistic models be recovered from limited amounts of labeled data and for rare events?; and (4) How can autonomous agents be trained effectively to collaborate?

In this talk we present vignettes of our research to address those questions. We start by discussing MaskFormer and Mask2Former, a recent architecture which achieves state-of-the-art results on three tasks: panoptic, instance and semantic segmentation. We then discuss the importance of memory for video object segmentation and its combination with foundation models for open-world segmentation. Finally, and if time permits, we discuss SDFusion, a generative model to infer parts of an object that are unobserved. For additional info and questions, please browse to http://alexander-schwing.de.

Biography:

Prof. Alexander Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on computer vision and machine learning topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016. His research interests are in the area of computer vision and machine learning, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award. For additional info, please browse to http://alexander-schwing.de.

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )

Speaker:
Professor David Atienza
Professor
Department of Electrical and Computer Engineering, The École Polytechnique Fédérale de Lausanne (EPFL)

Abstract:

Edge computing is becoming an essential concept covering multiple domains nowadays as our world becomes increasingly connected to enable the Internet of Things (IoT) concept. In addition, the new wave of Artificial Intelligence (AI), particularly complex Machine Learning (ML) and Deep Learning (DL) models, is demanding new computing paradigms beyond traditional general-purpose computing to make IoT a viable reality in a sustainable world.

In this seminar, Prof. Atienza will discuss new approaches to effectively design the next generation of edge AI computing architectures by taking inspiration from how biological computing systems operate. In particular, these novel bioinspired edge AI architectures includes two key concepts. First, it exploits the idea of accepting computing inexactness and integrating multiple computing acceleration engines and low-power principles to create a new open-source eXtended and Heterogeneous Energy-Efficient hardware Platform (called x-HEEP). Second, x-HEEP can be instantiated for different application domains of edge AI to operate ensembles of neural networks to improve the ML/DL outputs’ robustness at system level, while minimizing memory and computation resources for the target application. Overall, x-HEEP instantiations for edge AI applications included in-memory computing or run-time reconfigurable coarse-grained accelerators to minimize energy according to the required precision of the target application.

Biography:

Prof. David Atienza is a professor of Electrical and Computer Engineering, and leads both the Embedded Systems Laboratory (ESL) and the new EcoCloud Sustainable Computing Center at EPFL, Switzerland. He received his M.Sc. and Ph.D. degrees in Computer Science and Engineering from UCM (Spain) and IMEC (Belgium). His research interests include system-level design methodologies for high-performance multi-processor system-on-chip (MPSoC) and low-power Internet-of-Things (IoT) systems, including edge AI architectures for wearables and IoT systems as well as thermal-aware designs for MPSoCs and many-core servers. He is a co-author of more than 400 papers, two books, and has 14 licensed patents in these topics. He served as DATE General Chair and Program Chair, and is currently Editor-in-Chief of IEEE TCAD. Among others, Dr. Atienza has received the ICCAD 10-Year Retrospective Most Influential Paper Award, the DAC Under-40 Innovators Award, the IEEE TC-CPS Mid-Career Award, and the ACM SIGDA Outstanding Faculty Award. He is a Fellow of IEEE, a Fellow of ACM, served as IEEE CEDA President (period 2018-2019), and he is currently the Chair of the European Design Automation Association (EDAA).

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )

Speaker:
Prof. Shengdong Zhao
Associate Professor
Department of Computer Science, National University of Singapore

Abstract:

Heads-up computing is an emerging concept in human-computer interaction (HCI) that focuses on natural and intuitive interaction with technology. By making technology more seamlessly integrated into our lives, heads-up computing has the potential to revolutionize the way we interact with devices. With the rise of large language models (LLMs) such as ChatGPT and GPT4, the vision of heads-up computing is becoming much easier to realize. The combination of LLMs and heads-up computing can create more proactive, personalized, and responsive systems that are more human-centric. However, technology is a double-edged sword. While technology provides us with great power, it also comes with the responsibility to ensure that it is used ethically and for the benefit of all. That’s why it is essential to place fundamental human values at the center of research programs and work collaboratively among disciplines. As we navigate through this historic transition, it is crucial to shape a future that reflects our values and enhances our quality of life.

Biography:

Dr. Shengdong Zhao is an Associate Professor in the Department of Computer Science at the National University of Singapore, where he established and leads the NUS-HCI research lab. He received his Ph.D. degree in Computer Science from the University of Toronto and a Master’s degree in Information Management & Systems from the University of California, Berkeley. With a wealth of experience in developing new interface tools and applications, Dr. Zhao regularly publishes his research in top-tier HCI conferences and journals. He has also worked as a senior consultant with the Huawei Consumer Business Group in 2017. In addition to his research, Dr. Zhao is an active member of the HCI community, frequently serving on program committees for top HCI conferences and as the paper chair for the ACM SIGCHI 2019 and 2020 conferences. For more information about Dr. Zhao and the NUS-HCI lab, please visit http://www.shengdongzhao.com and http://www.nus-hci.org .

Enquiries: Ms Anna Wong (annawong@cse.cuhk.edu.hk)

Speaker:
Prof. Yizheng Chen
Assistant Professor
Department of Computer Science, University of Maryland

Abstract:

Artificial Intelligence is becoming more powerful than ever, e.g., GitHub Copilot suggests code to developers, and Large Language Model (LLM) Plugins will soon assist many tasks in our daily lives. We can utilize the power of AI to solve security problems, which needs to be robust against new attacks and new vulnerabilities.
In this talk, I will first discuss how to develop robust AI techniques for malware detection. Our research finds that, after training an Android malware classifier on one year’s worth of data, the F1 score quickly dropped from 0.99 to 0.76 after 6 months of deployment on new test samples. I will present new methods to make machine learning for Android malware detection more effective against data distribution shift. My vision is, continuous learning with a human-in-the-loop setup can achieve robust malware detection. Our results show that to maintain a steady F1 score over time, we can achieve 8X reduction in labels indeed from security analysts.
Next, I will discuss the potential of using large language models to solve security problems, using vulnerable source code detection as a case study. We propose and release a new vulnerable source code dataset, DiverseVul. Using the new dataset, we study 11 model architectures belonging to 4 families for vulnerability detection. Our results indicate that developing code-specific pre-training tasks is a promising research direction of using LLMs for security. We demonstrate an important generalization challenge for the deployment of deep learning-based models.
In closing, I will discuss security issues of LLMs and future research directions.

Biography:

Yizheng Chen is an Assistant Professor of Computer Science at University of Maryland. She works at the intersection of AI and security. Her research focuses on AI for Security and robustness of AI models. Previously, she received her Ph.D. in Computer Science from the Georgia Institute of Technology, and was a postdoc at University of California, Berkeley and Columbia University. Her work has received an ACM CCS Best Paper Award Runner-up and a Google ASPIRE Award. She is a recipient of the Anita Borg Memorial Scholarship.

Enquiries: Ms Anna Wong (annawong@cse.cuhk.edu.hk)

Speaker:
Prof. Xiaolong Wang
Associate Professor
Department of Electrical and Computer Engineering, University of California, San Diego

Abstract:

Robot learning has witnessed significant progress in terms of generalization in the past few years. At the heart of such a generalization, the advancement of representation learning, such as image and text foundation models plays an important role. While these achievements are encouraging, most tasks conducted are relatively simple. In this talk, I will talk about our recent efforts on learning generalizable skills focusing on tasks with complex physical contacts and geometric reasoning. Specifically, I will discuss our research on: (i) the use of a large number of low-cost, binary force sensors to enable Sim2Real manipulation; (ii) unifying 3D and semantic representation learning to generalize policy learning across diverse objects and scenes. I will showcase the real-world applications of our research, including dexterous manipulation, language-driven manipulation, and legged locomotion control.

Biography:

Xiaolong Wang is an Assistant Professor in the ECE department at the University of California, San Diego, affiliated with the TILOS NSF AI Institute. He received his Ph.D. in Robotics at Carnegie Mellon University. His postdoctoral training was at the University of California, Berkeley. His research focuses on the intersection between computer vision and robotics. His specific interest lies in learning 3D and dynamics representations from videos and physical robotic interaction data. These comprehensive representations are utilized to facilitate the learning of robot skills, with the goal of generalizing the robot to interact effectively with a wide range of objects and environments in the real physical world. He is the recipient of the NSF CAREER Award, Intel Rising Star Faculty Award, and Research Awards from Sony, Amazon, Adobe, and Cisco.

Enquiries: Ms Anna Wong (annawong@cse.cuhk.edu.hk)

Speaker:
Dr. LIU Sifei

Abstract:

Disentangled representation in computer vision refers to encoding visual data into distinct, independent factors. These representations are critical for enhancing interpretability, improving generalization across tasks, and enabling controlled manipulation of specific visual attributes. Learning disentangled representation is challenging, primarily because obtaining ground-truth factorizations is often elusive.

In this talk, I will discuss our latest efforts to extract disentangled representations from GANs and diffusion models, for both 2D images and 3D textured shapes. I will demonstrate how, in the absence of annotations, our approaches can discern and extract fine-grained structural information, such as correspondence maps, in a self-supervised manner. Building on this space, I will introduce our work on a generalizable network designed for controlled generation and editing in a feed-forward paradigm. Additionally, I will spotlight our recent exploration into generating hand-object interactions, leveraging the disentanglement of layout and content through image diffusion models.

Biography:

Dr. LIU Sifei is a staff-level Senior Research Scientist at NVIDIA, where she is part of the LPR team led by Jan Kautz. Her work primarily revolves around the development of generalizable visual representation and data-efficiency learning for images, videos, and 3D contents. Prior to this, she pursued her Ph.D. at the VLLAB, under the guidance of Ming-Hsuan Yang. Sifei had received several prestigious awards and recognitions. In 2013, she was honored with the Baidu Graduate Fellowship. This was followed by the NVIDIA Pioneering Research Award in 2017, and the Rising Star EECS accolade in 2019. Additionally, she was nominated for the VentureBeat Women in AI Award in 2020.

Enquiries: Ms Anna Wong (annawong@cse.cuhk.edu.hk)

Speaker:
Prof. DAI Hong-Ning
Associate Professor
Department of Computing Science, Hong Kong Baptist University (HKBU)

Abstract:

The metaverse is essentially constructed by multiple technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), artificial intelligence (AI), digital twin (DT), blockchain, and 5G communications. The advent of the metaverse has proliferated a number of VR/AR apps on top of diverse VR/AR devices, such as Meta Quest 2, MS Hololens, Sony PlayStation VR, ByteDance Pico, and Apple Vision Pro. Meanwhile, diverse metaverse applications have emerged, such as gaming, healthcare, industry, creator economy, and digital arts. However, the current development of the metaverse is still in its early stage because of the complexity and heterogeneity of the entire system, which cannot be scalable to fulfill the increasing number of participants as well as the stringent demands of metaverse applications. Moreover, emerging security vulnerabilities and privacy-leakage concerns have also prevented the metaverse from wide adoption. In this talk, I will first briefly review the Metaverse as well as relevant technologies. I will then elaborate on its challenges as well as potential solutions. Finally, I will discuss several future directions in this promising area.

Biography:

Hong-Ning Dai is an associate professor in the Department of Computer Science, Hong Kong Baptist University (HKBU). He obtained a Ph.D. degree in Computer Science and Engineering from The Chinese University of Hong Kong. Before joining HKBU, he has more than 10-year academic experience in the Chinese University of Hong Kong, Macau University of Science and Technology (Macau), and Lingnan University (Hong Kong). His current research interests include the Internet of Things, Blockchain, and Big Data Analytics. Prof. Dai has published more than 200 papers in referred journals and conferences. His publications have received more than 15,000 citations. He was also included in the world’s top 2% scientists for career-long impact (2022, 2021) by Stanford University, USA. He was also conferred on AI 2000 Most Influential Scholar Award (Honorable Mention) in Internet of Things, 2023. He is the holder of 1 U.S. patent. He is the senior member of IEEE and ACM. Prof. Dai has served as an associate editor for IEEE Communications Surveys & Tutorials, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Industrial Informatics, IEEE Transactions on Industrial Cyber-Physical Systems, Ad Hoc Networks (Elsevier), and Connection Science (Taylor & Francis).

Enquiries: Ms Anna Wong (annawong@cse.cuhk.edu.hk)

Speaker:
Prof. Peter Kazanzides
Research  Professor
Department of Computing Science, Johns Hopkins University

Abstract:

The da Vinci Research Kit (dVRK) is an open research platform that couples open-source control electronics and software with the mechanical components of the da Vinci surgical robot. This presentation will describe the dVRK system architecture, followed by selected research enabled by this system, including mixed reality for the first assistant, autonomous camera motion, and force estimation for bilateral teleoperation. The presentation will conclude with an overview of the AccelNet Surgical Robotics Challenge, which includes both simulated and physical environments.

Biography:

Peter Kazanzides received the Ph.D. degree in electrical engineering from Brown University in 1988. He began work on surgical robotics in March 1989 as a postdoctoral researcher at the IBM T.J. Watson Research Center and co-founded Integrated Surgical Systems (ISS) in November 1990. As Director of Robotics and Software at ISS, he was responsible for the design, implementation, validation and support of the ROBODOC System, which has been used for more than 20,000 hip and knee replacement surgeries. Dr. Kazanzides joined Johns Hopkins University December 2002 and currently holds an appointment as a Research Professor of Computer Science. His research focuses on computer-integrated surgery, space robotics and mixed reality.

Enquiries: Ms Anna Wong (annawong@cse.cuhk.edu.hk)

Speaker:
Dr. HAO Cong, Callie
Assistant Professor
Department of Electrical and Computer Engineering (ECE), Georgia Institute of Technology (GaTech)

Abstract:

In this talk, we introduce two architectures, one for graph neural work (GNN) called FlowGNN, one for vision transformer (ViT) called Edge-MoE. In FlowGNN, a generic dataflow architecture for GNN acceleration is proposed, supporting a wide range of GNN models without graph pre-processing. GNNBuilder is then introduced as an automated, end-to-end GNN accelerator generation framework, allowing the generation of accelerators for various GNN models with minimal overhead. Next, Edge-MoE presents an FPGA accelerator for multi-task Vision Transformers (ViTs) with architectural innovations, achieving improved energy efficiency compared to GPU and CPU. The talk demonstrates the performance of these approaches, with code and measurements available for public access. Finally, we briefly introduce LightningSim, a fast and rapid simulation tool for High-Level Synthesis (HLS) designs, which can significantly improve HLS design simulation speed.

Biography:

Dr. HAO Cong, Callie is an assistant professor in ECE at Georgia Tech. She received the Ph.D. degree in Electrical Engineering from Waseda University in 2017. Her primary research interests lie in the joint area of efficient hardware design and machine learning algorithms, as well as reconfigurable and high-efficiency computing and agile electronic design automation tools.

Join Zoom Meeting:
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Meeting ID: 963 5105 6844
Passcode: 471978

Enquiries: Ms Anna Wong (annawong@cse.cuhk.edu.hk)

Speaker:
Professor ZHANG Hao, Richard
Amazon Scholar, Professor
School of Computing Science, Simon Fraser University, Canada

Abstract:

Neural implicit representations are the immediate precursors to neural radiance fields (NeRF). In a short span of only four years, they have quickly become the representation of choice for learning reconstructive and generative models of 3D shapes. Unlike traditional convolutional neural networks that have been widely applied to reason about images and video, neural implicit models encode shape boundaries in a continuous manner to lead to superior visual quality; they are also amenable to simple network architectures to facilitate a variety of extensions and adaptations. In this talk, I will recount a brief history of the development of neural implicit representations, while focusing mainly on several paths of follow-ups from our recent works, including structured implicit models, direct mesh generation, CSG assemblies, and the use of contextual, query-specific feature encoding for category-agnostic and generalizable shape representation learning.

Biography:

ZHANG Hao, Richard is a professor in the School of Computing Science at Simon Fraser University, Canada. Currently, he holds a Distinguished University Professorship and is an Amazon Scholar. Richard earned his Ph.D. from the University of Toronto, and MMath and BMath degrees from the University of Waterloo. His research is in computer graphics and visual computing with special interests in geometric modeling, shape analysis, 3D vision, geometric deep learning, as well as computational design and fabrication. He has published more than 180 papers on these topics, including over 60 articles in SIGGRAPH (+Asia) and ACM Transactions on Graphics (TOG), the top venue in computer graphics. Awards won by Richard include a Canadian Human-Computer Communications Society Achievement Award in Computer Graphics (2022), a Google Faculty Award (2019), a National Science Foundation of China Overseas Outstanding Young Researcher Award (2015), an NSERC Discovery Accelerator Supplement Award (2014), a Best Dataset Award from ChinaGraph (2020), as well as faculty grants/gifts from Adobe, Autodesk, Google, and Huawei. He and his students have won the CVPR 2020 Best Student Paper Award and Best Paper Awards at SGP 2008 and CAD/Graphics 2017.

Enquiries: Ms Anna Wong (annawong@cse.cuhk.edu.hk)

Speaker:
Dr. Xiaojie Qiu
Incoming Assistant Professor
Department of Genetics, Department of Computer Science, Stanford University

Abstract:

Single-cell RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions.

In the first part of my talk, I will introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), and highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Cells do not live in a vacuum, but in a milieu defined by cell–cell communication that can be quantified via recent advances in spatial transcriptomics. In my second section of my talk, I will talk about Spateo, a general framework for quantitative spatiotemporal modeling of single-cell resolution spatial transcriptomics. Spateo develops a comprehensive framework of cell-cell interaction to reveal spatial effects of niche factors and cell type-specific ligand-receptor interactions. Furthermore, Spateo reconstructs 3D models of whole embryos, and performs 3D morphometric analyses. Lastly, Spateo introduces the concept of “morphometric vector field” of cell migrations, and integrates spatial differential geometry to unveil regulatory programs underlying various organogenesis patterns of Drosophila. Thus, Spateo enables the study of the ecology of organs at a molecular level in 3D space, beyond isolated single cells.

Biography:

Dr. Xiaojie Qiu is an incoming assistant professor at the Department of Genetics, the BASE program, and the Department of Computer Science at Stanford. Xiaojie’s Ph.D. work at University of Washington with Dr. Cole Trapnell made substantial contributions to the field of single-cell genomics, exemplified by the development of Monocle ⅔ (monocle 2 & monocle 3), which can accurately and robustly reconstruct complex developmental trajectories from scRNA-seq data. In his post-doc at Whitehead Institute with Dr. Jonathan Weissman, Xiaojie developed Dynamo (aristoteleo/dynamo-release) to infers absolute RNA velocity with metabolic labeling enabled single-cell RNA-seq, reconstructs continuous vector fields that predict fates of individual cells, employs differential geometry to extract underlying gene regulatory network regulations, and ultimately predicts optimal reprogramming paths and makes nontrivial in silico perturbation predictions. Recently he also developed a powerful toolkit, Spateo (aristoteleo/spateo-release), for advanced multi-dimensional spatiotemporal modeling of single cell resolution spatial transcriptomics. Spateo delivers novel methods for digitizing spatial layers/columns to identify spatially-polar genes, and develops a comprehensive framework of cell-cell interaction to reveal spatial effects of niche factors and cell type-specific ligand-receptor interactions. Furthermore, Spateo reconstructs 3D models of whole embryos, and performs 3D morphometric analyses. Lastly, Spateo introduces the concept of “morphometric vector field” of cell migrations, and integrates spatial differential geometry to unveil regulatory programs underlying various organogenesis patterns of Drosophila.

The Qiu lab at Stanford will officially start on Dec. 16, 2024. Xiaojie will continue leveraging his unique background in single-cell genomics, mathematical modeling, and machine learning to lead a research team that bridges the gap between the “big data” from single-cell and spatial genomics and quantitative/predictive modeling in order to address fundamental questions in mammalian cell fate transitions, especially that of heart development and disease. There will be mainly four directions in the lab: 1) dissect the mechanisms of mammalian cell differentiation, reprogramming, and maintenance, including that of cardiac cells, through differentiable deep learning frameworks; 2) integrate multi-omics and harmonize short-term RNA velocities with long-term lineage tracing and apply such methods to heart developmental and heart congenital disease; 3) build predictive in silico 3D spatiotemporal models of mammalian organogenesis with a focus on the heart morphogenesis; and 4) establish foundational software ecosystem for predictive and mechanistic modeling of single cell and spatial transcriptomics.

Enquiries: Ms Anna Wong (annawong@cse.cuhk.edu.hk)

Speaker:
Professor Chris G. Willcocks
Associate Professor
Department of Computer Science, Durham University

Abstract:

There are several key equations in the generative modelling literature, most of which estimate the probability of data. Each related modelling approach (Flows, EBMs, VAEs, GANs, OT, Autoregressive,…) have trade-offs in terms of (i) modelling quality, (i) inference time/depth, and (iii) distribution coverage/mode collapse. Building off findings in our TPAMI 2022 review, “Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models”, this talk covers high-level theoretical insights into the different generative modelling paradigms, discussing where there is a gap in the current theory and looking at promising directions such as from optimal transport theory and implicit networks, to address upcoming challenges.

Biography:

Chris G. Willcocks is an associate professor in computer science at Durham University where he leads the deep learning and reinforcement learning modules. His research is in theoretical aspects of deep learning, with a particular emphasis on non-adversarial methodologies such as probabilistic diffusion models and stochastic processes. Research within his group has led to several impactful results in generative modelling including an extension of diffusion models to infinite dimensions without requiring latent vector compression, and an approach that shows you don’t need encoders in traditional autoencoders. He is a Fellow of the Higher Education Academy (FHEA), an area chair for BMVC, and has authored over 30 peer-reviewed publications in venues such as ICLR, CVPR, ECCV and TPAMI.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Meisam Razaviyayn
Associate Professor
University of Southern California

Abstract:

Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against certain demographic groups such as individuals with certain race, gender, or age. Another major concern in these applications is the violation of the privacy of users. While fair learning algorithms have been developed to mitigate discrimination issues, these algorithms can still leak sensitive information, such as individuals’ health or financial records. Utilizing the notion of differential privacy (DP), prior works aimed at developing learning algorithms that are both private and fair. However, existing algorithms for DP fair learning require a full-batch of data in each iteration of the algorithm to be able to impose fairness. Moreover, the fairness/accuracy of the model can degrade significantly in prior DP training algorithms. In this work, we developed a min-batch (stochastic) differentially private algorithm for fair learning (with theoretical convergence guarantee). Here, the term “stochastic” refers to the fact that our proposed algorithm converges even when mini-batches of data are used at each iteration (i.e. stochastic optimization). Our framework is flexible enough to permit different fairness notions, including demographic parity and equalized odds. In addition, our algorithm can be applied to non-binary classification tasks with multiple (non-binary) sensitive attributes. Our numerical experiments show that the proposed algorithm consistently offers significant performance gains over the state-of-the-art baselines, and can be applied to larger-scale problems with non-binary target/sensitive attributes.

Biography:

Meisam Razaviyayn is an associate professor of Industrial and Systems Engineering, Computer Science, Quantitative and Computational Biology, and Electrical Engineering at the University of Southern California. He is also the associate director of the USC-Meta Center for Research and Education in AI and Learning. Prior to joining USC, he was a postdoctoral research fellow in the Department of Electrical Engineering at Stanford University. He received his PhD in Electrical Engineering with a minor in Computer Science at the University of Minnesota. He obtained his M.Sc. degree in Mathematics from the University of Minnesota. Meisam Razaviyayn is the recipient of the 2022 NSF CAREER Award, the 2022 Northrop Grumman Excellence in Teaching Award, the 2021 AFOSR Young Investigator Award, the 2021 3M Nontenured Faculty award, 2020 ICCM Best Paper Award in Mathematics, IEEE Data Science Workshop Best Paper Award in 2019, the Signal Processing Society Young Author Best Paper Award in 2014, and the finalist for Best Paper Prize for Young Researcher in Continuous Optimization in 2013 and 2016. He is also the silver medalist of Iran’s National Mathematics Olympiad. His research interests include the design and the study of the fundamental aspects of optimization algorithms that arise in the modern data science era.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Dr. YANG Lin
Assistant Professor
Electrical and Computer Engineering Department, University of California

Abstract:

The success of reinforcement learning (RL) heavily depends on the approximation of functions such as policy, value, or models. Misspecification—a mismatch between the ground-truth and the best function approximators—often occurs, particularly when the ground-truth is complex. Because the misspecification error does not disappear even with an infinite number of samples, it’s crucial to design algorithms that demonstrate robustness under misspecification. In this talk, we will first present a lower bound illustrating that RL can be inefficient (e.g., possessing exponentially large complexity) if the features can only represent the optimal value functions approximately but with high precision. Subsequently, we will show that this issue can be mitigated by approximating the transition probabilities. In such a setting, we will demonstrate that both policy-based and value-based approaches can be resilient to model misspecifications. Specifically, we will show that these methods can maintain accuracy even under large, locally-bounded misspecification errors. Here, the function class might have a \Omega(1) approximation error in specific states and actions, but it remains small on average under a policy-induced state-distribution. Such robustness to model misspecification partially explains why practical algorithms perform so well, paving the way for new directions in understanding model misspecifications.

Biography:

Dr. Lin Yang is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. His current research focuses on the theory and applications of reinforcement learning. Previously, he served as a postdoctoral researcher at Princeton University. He earned two Ph.D. degrees in Computer Science and in Physics & Astronomy from Johns Hopkins University. Prior to that, he obtained a Bachelor’s degree in Math & Physics from Tsinghua University. Dr. Yang has numerous publications in premier machine learning venues like ICML and NeurIPS, and has served as area chairs for these conferences. His receives an Amazon Faculty Award, a Simons-Berkeley Research Fellowship, the JHU MINDS Best Dissertation Award, and the JHU Dean Robert H. RoyFellowship.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Professor ZHANG Hong
Assistant Professor
Cheriton School of Computer Science, University of Waterloo

Abstract:

The world is undergoing a data revolution. Emerging big data and ML applications are harnessing massive volumes of data to uncover hidden patterns, correlations, and other valuable insights, transforming information and knowledge production. As the data volume keeps growing explosively, these applications require high-performance big data and ML systems to efficiently transfer, store, and process data at a massive scale.

In this talk, I advocate an application-oriented principle to design big data and ML systems: fully exploiting application-specific structures — communication patterns, execution dependencies, ML model structures, etc. — to suit application-specific performance demands. I will present how I have developed the application-oriented principle throughout my PhD-Postdoc-Faculty research, and how I have applied it to build systems tailored for different big data and ML applications.

Biography:

ZHANG Hong  is currently an assistant professor at the Cheriton School of Computer Science at the University of Waterloo. Previously, he was a postdoctoral scholar at UC Berkeley and obtained his Ph.D. degree in Computer Science and Engineering from HKUST. Hong is broadly interested in computer systems and networking, with special focuses on distributed data analytics and ML systems, data center networking, and serverless computing. His research work appeared in prestigious systems and networking conferences, such as SIGCOMM, NSDI, and EuroSys. He has been awarded the Google Ph.D. Fellowship in systems and networking.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Professor Chang Wen Chen
Chair Professor of Visual Computing
The Hong Kong Polytechnic University

Abstract:
Visual computing, traditionally, is a generic term for all computer science disciplines for algorithmic development dealing with images, videos, and other types of visual data. This talk shall focus on contemporary visual computing design from several systematic perspectives. Contemporary visual computing has been substantially advanced to enhance both human understanding and machine intelligence. The ultimate goal for human understanding will be for visual computing algorithms to generate human-like storytelling with a rational contextual setting and the capability to apply general knowledge. For machine intelligence, a more appropriate form of representing semantics from visual data will be to utilize a well-structured scene graph generation approach to characterize the logical relationship among the subjects and objects detected from the visual data. We shall report our recent research activities in developing advanced visual computing algorithms for both human understanding and machine intelligence. These exemplary applications demonstrate several unique visual computing capabilities in understanding the real world with more accurate contextual and environmental interpretations. These examples also illustrate the technical challenges we are facing and the potential impacts that contemporary visual computing systems are making, including the paradigm-shifting visual semantic communication design for the future 6G mobile networks.

Biography:

Chang Wen Chen is currently Chair Professor of Visual Computing at The Hong Kong Polytechnic University. Before his current position, he served as Dean of the School of Science and Engineering at The Chinese University of Hong Kong, Shenzhen from 2017 to 2020, and concurrently as Deputy Director at Peng Cheng Laboratory from 2018 to 2021. Previously, he has been an Empire Innovation Professor at the State University of New York at Buffalo (SUNY) from 2008 to 2021 and the Allan Henry Endowed Chair Professor at the Florida Institute of Technology from 2003 to 2007.

He has served as an Editor-in-Chief for IEEE Trans. Multimedia (2014-2016) and IEEE Trans. Circuits and Systems for Video Technology (2006-2009). He has received many professional achievement awards, including ten (10) Best Paper Awards in premier publication venues, the prestigious Alexander von Humboldt Award in 2010, the SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2016, and UIUC ECE Distinguished Alumni Award in 2019. He is an IEEE Fellow, a SPIE Fellow, and a Member of the Academia Europaea.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Prof. Haohuan Fu
Professor
Department of Earth System Science, Tsinghua University

Abstract:
Defined as the fastest computers in the world by the name, supercomputers have been important tools for making scientific discoveries and technology breakthroughs. In this talk, we will introduce a series of Sunway Supercomputers, which demonstrate a superb example of integrating tens of millions of cores into a high-resolution numerical simulator or a large-scale machine learning engine, and bringing opportunities for widening our knowledge boundaries in various domains. Application examples include ultra-high-resolution climate modeling and earthquake simulation, close-to-real-time quantum circuit simulation, unsupervised learning to achieve nation-scale land cover mapping, and training large deep learning models of brain-scale complexity. Through these examples, we discuss the key issues and potential of combining supercomputing and AI technologies for solving the major challenges that we face.

Biography:
Haohuan Fu is a professor in the Department of Earth System Science, Tsinghua University, and the deputy director of the National Supercomputing Center in Wuxi. Fu has his BE (2003) in CS from Tsinghua University, MPhil (2005) in CS from City University of Hong Kong, and PhD (2009) in computing from Imperial College London. His research work focuses on supercomputing architecture and software, leading to three ACM Gordon Bell Prizes (nonhydrostatic atmospheric dynamic solver in 2016, nonlinear earthquake simulation in 2017, and random quantum circuit simulation in 2021).

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Prof. Jin-Song Dong
Professor
School of Computing, National University of Singapore

Abstract:
Sports analytics encompasses the utilization of data science, artificial intelligence (AI), psychology, and advanced Internet of Things (IoT) devices to enhance sports performance, strategy, and decision-making. This process involves the collection, processing, and interpretation of cloud-based data from a variety of sources, such as video recordings, performance metrics, and scouting reports. The resulting insights aid in evaluating player and team performance, preventing injuries, and supporting coaches and team managers in making well-informed decisions to optimize resources and achieve superior outcomes.

One widely recognized formal method, Probabilistic Model Checking (PMC), has been conventionally employed in reliability analysis for intricate safety critical systems. For instance, the reliability of an aircraft can be determined by evaluating the reliability of its individual components, including the engine, wings, and sensors. Our groundbreaking approach applies PMC to a novel domain: Sports Strategy Analytics. As an example, the reliability (winning percentage) of a sports player can be ascertained from the reliability (success rate) of their specific sub-skill sets (e.g., serve, forehand, backhand, etc., in tennis).

In this presentation, we will discuss our recent research work, which involves the application of PMC, machine learning, and computer vision to the realm of sports strategy analytics. At the end of the presentation, we will also discuss the vision of a new international sports analytics conference series (https://formal-analysis.com/isace/2023/).

Biography:
Jin-Song Dong is a professor at the National University of Singapore. His research spans a range of fields, including formal methods, safety and security systems, probabilistic reasoning, sports analytics, and trusted machine learning. He co-founded the commercialized PAT verification system, which has garnered thousands of registered users from over 150 countries and received the 20-Year ICFEM Most Influential System Award. Jin Song also co-founded the commercialized trusted machine learning system Silas (www.depintel.com). He has received numerous best paper awards, including the ACM SIGSOFT Distinguished Paper Award at ICSE 2020.

He served on the editorial board of ACM Transactions on Software Engineering and Methodology, Formal Aspects of Computing, and Innovations in Systems and Software Engineering, A NASA Journal. He has successfully supervised 28 PhD students, many of whom have become tenured faculty members at leading universities worldwide. He is also a Fellow of the Institute of Engineers Australia. In his leisure time, Jin Song developed Markov Decision Process (MDP) models for tennis strategy analysis using PAT, assisting professional players with pre-match analysis (outperforming the world’s best). He is a Junior Grand Slam coach and takes pleasure in coaching tennis to his three children, all of whom have reached the #1 national junior ranking in Singapore/Australia. Two of his children have earned NCAA Division 1 full scholarships, while his second son, Chen Dong, played #1 singles for Australia in the Junior Davis Cup and participated in both the Australian Open and US Open Junior Grand Slams.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Dr. CHENG Yu
Microsoft Research Redmond, USA

Abstract:

In recent years, we are witnessing a paradigm shift where foundational models, such as GPT-4, ChatGPT, and Codex, are consolidating into fewer, but extremely large models that cover multiple modalities and tasks and significantly surpass the performance of standalone models. However, these extremely large models are still very expensive to adapt to new scenarios/tasks, deploy in the runtime inference in real-world applications, and are vulnerable to crafted adversarial examples. In this talk, I will present the techniques we developed to enable foundation models to smoothly scale to small computational footprints/new tasks, and be robust to handle diverse/adversarial textual inputs. The talk also introduces how to productionize these techniques in several applications such as Github Copliot and New Bing.

Biography:

Dr. CHENG Yu is a Principal Researcher at Microsoft Research and an Adjunct Professor at Rice University/Renmin University of China. Before joining Microsoft, he was a Research Staff Member at IBM Research & MIT-IBM Watson AI Lab. He got a Ph.D. from Northwestern University in 2015 and a bachelor’s degree from Tsinghua University in 2010. His research covers deep learning in general, with specific interests in model compression and efficiency, deep generative models, and adversarial robustness. Yu has led several teams and productized these techniques for Microsoft-OpenAI core products (e.g., Copilot, DALL-E-2, ChatGPT, GPT-4). He serves (or, has served) as an area chair for CVPR, NeurIPS, AAAI, IJCAI, ACMMM, WACV, and ECCV.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Prof. Qirun Zhang
Assistant Professor
School of Computer Science, Georgia Institute of Technology

Abstract:

Program analysis automatically reasons about program runtime behavior and provides mechanisms to determine whether a program’s execution will satisfy certain properties. Program analysis offers a rich spectrum of methods for improving software reliability. A variety of program analysis problems can be formulated as graph reachability problems in edge-labeled graphs. Over the years, we have witnessed the tremendous success of various graph-reachability-based program-analysis techniques. In this talk, I will discuss our work, in the past three years, on CFL-reachability, Dyck-reachability, and InterDyck-reachability.

Biography:

Qirun Zhang is an Assistant Professor in Computer Science at Georgia Tech. His general research interests are in programming languages and software engineering, focusing on developing new static program analysis frameworks to improve software reliability. He has received a PLDI 2020 Distinguished Paper Award, an OOPSLA 2022 Distinguished Artifact award, an NSF CAREER Award, and an Amazon Research Award in Automated Reasoning. He served on the program committees of FSE, ICSE, ISSTA, OOPSLA, PLDI, and POPL.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Prof. CAI Jianfei
Professor
Faculty of IT, Monash University

Abstract:

Neural Radiance Fields (NeRF) has been a new paradigm for 3D representation, providing implicit shape information and view-dependent appearance simultaneously. Based on this new representation, seminal 3D generation approaches have been proposed that aim to generate photorealistic images from a given distribution in a 3D-aware and view-consistent manner, while their performance in 3D geometry reconstruction is limited. On the other hand, several works demonstrate that rendering neural implicit surfaces, where gradients are concentrated around surface regions, is able to produce a high-quality 3D reconstruction. However, they focus only on holistic scene representation yet ignore individual objects inside it, thus limiting potential downstream applications. In this talk, we will first present our recent ECCV’22 work, ObjectSDF, which provides a nice object-compositional neural implicit surfaces framework that can jointly reconstruct the scene and objects inside it with only semantic masks. We will also introduce our another ECCV’22 work that can reconstruct a 3D scene modelled by NeRF, conditioned on one single-view semantic mask as input. Finally, we will provide some future directions on this topic.

Biography:

CAI Jianfei is a Professor at Faculty of IT, Monash University, where he currently serves as the Head for the Data Science & AI Department. He is also a visiting professor at Nanyang Technological University (NTU). Before that, he was Head of Visual and Interactive Computing Division and Head of Computer Communications Division in NTU. His major research interests include computer vision, deep learning and multimedia. He has successfully trained 30+ PhD students with three getting NTU SCSE Outstanding PhD thesis award. Many of his PhD students joined leading IT companies such as Facebook, Apple, Amazon, and Adobe or become faculty members in reputable universities. He is a co-recipient of paper awards in ACCV, ICCM, IEEE ICIP and MMSP. He serves or has served as an Associate Editor for IJCV, IEEE T-IP, T-MM, and T-CSVT as well as serving as Area Chair for CVPR, ICCV, ECCV, IJCAI, ACM Multimedia, ICME and ICIP. He was the Chair of IEEE CAS VSPC-TC during 2016-2018. He had also served as the leading TPC Chair for IEEE ICME 2012 and the best paper award committee chair & co-chair for IEEE T-MM 2020 & 2019. He will be the leading general chair for ACM Multimedia 2024. He is a Fellow of IEEE.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Mr. SHE Dongdong
PhD candidate
Department of Computer Science, Columbia University

Abstract:

Security vulnerabilities significantly impact our daily lives, from ransomware attacks costing billions of dollars every year to confidential data leakage in government, military and industry. Fuzzing is a popular automated technique to catch these vulnerabilities in real-world programs. Despite the wide application in industry, existing fuzzers heavily rely on rule-based designs (i.e., incorporating a set of static rules and heuristics). These fixed rules and heuristics often fail on diverse programs and severely limit fuzzing performance.

In this talk, I will present a novel and pioneering approach to general fuzzing: a data-driven approach. Fuzzing is an iterative process. Data-driven approach extracts useful knowledge from the massive amount of iterations in fuzzing and uses the learned knowledge to perform future fuzzing smartly. Meanwhile, in a data-driven approach, we can formulate fuzzing as a data-centric problem, thus bridging the connection between fuzzing to various domains (e.g., machine learning, optimization and social network), enabling adaptive and effective designs in the general fuzzing framework.

Biography:
SHE Dongdong is a PhD candidate in Computer Science at Columbia University. His research focuses on security and machine learning, particularly applying machine learning and other data-driven approaches to security problems. His work has been published at top-tier security and software engineering conferences (S&P, CCS, Security and FSE). He is the recipient of an ACM CCS Best Paper runner-up award and a finalist in the NYU CSAW applied research competition. Before attending Columbia, he obtained a Master’s in Computer Science from UC, Riverside and Bachelor’s in Electronic and Information Engineering from HUST.

Join Zoom Meeting:
https://cuhk.zoom.us/j/92596540594?pwd=bEJKc0RlN3hXQVFNTWpmcWRmVnRFdz09
Meeting ID: 925 9654 0594
Passcode: 202300

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Prof. Renyuan Zhang
Associate Professor
Nara Institute of Science and Technology

Abstract:

This talk aims at introducing the multi-grained re-configurable computing platforms which are elastic in both of space and time domains. As the preliminary, several approximate computing technologies by Prof. Zhang’s group are introduced for efficiently accelerating the AI tasks. For the next generation of AI platforms, it is expected to explore the disruptive computer architectures for ultra-high speed, low cost, and flexible tensor computations without any benefitting of Moore’s Law. For this purpose, temporal-spatial re-configurable accelerators are demanded: (1) an innovative mechanism for data processing is explored by the snapshot (or accumulative, optionally) observation of spiking (addressing time-elastic); (2) the multi-grained re-configurable architecture is developed on the basis of our novel neural network topology seen as “DiaNet” (addressing space-elastic).

Biography:

Prof. Renyuan Zhang (Senior Member, IEEE) received the M.E. degree from Waseda University, in 2010, and the Ph.D. degree from The University of Tokyo, in 2013. He was an Assistant Professor with the Japan Advanced Institute of Science and Technology, from 2013 to 2017. He has been an Assistant Professor and an Associate Professor with the Nara Institute of Science and Technology, since 2017 and 2021, respectively. His research interests include analog–digital mixed circuits and approximate computing. He is a member of IEICE.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Dr. Xiaoxiao Li,
Assistant Professor
Department of Electrical and Computer Engineering
The University of British Columbia (UBC)

Abstract:

Federated learning (FL) is a trending framework to enable multi-institutional collaboration in machine learning without sharing raw data. This presentation will discuss our ongoing progress in designing FL algorithms that embrace the data heterogeneity properties for distributed data analysis in the FL setting. First, I will present our work on theoretically understanding FL training convergence and generalization using a neural tangent kernel, called FL-NTK. Then, I will present our algorithms for tackling data heterogeneity (on features and labels) and device heterogeneity, motivated by our previous theoretical foundation. Lastly, I will also show the promising results of applying our FL algorithms in real-world applications.

Biography:

Dr. Xiaoxiao Li is an Assistant Professor at the Department of Electrical and Computer Engineering at The University of British Columbia (UBC) starting August 2021. In addition, Dr. Li is an adjunct Assistant Professor at Yale University. Before joining UBC, Dr. Li was a Postdoc Research Fellow at Princeton University. Dr. Li obtained her Ph.D. degree from Yale University in 2020. Dr. Li’s research focuses on developing theoretical and practical solutions for enhancing the trustworthiness of AI systems in healthcare. Specifically, her recent research has been dedicated to advancing federated learning techniques and their applications in the medical field. Dr. Li’s work has been recognized with numerous publications in top-tier machine learning conferences and journals, including NeurIPS, ICML, ICLR, MICCAI, IPMI, ECCV, TMI, TNNLS, Medical Image Analysis, and Nature Methods.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Professor Yuqun Zhang
Assistant Professor
Department of Computer Science and Engineering
Southern University of Science and Technology

Abstract:

Fuzzing (or fuzz testing) refers to inputting invalid, unexpected, or random data to programs for exposing unexpected program behaviors (such as crashes, failing assertions, or memory leaks), which can be further inspected or analyzed to detect potential vulnerabilities/bugs. While recently there is a growing trend to propose new fuzzing techniques, limited attentions have been paid on studying their common/representative strategies, e.g., exploring why and how exactly their strategies work. In this talk, I will discuss a rather common fuzzing strategy, namely Havoc, which randomly mutates seeds via a mutator stacking mechanism and is widely adopted in coverage-guided fuzzers. I will show that essentially, it is Havoc which dominates the fuzzing effectiveness, including increasing coverage and exposing program bugs, rather than the strategies proposed by the coverage-guided fuzzers. Moreover, it can be rather simple to enhance the effectiveness of Havoc.

Biography:

Yuqun Zhang is an Assistant Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology, Shenzhen, China. His research focuses on exploring new general-purpose and domain-specific quality assurance methods for software. His research output on fuzzing and taint analysis has been deployed in Tencent and Alibaba to successfully detect hundreds of bugs/vulnerabilities. He received his PhD from UT Austin. He has been awarded one ACM SIGSOFT Distinguished Paper Award as well as one nominee.

Enquiries: Mr Jeff Liu (jeffliu@cse.cuhk.edu.hk)

6 lab managers from Huawei Cloud will hold a presentation and communication session in the Room 121, HSH Engineering Building at the Chinese University of Hong Kong on March 30th, from 10 – 11 am. They will introduce the following six innovative Labs from Huawei Cloud:

1. Algorithm Innovation Lab: Application of mathematical modeling and optimization algorithms in Huawei Cloud, presented by Dr. Wenli Zhou.
2. Cloud Storage Innovation Lab: Introduction to building a high-performance, highly reliable, secure, and intelligent cloud-native storage platform (research areas include key technologies and core algorithms in block storage, object storage, file storage, memory storage, etc., including distributed data consistency, space management, metadata indexing, intelligent caching, etc.), presented by Dr. Xusheng Chen.
3. Data Intelligence Innovation Lab: Provide right data to the right person at the right time, presented by Dr. Ke Xu.
4. Availability Engineering Lab: Introduction to the related technologies of public cloud and large-scale distributed application architecture reliability and availability engineering, technology, and innovation capabilities center, presented by Ling Wei.
5. Computing and Networking Innovation Lab: In the computing domain, focus on tapping the efficiency of large-scale computing resource reuse in Huawei Cloud + continuous research on next-generation autonomous cloud network systems in the networking domain, presented by Dr. Zengyin Yang.
6. Cloud Database Innovation Lab: Innovating Cloud-Native Databases for Next-Gen Applications, presented by Dr. Hao Zhang.

Lab Introduction:

The Computing and Networking Innovation Lab focuses on the research and development of new computing and networking in Huawei Cloud. Positioned as a technical pre-research team for Huawei Cloud, it mainly studies two major areas of cloud computing:
-In the computing domain, focus on tapping the efficiency of large-scale computing resource reuse in Huawei Cloud, including cloud service application load profiling, VM/container scheduling algorithms and systems, real-time QoS detection and control systems, and new research directions in virtualization -In the networking domain, based on the requirements and data of cloud computing itself, continuously research the next-generation autonomous cloud network system, including the next-generation gateway platform, P4/NP programmable device platform, network brain combined with AI, large-scale high-performance SDN platform, real-time network measurement and verification, and other new cloud computing network directions.

The Cloud Storage Innovation Lab is Huawei Cloud’s storage innovation research center. The research areas involve key technologies and core algorithms in block storage, object storage, file storage, memory storage, etc., including distributed data consistency, space management, metadata indexing, intelligent caching, etc. It is committed to building a high-performance, highly reliable, secure, and intelligent cloud-native storage platform, providing the best experience and cost-effective storage services for enterprises moving to the cloud.

Enquiries: Professor Michael LYU (lyu@cse.cuhk.edu.hk) / Jeff Liu (jeffliu@cse.cuhk.edu.hk)

Speaker:
Professor Yibo Lin
Assistant Professor
School of Integrated Circuits
Peking University

Abstract:

Physical design is a critical step in the design flow of modern VLSI circuits. With continuous increase of design complexity, physical design becomes extremely challenging and time-consuming due to the repeated design iterations for the optimization of performance, power, and area. With recent boom of artificial intelligence, deep learning has shown its potential in various fields, like computer vision, recommendation systems, robotics, etc. Incorporating deep learning into the VLSI design flow has also become a promising trend. In this talk, we will introduce our recent studies on developing dedicated deep learning techniques for cross-stage modeling and optimization in physical design. We will also discuss the impact of large-scale and diverse datasets (e.g., CircuitNet) on improving the performance of deep learning models.

Biography:

Yibo Lin is an assistant professor in the School of Integrated Circuits at Peking University. He received the B.S. degree in microelectronics from Shanghai Jiaotong University in 2013, and his Ph.D. degree from the Electrical and Computer Engineering Department of the University of Texas at Austin in 2018. His research interests include physical design, machine learning applications, and GPU/FPGA acceleration. He has received 6 Best Paper Awards at premier venues including DATE 2022, TCAD 2021, and DAC 2019. He has also served in the Technical Program Committees of many major conferences, including ICCAD, ICCD, ISPD, and DAC.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Mr. DING Kaize

Abstract:

The world around us — and our understanding of it — is rich in relational structure: from atoms and their interactions to objects and entities in our environments. Graphs, with nodes representing entities and edges representing relationships between entities, serve as a common language to model complex, relational, and heterogeneous systems. Despite the success of recent deep graph learning, the efficacy of existing efforts heavily depends on the ideal data quality of the observed graphs and the sufficiency of the supervision signals provided by the human-annotated labels, leading to the fact that those carefully designed models easily fail in resource-constrained scenarios.

In this talk, I will present my recent research contributions centered around data-efficient learning for relational and heterogeneous graph-structured data. First, I will introduce what data-efficient graph learning is and my contributions to different research problems under its umbrella, including graph few-shot learning, graph weakly-supervised learning, and graph self-supervised learning. Based on my work, I will elucidate how to push forward the performance boundary of graph learning models especially graph neural networks with minimal human supervision signals. I will also touch upon the applications of data-efficient graph learning to different domains and finally conclude my talk with a brief overview of my future research agenda.

Biography:

DING Kaize is currently a Ph.D. candidate from the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU). Kaize is working at the Data Mining and Machine Learning (DMML) Lab with Prof. Huan Liu and previously he was previously interned at Google Brain, Microsoft Research, and Amazon Alexa AI. Kaize is broadly interested in the areas of data mining, machine learning, and natural language processing and their interdisciplinary applications in different domains including cybersecurity, social good, and healthcare. His recent research interests particularly focus on data-efficient learning and graph neural networks. He has published a series of papers in top conferences and journals such as AAAI, EMNLP, IJCAI, KDD, NeurIPS, and TheWebConf. Kaize was the recipient of the ASU Graduate College Completion Fellowship and ASU GPSA Outstanding Research Award, etc. More information about him can be found at https://www.public.asu.edu/~kding9/ .

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Speaker:
Professor Jeff Kramer
Emeritus Professor, Department of Computing,
Imperial College London

Abstract:

Change in complex systems is inevitable. Providing rigorous techniques and tools to support dynamic system adaptation so that it can be performed online, at runtime, is certainly challenging. However the potential resilience rewards could be great. There is the need for a software architecture and runtime support for dynamic software configuration, plan execution and plan synthesis, domain environment modelling and monitoring, and ultimately even potentially performing some elements of requirements engineering at runtime! This talk will present our motivation and vision, describing our work to date and our hopes for the future.

Biography:

Jeff Kramer is Emeritus Professor of Computing at Imperial College London.

His research work is primarily concerned with software engineering, with particular emphasis on evolving software architectures, behaviour analysis, the use of models in requirements elaboration and self organising adaptive software systems. An early research result was the DARWIN language for evolving distributed architectures, and more recently was the Three Layer Model for self-adaptive systems. One of the major research challenges in self-managed adaptation is the need to perform requirements analysis at runtime.

Jeff has been involved in many major conferences and journals, notably as program co-chair of ICSE in Los Angeles in 1999, general co-chair of ICSE 2010 in Cape Town, and Editor in Chief of IEEE TSE from 2006 to 2010. His awards include the 2005 ACM SIGSOFT Outstanding Research Award and the 2011 ACM SIGSOFT Distinguished Service. He is a Fellow of the Royal Academy of Engineering, Fellow of the ACM, and a Member of Academia Europaea.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Mr. NIE Pengyu

Abstract:

Machine Learning (ML) techniques have been increasing adopted for Software Engineering (SE) tasks, such as code completion and code summarization. However, existing ML models provide limited value for SE tasks, because these models do not take into account the key characteristics of software: software is executable and software constantly evolves. In this talk, I will present my insights and work on developing execution-guided and evolution-aware ML models for several SE tasks targeting important domains, including software testing, verification, and maintenance.

First, I will present my techniques to help developers write tests and formal proofs. My work has direct impact on software correctness and everyone that depends on software. I will present TeCo: the first ML model for test completion/generation, and Roosterize: the first model for lemma name generation. In order to achieve good performance, these two tasks require reasoning about code execution, which existing ML models are not capable of. To tackle this problem, I designed and develop ML models that integrate execution data and use such data to validate generation results.

Next, I will present my techniques to help developers maintain software. Specifically, I will present my work on comment updating, i.e., automatically updating comments when associated code changes. I proposed the first edit ML model for SE to solve this task, which learns to perform developer-like edits instead of generating comments from scratch. This model can be generalized for general-purpose software editing, including tasks such as bug fixing and automated code review.

All my code and data are open-sourced, evaluated on real-world software, and shown to outperform existing ML models by large margins. My contributions lay the foundation for the development of accurate, robust, and interpretable ML models for SE.

Biography:
NIE Pengyu is a Ph.D. candidate at the University of Texas at Austin, advised by Milos Gligoric. Pengyu obtained his Bachelor’s Degree at the University of Science and Technology of China. His research area is the fusion of Software Engineering (SE) and Natural Language Processing (NLP), with a focus on improving developers’ productivity during software development, testing, and maintenance. He has published 14 papers in top-tier SE, NLP, and PL conferences. He is the recipient of an ACM SIGSOFT Distinguished Paper Award (FSE 2019), and the UT Austin Graduate School Continuing Fellowship. More information can be found on his webpage: https://pengyunie.github.io.

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Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Prof. ZHAO Xiangyu
Assistant Professor, School of Data Science
City University of Hong Kong (CityU)

Abstract:

Deep recommender systems have become increasingly popular in recent years, and have been utilized in a variety of domains, including movies, music, books, search queries, and social networks. They assist users in their information-seeking tasks by suggesting items (products, services, or information) that best fit their needs and preferences. Most existing recommender systems are based on static recommendation policies and hand-crafted architectures. Specifically, (i) most recommender systems consider the recommendation procedure as a static process, which may fail given the dynamic nature of the users’ preferences; (ii) existing recommendation policies aim to maximize the immediate reward from users, while completely overlooking their long-term impacts on user experience; (iii) designing architectures manually requires ample expert knowledge, non-trivial time and engineering efforts, while sometimes human error and bias can lead to suboptimal architectures. I will introduce my efforts in tackling these challenges via reinforcement learning (RL) and automated machine learning (AutoML), which can (i) adaptively update the recommendation policies, (ii) optimize the long-term user experience, and (iii) automatically design the deep architectures for recommender systems.

Biography:

Prof. Xiangyu ZHAO is an assistant professor of the school of data science at City University of Hong Kong (CityU). His current research interests include data mining and machine learning, and their applications in Recommender System, Smart City, Healthcare, Carbon Neutral and Finance. He has published more than 60 papers in top conferences (e.g., KDD, WWW, AAAI, SIGIR, IJCAI, ICDE, CIKM, ICDM, WSDM, RecSys, ICLR) and journals (e.g., TOIS, SIGKDD, SIGWeb, EPL, APS). His research has been awarded ICDM’22 and ICDM’21 Best-ranked Papers, Global Top 100 Chinese New Stars in AI, CCF-Ant Research Fund, CCF-Tencent Open Fund, Criteo Faculty Research Award, Bytedance Research Collaboration Award, and nomination for Joint AAAI/ACM SIGAI Doctoral Dissertation Award. He serves as top data science conference (senior) program committee members and session chairs, and journal guest editors and reviewers. He serves as the organizers of DRL4KDD@KDD’19/WWW’21 and DRL4IR@SIGIR’20/21/22 and a lead tutor at WWW’21/22/23, IJCAI’21 and WSDM’23. He also serves as the founding academic committee members of MLNLP, the largest Chinese AI community with millions of members/followers. The models and algorithms from his research have been launched in the online system of many companies. Please find more information at https://zhaoxyai.github.io/.

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Meeting ID: 963 8219 9967

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Mr. ZHANG Hanrui

Abstract:

Machine learning algorithms now play a major part in all kinds of decision-making scenarios. When the stakes are high, self-interested agents — about whom decisions are being made — are increasingly tempted to manipulate the machine learning algorithm, in order to better fulfill their own goals, which are generally different from the decision maker’s. This highlights the importance of making machine learning algorithms robust against manipulation. In this talk, I will focus on generalization (i.e., the bridge between training and testing) in strategic classification: Traditional wisdom suggests that a classifier trained on historical observations (i.e., the training set) usually also works well on future data points to be classified (i.e., the test set). I will show how this very general principle fails when agents being classified strategically respond to the classifier, and present an intuitive fix that leads to provable (and in fact, optimal) generalization guarantees under strategic manipulation. I will then discuss the role of incentive-compatibility in strategic classification, and present experimental results that illustrate how the theoretical results can guide practice. If time permits, I will also discuss distinguishing strategic agents with samples, and/or dynamic decision making with strategic agents.

Biography:
ZHANG Hanrui is a PhD student at Carnegie Mellon University, advised by Vincent Conitzer. He was named a finalist for the 2021 Facebook Fellowship. His work won the Best Student Paper Award at the European Symposia on Algorithms (ESA), and a Honorable Mention for Best Paper Award at the AAAI Conference on Human Computation and Crowdsourcing (HCOMP). He received his bachelor’s degree in Yao’s Class, Tsinghua University, where he won the Outstanding Undergraduate Thesis Award.

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Speaker:
Mr. ZHOU Dongruo

Abstract:

Reinforcement learning (RL) has achieved great empirical success in many real-world problems in the last few years. However, many RL algorithms are inefficient due to their data-hungry nature. Whether there exists a universal way to improve the efficiency of existing RL algorithms remains an open question.

In this talk, I will give a selective overview of my research, which suggests that efficient (and optimal) RL can be built through the lens of uncertainties. I will show that uncertainties can not only guide RL to make decisions efficiently, but also have the ability to accelerate the learning of the optimal policy over a finite number of data samples collected from the unknown environment. By utilizing the proposed uncertainty-based framework, I design computationally efficient and statistically optimal RL algorithms under various settings, which improve existing baseline algorithms from both theoretical and empirical aspects. At the end of this talk, I will briefly discuss several additional works, and my future research plan for designing next-generation decision making algorithms.

Biography:
ZHOU Dongruo is a final-year PhD student in the Department of Computer Science at UCLA, advised by Prof. Quanquan Gu. His research is broadly on the foundation of machine learning, with a particular focus on reinforcement learning and stochastic optimization. He aims to provide a theoretical understanding of machine learning methods, as well as to develop new machine learning algorithms with better performance. He is a recipient of the UCLA dissertation year fellowship.

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Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Mr. Shi QIU

Abstract:

As a fundamental 3D data representation, point clouds can be easily collected using 3D scanners, retaining abundant information for AI-driven applications such as autonomous driving, virtual/augmented reality, and robotics. Given the prominence of deep neural networks in current days, deep learning-based point cloud data understanding is playing an essential role in 3D computer vision research.

In this seminar, we focus on learning deep feature representations of point clouds for 3D data processing and analysis. Basically, we start from investigating low-level vision problems of 3D point clouds, which helps to comprehend and deal with the inherent sparsity, irregularity and unorderedness of this 3D data type. On this front, we introduce a novel transformer-based model that fully utilizes the dependencies between scattered points for high-fidelity point cloud upsampling. Moreover, we deeply explore high-level vision problems of point cloud analysis, including the classification, segmentation and detection tasks. Specifically, we propose to (i) learn more geometric information for accurate point cloud classification, (ii) exploit dense-resolution features to recognize small-scale point clouds, (iii) augment local context for large-scale point cloud analysis, and (iv) refine the basic point feature representations for benefiting various point cloud recognition problems and different baseline models. By conducting comprehensive experiments, ablation studies and visualizations, we quantitatively and qualitatively demonstrate our contributions in the deep learning-based research of 3D point clouds.

In general, this seminar presents a review of deep learning-based 3D point cloud research, introduces our contributions in learning deep feature representations of point cloud data, and proposes research directions for future work. We expect this seminar to inspire further exploration into 3D computer vision and its applications.

Biography:
Shi Qiu is a PhD candidate at Australian National University and a postgraduate researcher at Data61-CSIRO. Previously, he obtained his bachelor degree of Electronic Engineering from Dalian University of Technology in 2015, and master degrees of Digital Media Technology from KTH and UCL in 2017. His main research interests are in 3D computer vision and virtual/augmented reality, where he has authored a few research papers in top venues including T-PAMI, CVPR, etc. In addition to academic research, he also interned at industry-based labs including Vivo AI Lab and Tencent’s XR Vision Labs. He is a recipient of scholarships funded by China, EU, and Australia.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Prof. Kazushi Ikeda
Nara Institute of Science and Technology (NAIST)

Abstract:

In this talk, I introduce some studies in Mathematical Informatics Lab, NAIST. Mathematical models are a strong tool in science to describe the nature. However, they are also useful in engineering or even in computation. One example of the math models is the deep learning theory. In deep learning, so many techniques, such as drop-out and skip connections, have been proposed but their effectiveness is not clear. We analyzed it by considering their geometrical meaning.  I show other examples in science and engineering in our projects.

Biography:
Kazushi Ikeda got his B.E., M.E., and Ph.D. in Mathematical Engineering from University of Tokyo in 1989, 1991, and 1994. He joined Kanazawa University as an assistant professor in 1994 and became a junior/senior associate professor of Kyoto University in 1998 and 2003, respectively. He has been a full professor of NAIST since 2008.
He was a research associate of CUHK for two months in 1995.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Prof. MENG Wei
Assistant Professor
Department of Computer Science and Engineering
The Chinese University of Hong Kong

Abstract:

The Internet has powered a large variety of important services and applications, such as search, social networking, banking and shopping. Because of their increasing importance, the applications and their huge number of users on the Internet have become the primary targets of cyber attacks and abuses. However, the dynamic nature of the complex modern software makes it very difficult to reason about the security of those applications, especially by using static program analysis techniques.

In this talk, I will share my experience in understanding and improving application security with dynamic program analysis approaches. I will illustrate it with two representative works that address two emerging threats. First, I will introduce how we investigated click interception on the web with a dynamic JavaScript monitoring system. Second, I will present how we combined static analysis and dynamic analysis to accurately detect algorithmic complexity vulnerabilities. Finally, I will discuss about the other challenges and opportunities for further securing software applications.

Biography:
Wei Meng is an Assistant Professor in the Department of Computer Science and Engineering, The Chinese University of Hong Kong.   His main research interests are in computer security and privacy. He designs and builds systems to protect end users and applications on the Internet. His research has been published primarily at top conferences such as IEEE S&P, USENIX Security, CCS, WWW, and ESEC/FSE. He received his Ph.D. degree in Computer Science from the Georgia Institute of Technology in 2017 and his Bachelor’s degree in Computer Science and Technology from Tsinghua University in 2012. He currently leads the CUHK Computer Security Lab in the CSE department.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Prof. YANG Ming-Chang
Assistant Professor
Department of Computer Science and Engineering
The Chinese University of Hong Kong

Abstract:
In light of technological advancement over the last few decades, there have been many revolutionary developments in memory and storage technologies. Nevertheless, though these emerging technologies offer us new design choices and trade-offs, deploying them in modern memory and storage systems is non-trivial and challenging. In this talk, I will first summarize our efforts in embracing the cutting-edge technologies in memory and storage systems through co-designing the hardware and software. To make a case, I will present two of our recent studies: one in delivering a scalable, efficient and predictable hashing on the persistent memory (PM) technology and the other in constructing a cost-effective yet high-throughput persistent key-value store on the latest hard disk technology called interlaced magnetic recording (IMR). Finally, I will highlight some new promising memory/storage technologies that may pave new paths for and even completely revolutionize the upcoming computer systems.

Biography:
Ming-Chang Yang is currently an Assistant Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He received his B.S. degree from the Department of Computer Science at National Chiao-Tung University, Taiwan, in 2010. He received his Master and Ph.D. degrees from the Department of Computer Science and Information Engineering at National Taiwan University, Taiwan, in 2012 and 2016, respectively. He now serves as an Associate Editor in ACM Transactions on Cyber-Physical Systems (TCPS). Also, he served as a TPC co-chair for NVMSA 2021 and as a TPC member for several major conferences. In addition, he received 2 best paper awards from the prestigious conferences in his field (including ACM/IEEE ISLPED 2020 and IEEE NVMSA 2019). His primary research interests include the emerging non-volatile memory and storage technologies, memory and storage systems, and the next-generation memory/storage architecture designs. For details, please refer to his personal homepage: http://www.cse.cuhk.edu.hk/~mcyang/

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Prof. WONG Chi-Wing Raymond
Professor
Department of Computer Science and Engineering
The Hong Kong University of Science and Technology

Abstract:
When faced with a database containing millions of tuples, an end user might be only interested in finding his/her favorite tuple in the database. In this talk, we study how to help an end user to find such a favorite tuple with a few user interations. In each interaction, a user is presented with a small number of tuples (which can be artificial tuples outside the database or true tuples inside the database) and s/he is asked to indicate the tuple s/he favors the most among them.

Different from the previous work which displays artificial tuples to users during the interaction and requires heavy user interactions, we achieve a stronger result. Specifically, we use a concept, called the utility hyperplane, to model the user preference and an effective pruning strategy to locate the favorite tuple for a user in the whole database. Based on these techniques, we developed an interactive system, called FindYourFavorite, and demonstrate that the system could identify the favorite tuple for a user with a few user interactions by always displaying true tuples in the database.

Biography:
Raymond Chi-Wing Wong is a Professor in Computer Science and Engineering (CSE) of The Hong Kong University of Science and Technology (HKUST). He is currently the associate head of Department of Computer Science and Engineering (CSE). He was the associate director of the Data Science & Technology (DSCT) program (from 2019 to 2021), the director of the Risk Management and Business Intelligence (RMBI) program (from 2017 to 2019), the director of the Computer Engineering (CPEG) program (from 2014 to 2016) and the associate director of the Computer Engineering (CPEG) program (from 2012 to 2014). He received the BSc, MPhil and PhD degrees in Computer Science and Engineering in the Chinese University of Hong Kong (CUHK) in 2002, 2004 and 2008, respectively. In 2004-2005, he worked as a research and development assistant under an R&D project funded by ITF and a local industrial company called Lifewood.

He received 38 awards. He published 104 conference papers (e.g., SIGMOD, SIGKDD, VLDB, ICDE and ICDM), 38 journal/chapter papers (e.g., TODS, DAMI, TKDE, VLDB journal and TKDD) and 1 book. He reviewed papers from conferences and journals related to data mining and database, including VLDB conference, SIGMOD, TODS, VLDB Journal, TKDE, TKDD, ICDE, SIGKDD, ICDM, DAMI, DaWaK, PAKDD, EDBT and IJDWM. He is a program committee member of conferences, including SIGMOD, VLDB, ICDE, KDD, ICDM and SDM, and a referee of journals, including TODS, VLDBJ, TKDE, TKDD, DAMI and KAIS.

His research interests include database, data mining and artificial intelligence.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Prof. Hervé Lombaert
Associate Professor
ETS Montreal, Canada

Abstract:
How to analyze the shapes of complex organs, such as the highly folded surface of the brain? This talk will show how spectral shape analysis can benefit general learning problems where data fundamentally lives on surfaces. We exploit spectral coordinates derived from the Laplacian eigenfunctions of shapes. Spectral coordinates have the advantage over Euclidean coordinates, to be geometry aware, invariant to isometric deformations, and to parameterize surfaces explicitly. This change of paradigm, from Euclidean to spectral representations, enables a classifier to be applied *directly* on surface data, via spectral coordinates. Brain matching and learning of surface data will be shown as examples. The talk will focus, first, on the spectral representations of shapes, with an example on brain surface matching; second, on the basics of geometric deep learning; and finally, on the learning of surface data, with an example on automatic brain surface parcellation.

Biography:
Hervé Lombaert (和偉 隆巴特/和伟 隆巴特) is an Associate Professor at ETS Montreal, Canada, where he holds a Canada Research Chair in Shape Analysis in Medical Imaging. His research focuses on the statistics and analysis of shapes in the context of machine learning and medical imaging. His work on graph analysis has impacted the performance of several applications in medical imaging, from the early image segmentation techniques with graph cuts, to recent surface analysis with spectral graph theory and graph convolutional networks. Hervé has authored over 70 papers, 5 patents, and earned several awards, such as the IPMI Erbsmann Prize. He had the chance to work in multiple centers, including Inria Sophia-Antipolis (France), Microsoft Research (Cambridge, UK), Siemens Corporate Research (Princeton, NJ), McGill University (Canada), and the University of Montreal (Canada).

More at: https://profs.etsmtl.ca/hlombaert

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Prof. QIN Jing
Centre for Smart Health
School of Nursing
The Hong Kong Polytechnic University

Abstract:
Deep learning has achieved remarkable success in various medical image analysis tasks. No matter the past, present, or the foreseeable future, one of the main obstacles that prohibits deep learning models from being successfully developed and deployed in clinical settings is the scarcity of training data. In this talk, we shall review, as well as rethink, our long experience in investigating how to enhance representation capability of deep learning models to achieve satisfactory performance under limited training data. Based on our experience, we attempt to identify and sort out the evolution trajectory of applying deep leaning to medical image analysis, somehow reflecting the development path of deep learning itself beyond the context of our specific applications. The models we developed, at least in our experience, are both effects and causes: effects of the clinical challenges we faced and the technical frontiers at that time; causes, if they are really useful and inspiring, of following more advanced models that are capable of addressing their limitations. To the end, by rethinking such an evolution, we can identify some future directions that deserve to be further studied.

Biography:
QIN, Jing (Harry) is currently an associate professor in Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University. His research focuses on creatively leveraging advanced virtual/augmented reality (VR/AR) and artificial intelligence (AI) techniques in healthcare and medicine applications and his achievements in relevant areas has been well recognized by the academic community. He won the Hong Kong Medical and Health Device Industries Association Student Research Award for his PhD study on VR-based simulation systems for surgical training and planning. He won 5 best paper awards for his research on AI-driven medical image analysis and computer-assisted surgery. He served as a local organization chair for MICCAI 2019, program committee members for AAAI, IJCAI, MICCAI, etc., speakers for many conferences, seminars, and forums, and referees for many prestigious journals in relevant fields.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Dr. Xi Zheng
Director of Intelligent Systems Research Group
Macquarie University, Australia

Abstract:
Autonomous driving has shown great potential to reform modern transportation. Yet its reliability and safety have drawn a lot of attention and concerns. Compared with traditional software systems, autonomous driving systems (ADSs) often use deep neural networks in tandem with logic-based modules. This new paradigm poses unique challenges for software testing. Despite the recent development of new ADS testing techniques, it is not clear to what extent those techniques have addressed the needs of ADS practitioners. To fill this gap, we have published a few works and I will present some of them. The first work is to reduce and prioritize test for multi-module autonomous driving systems (Accepted in FSE’22). The second work is to conduct comprehensive study to identify the current practices, needs and gaps in testing autonomous driving systems (Accepted also in FSE’22). The third work is to analyse the robustness issues in the deep learning driving models (Accepted in PerCom’20). The fourth work is to generate test cases from traffic rules for autonomous driving models (Accepted in TSE’22). I will also cover some ongoing and future work in autonomous driving systems.

Biography:
Dr. Xi Zheng received the Ph.D. in Software Engineering from the University of Texas at Austin in 2015. From 2005 to 2012, he was the Chief Solution Architect for Menulog Australia. He is currently the Director of Intelligent Systems Research Group, Director of International engagement in the School of Computing, Senior Lecturer (aka Associate Professor US) and Deputy Program Leader in Software Engineering, Macquarie University, Australia. His research interests include Internet of Things, Intelligent Software Engineering, Machine Learning Security, Human-in-the-loop AI, and Edge Intelligence. He has secured more than $1.2 million competitive funding in Australian Research Council (Linkage and Discovery) and Data61 (CRP) projects on safety analysis, model testing and verification, and trustworthy AI on autonomous vehicles. He also won a few awards including Deakin Industry Researcher (2016) and MQ Earlier Career Researcher (Runner-up 2020). He has a number of highly cited papers and best conference papers. He serves as PC members for CORE A* conferences including FSE (2022) and PerCom (2017-2023). He also serves as the PC chairs of IEEE CPSCom-2021, IEEE Broadnets-2022 and associate editor for Distributed Ledger Technologies.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Dr. C. Mohan
Distinguished Visiting Professor, Tsinghua University

Abstract:
In this talk, I will first introduce traditional (non-cloud) parallel and distributed database systems. Concepts like SQL and NoSQL systems, data replication, distributed and parallel query processing, and data recovery after different types of failures will be covered. Then, I will discuss how the emergence of the (public) cloud has introduced new requirements on parallel and distributed database systems, and how such requirements have necessitated fundamental changes to the architectures of such systems. I will illustrate the related developments by discussing some of the details of systems like Alibaba POLARDB, Microsoft Azure SQL DB, Microsoft Socrates, Azure Synapse POLARIS, Google Spanner, Google F1, CockroachDB, Amazon Aurora, Snowflake and Google AlloyDB.

Biography:
Dr. C. Mohan is currently a Distinguished Visiting Professor at Tsinghua University in China, a Visiting Researcher at Google, a Member of the inaugural Board of Governors of Digital University Kerala, and an Advisor of the Kerala Blockchain Academy (KBA) and the Tamil Nadu e-Governance Agency (TNeGA) in India. He retired in June 2020 from being an IBM Fellow at the IBM Almaden Research Center in Silicon Valley. He joined IBM Research (San Jose, California) in 1981 where he worked until May 2006 on several topics in the areas of database, workflow, and transaction management. From June 2006, he worked as the IBM India Chief Scientist, based in Bangalore, with responsibilities that relate to serving as the executive technical leader of IBM India within and outside IBM. In February 2009, at the end of his India assignment, Mohan resumed his research activities at IBM Almaden. Mohan is the primary inventor of the well-known ARIES family of database recovery and concurrency control methods, and the industry-standard Presumed Abort commit protocol. He was named an IBM Fellow, IBM’s highest technical position, in 1997 for being recognized worldwide as a leading innovator in transaction management. In 2009, he was elected to the United States National Academy of Engineering (NAE) and the Indian National Academy of Engineering (INAE). He received the 1996 ACM SIGMOD Edgar F. Codd Innovations Award in recognition of his innovative contributions to the development and use of database systems. In 2002, he was named an ACM Fellow and an IEEE Fellow. At the 1999 International Conference on Very Large Data Bases (VLDB), he was honored with the 10 Year Best Paper Award for the widespread commercial, academic and research impact of his ARIES work, which has been extensively covered in textbooks and university courses. From IBM, Mohan received 2 Corporate and 8 Outstanding Innovation/Technical Achievement Awards. He is an inventor on 50 patents. He was named an IBM Master Inventor in 1997. Mohan worked very closely with numerous IBM product and research groups, and his research results are implemented in numerous IBM and non-IBM prototypes and products like DB2, MQSeries, WebSphere, Informix, Cloudscape, Lotus Notes, Microsoft SQLServer, Sybase and System Z Parallel Sysplex. During the last many years, he focused on Blockchain, AI, Big Data and Cloud technologies (https://bit.ly/sigBcP, https://bit.ly/CMoTalks, https://bit.ly/CMgMDS). Since 2017, he has been an evangelist of permissioned blockchains and the myth buster of permissionless blockchains. During 1H2021, Mohan was the Shaw Visiting Professor at the National University of Singapore (NUS) where he taught a seminar course on distributed data and computing. In 2019, he became an Honorary Advisor to TNeGA of Chennai for its blockchain and other projects. In 2020, he joined the Advisory Board of KBA of India.
Since 2016, he has been a Distinguished Visiting Professor of China’s prestigious Tsinghua University in Beijing. In 2021, he was inducted as a member of the inaugural Board of Governors of the new Indian university Digital University Kerala (DUK). Mohan launched his consulting career by becoming a Consultant to Microsoft’s Data Team in October 2020. In March 2022, he became a consultant at Google with the title of Visiting Researcher. He has been on the advisory board of IEEE Spectrum and has been an editor of VLDB Journal, and Distributed and Parallel Databases. In the past, he has been a member of the IBM Academy of Technology’s Leadership Team, IBM’s Research Management Council, IBM’s Technical Leadership Team, IBM India’s Senior Leadership Team, the Bharti Technical Advisory Council, the Academic Senate of the International Institute of Information Technology in Bangalore, and the Steering Council of IBM’s Software Group Architecture Board. Mohan received his PhD in computer science from the University of Texas at Austin in 1981. In 2003, he was named a Distinguished Alumnus of IIT Madras from which he received a B.Tech. in chemical engineering in 1977. Mohan is a frequent speaker in North America, Europe and Asia. He has given talks in 43 countries. He is highly active on social media and has a huge following. More information can be found in the Wikipedia page at https://bit.ly/CMwIkP and his homepage at https://bit.ly/CMoDUK.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Prof. Anupam Chattopadhyay
Associate Professor, NTU

Abstract:
The continued scaling of horizontal and vertical physical features of silicon-based complementary metal-oxide-semiconductor (CMOS) transistors, termed as “More Moore”, has a limited runway and would eventually be replaced with “Beyond CMOS” technologies. There has been a tremendous effort to follow Moore’s law but it is currently approaching atomistic and quantum mechanical physics boundaries. This has led to active research in other non-CMOS technologies such as memristive devices, carbon nanotube field-effect transistors, quantum computing, etc. Several of these technologies have been realized on practical devices with promising gains in yield, integration density, runtime performance, and energy efficiency. Their eventual adoption is largely reliant on the continued research of Electronic Design Automation (EDA) tools catering to these specific technologies. Indeed, some of these technologies present new challenges to the EDA research community, which are being addressed through a series of innovative tools and techniques. In this tutorial, we will particularly cover the two phases of EDA flow, logic synthesis, and technology mapping, for two types of emerging technologies, namely, in-memory computing and quantum computing.

Biography:
Anupam Chattopadhyay received his B.E. degree from Jadavpur University, India, MSc. from ALaRI, Switzerland, and Ph.D. from RWTH Aachen in 2000, 2002, and 2008 respectively. From 2008 to 2009, he worked as a Member of Consulting Staff in CoWare R&D, Noida, India. From 2010 to 2014, he led the MPSoC Architectures Research Group in RWTH Aachen, Germany as a Junior Professor. Since September 2014, Anupam was appointed as an Assistant Professor in SCSE, NTU, where he got promoted to Associate Professor with Tenure from August 2019. Anupam is an Associate Editor of IEEE Embedded Systems Letters and series editor of Springer Book Series on Computer Architecture and Design Methodologies. Anupam received Borcher’s plaque from RWTH Aachen, Germany for outstanding doctoral dissertation in 2008, nomination for the best IP award in the ACM/IEEE DATE Conference 2016 and nomination for the best paper award in the International Conference on VLSI Design 2018 and 2020. He is a fellow of Intercontinental Academia and a senior member of IEEE and ACM.

Enquiries: Mr Jeff Liu at Tel. 3943 0624

Speaker:
Professor Peter J. Stuckey
Professor, Department of Data Science and Artificial Intelligence
Monash University

Abstract:
Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy. A commonly criticised point, however, is that the resulting trees may not necessarily be the best representation of the data in terms of accuracy and size. In recent years, this motivated the development of optimal classification tree algorithms that globally optimise the decision tree in contrast to heuristic methods that perform a sequence of locally optimal decisions.
In this talk I will explore the history of building decision trees, from greedy heuristic methods to modern optimal approaches.
In particular I will discuss a novel algorithm for learning optimal classification trees based on dynamic programming and search. Our algorithm supports constraints on the depth of the tree and number of nodes. The success of our approach is attributed to a series of specialised techniques that exploit properties unique to classification trees. Whereas algorithms for optimal classification trees have traditionally been plagued by high runtimes and limited scalability, we show in a detailed experimental study that our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances, providing several orders of magnitude improvements and notably contributing towards the practical realisation of optimal decision trees.

Biography:
Professor Peter J. Stuckey is a Professor in the Department of Data Science and Artificial Intelligence in the Faculty of Information Technology at Monash University. Peter Stuckey is a pioneer in constraint programming and logic programming. His research interests include: discrete optimization; programming languages, in particular declarative programing languages; constraint solving algorithms; path finding; bioinformatics; and constraint-based graphics; all relying on his expertise in symbolic and constraint reasoning. He enjoys problem solving in any area, having publications in e.g. databases, election science, system security, and timetabling, and working with companies such as Oracle and Rio Tinto on problems that interest them.

Peter Stuckey received a B.Sc and Ph.D both in Computer Science from Monash University in 1985 and 1988 respectively. Since then he has worked at IBM T.J. Watson Research Labs, the University of Melbourne and Monash University. In 2009 he was recognized as an ACM Distinguished Scientist. In 2010 he was awarded the Google Australia Eureka Prize for Innovation in Computer Science for his work on lazy clause generation. He was awarded the 2010 University of Melbourne Woodward Medal for most outstanding publication in Science and Technology across the university. In 2019 he was elected as an AAAI Fellow. and awarded the Association of Constraint Programming Award for Research Excellence. He has over 125 journal and 325 conference publications and 17,000 citations with an h-index of 62.

Enquiries: Mr. Jeff Liu at Tel. 3943 0624

Speaker:
Prof. CAI Shaowei
Institute of Software
Chinese Academy of Sciences

Abstract:
Satisfiability Modulo Theories (SMT) is the problem of deciding the satisfiability of a first order logic formula with respect to certain background theories. SMT solvers have become important formal verification engines, with applications in various domains. In this talk, I will introduce the basis of SMT solving and present our work on improving a famous SMT solver Z3, leading to Z3++, which has won 2 Gold Medals out of 6 from SMT Competition 2022.

Biography:
Shaowei Cai is a professor in Institute of Software, Chinese Academy of Sciences. He has obtained his PhD from Peking University in 2012, with Doctoral Dissertation Award. His research focus on constraint solving (particularly SAT, SMT, and integer programming), combinatorial optimization, and formal verification, as well as their applications in industries. He has won more than 10 Gold Medals from SAT and SMT Competitions, and the Best Paper Award of SAT 2021 conference.

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https://cuhk.zoom.us/j/99411951727

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. Hai Zhou
Associate Professor, Department of Electrical and Computer Engineering
Northwestern University

Abstract:
With the increasing cost and complexity in semiconductor hardware designs, circuit IP protection has become an important and challenging problem in hardware security. Logic encryption is a promising technique that modifies a sensitive circuit to a locked one with a password, such that only authorized users can access it. During its history of more than 20 years, many different attacks and defenses have been designed and proposed. In this talk, after a brief introduction to logic encryption, I will present important attacking and defending techniques in the field. Especially, the focus will be on the few key attacks and defenses created in NuLogiCS group at Northwestern.

Biography:
Hai Zhou is the director of the NuLogiCS Research Group in the Electrical and Computer Engineering at Northwestern University and a member of the Center for Ultra Scale Computing and Information Security (CUCIS). His research interest is on Logical Methods for Computer Systems (LogiCS), where logics is used to construct reactive computer systems (in the form of hardware, software, or protocol) and to verify their properties (e.g. correctness, security, and efficiency). In other words, he is interested in algorithms, formal methods, optimization, and their applications to security, machine learning, and economics.

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Dr. Baoyuan WU
Associate Professor, School of Data Science
The Chinese University of Hong Kong, Shenzhen

Abstract:
In this talk, Dr. Wu will review the development of backdoor learning and his lastest works on backdoor attack and defense. The first is the backdoor attack with sample-specific triggers, which can bypass most existing defense methods, as they are mainly developed for defending against sample-agnostic triggers. Then, he will introduce two effective backdoor defense methods which could preclude the backdoor injection during the training process, through exploring some intrinsic properties of poisoned samples. Finally, he will introduce BackdoorBench, which is a comprehensive benchmark containing mainstream backdoor attack and defense methods, as well as 8,000 pairs of attack-defense evaluations, several interesting findings and analysis. It was recently released at “What is BackdoorBench? ”

Biography:
Dr. Baoyuan Wu is an Associate Professor of School of Data Science, the Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), and the director of the Secure Computing Lab of Big Data, Shenzhen Research Institute of Big Data (SRIBD). His research interests are AI security and privacy, machine learning, computer vision and optimization. He has published 50+ top-tier conference and journal papers, including TPAMI, IJCV, NeurIPS, CVPR, ICCV, ECCV, ICLR, AAAI. He is currently serving as an Associate Editor of Neurocomputing, Area Chair of NeurIPS 2022, ICLR 2022/2023, AAAI 2022.

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https://cuhk.zoom.us/j/91408751707

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Dr. Li Zhenguo
Director, AI Theory Lab
Huawei Noah’s Ark Lab, Hong Kong

Abstract:
Noah’s Ark Lab is the AI research center for Huawei, with the mission of making significant contribution to both the company and society through innovation in artificial intelligence (AI), data mining and related fields. Our AI theory team focuses on the fundamental research in machine learning, including cutting-edge theories and algorithms such as out-of-distribution (OoD) generalization and controllable generative modeling, and disruptive applications such as self-driving. In this talk, we will present some of our progresses in out-of-distribution generalization, including OoD-learnable theories and model selection, understanding and quantification of OoD properties of various benchmark datasets, and related applications. We will also highlight some key challenges for future studies.

Biography:
Zhenguo Li is currently the director of the AI Theory Lab in Huawei Noah’s Ark Lab, Hong Kong. Before joining Huawei Noah’s Ark lab, he was an associate research scientist in the department of electrical engineering, Columbia University, working with Prof. Shih-Fu Chang. He received BS and MS degrees in mathematics at Peking University, and PhD degree in machine learning at The Chinese University of Hong Kong, advised by Prof. Xiaoou Tang. His current research interests include machine learning and its applications.

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. Au, Kwok Wai Samuel
Professor, Department of Mechanical and Automation Engineering, CUHK
Professor, Department of Surgery, CUHK
Co-Director, Chow Yuk Ho Technology Centre for Innovative Medicine, CUHK
Director, Multiscale Medical Robotic Center, InnoHK

Abstract:
Over the past decades, a wide range of bio-inspired legged robots have been developed that can run, jump, and climb over a variety of challenging surfaces. However, in terms of maneuverability they still lag far behind animals. Animals can effectively use their mechanical body and external appendages (such as tails) to achieve spectacular maneuverability, energy efficient locomotion, and robust stabilization to large perturbations which may not be easily attained in the existing legged robots. In this talk, we will present our efforts on the development of innovative legged robots with greater mobility/efficiency/robustness, comparable to its biological counterpart. We will discuss the fundamental challenges in legged robots and demonstrate the feasibility of developing such kinds of agile systems. We believe our solutions could potentially lead to more efficient legged robot design and give the legged robot animal-like mobility and robustness. Furthermore, we will also present our robotic development on surgery domain and show how these technologies can be integrated with legged robots to create novel teleoperated legged mobile manipulators for service and construction applications.

Biography:
Dr. Kwok Wai Samuel Au is currently a Professor of the Department of Mechanical and Automation Engineering and Department of Surgery (by courtesy) at CUHK, and the Founding Director of Multiscale Medical Robotics Center, InnoHK. In Sept 2019, Dr. Au found Cornerstone Robotics and has been serving as the president of the company, aiming to create affordable surgical robotic solution. Dr. Au received the B.Eng. and M.Phil degrees in Mechanical and Automation Engineering from CUHK in 1997 and 1999, respectively and completed his Ph.D. degree in Mechanical Engineering at MIT in 2007. During his PhD study, Prof. Hugh Herr, Dr. Au, and other colleagues from MIT Biomechatronics group co-invented the MIT Powered Ankle-foot Prosthesis.

Before joining CUHK(2016), he was the manager of Systems Analysis of the New Product Development Department at Intuitive Surgical, Inc. At Intuitive Surgical, he co-invented and was leading the software and control algorithm development for the FDA cleared da Vinci Si Single-Site surgical platform (2012), Single-Site Wristed Needle Driver (2014), and da Vinci Xi Single-Site surgical platform (2016). He was also a founding team member for the early development of Intuitive Surgical’s FDA cleared robot-assisted catheter system, da Vinci ION system from 2008 to 2012.

Dr. Au co-authored over 60 peer-reviewed manuscripts and conference journals, 17 granted US patents/EP, and 3 pending US Patents. He has won numerous awards including the first prize in the American Society of Mechanical Engineers (ASME) Student Mechanism Design Competition in 2007, Intuitive Surgical Problem Solving Award in 2010, and Intuitive Surgical Inventor Award in 2011.

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Dr. Quanshi Zhang

Abstract:
The interpretability of deep neural networks has received increasing attention in recent years, and diverse methods of explainable AI (XAI) have been developed. Currently, most XAI methods are designed in an experimental manner without solid theoretic foundations, or simply fit explanation results to people’s cognition instead of objectively reflecting the true knowledge in the DNN. The lack of theoretic supports has hampered the future development of XAI. Therefore, in this talk, Dr. Quanshi Zhang will review several studies of explainable AI theories of his research group in recent years, which use the system of game-theoretic interactions to explain the attribution, the adversarial robustness, model generalization, visual concepts learned by the DNN, and the aesthetic level of images.

Biography:
Dr. Quanshi Zhang is an associate professor at Shanghai Jiao Tong University, China. He received the Ph.D. degree from the University of Tokyo in 2014. From 2014 to 2018, he was a post-doctoral researcher at the University of California, Los Angeles. His research interests are mainly machine learning and computer vision. In particular, he has made influential research in explainable AI (XAI) and received the ACM China Rising Star Award. He was the co-chairs of the workshops towards XAI in ICML 2021, AAAI 2019, and CVPR 2019. We is the speaker of the tutorials on XAI at IJCAI 2020 and IJCAI 2021.

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https://cuhk.zoom.us/j/98782922295

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Dr. Zichao Yang

Abstract:
In recent years, advances in deep learning for NLP research have been mainly propelled by massive computation and large amounts of data. Despite the progress, those giant models still rely on in-domain data to work well in down-stream tasks, which is hard and costly to obtain in practice. In this talk, I am going to talk about my research efforts towards overcoming the challenge of learning with limited supervision by designing efficient NLP models. My research spans three directions towards this goal: designing structural neural networks models according to NLP data structures to take full advantage of labeled data, effective unsupervised models to alleviate the dependency on labeled corpus and data augmentation strategies which creates large amounts of labeled data at almost no cost.

Biography:
Zichao Yang is currently a research scientist working at Bytedance. Before that he obtained his Ph.D from CMU working with Eric Xing, Alex Smola and Taylor Berg-Kirkpatrick. His research interests lie in machine learning and deep learning with applications in NLP. He has published dozens of papers in top AI/ML conferences. He obtained his MPhil degree from CUHK and bachelor degree from Shanghai Jiao Tong University. He worked at Citadel Securities as a quantitative researcher, specializing in ML research for financial data, before joining Bytedance. He also interned in Google DeepMind, Google Brain and Microsoft Research during his Phd.

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https://cuhk.zoom.us/j/94185450343

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. HAN Dongsu

Abstract:
Internet video has experienced tremendous growth over the last few decades and is still growing at a rapid pace. Internet video now accounts for 73% of Internet traffic and is expected to quadruple in the next five years. Augmented reality and virtual reality streaming, projected to increase twentyfold in five years, will also accelerate this trend.

In this talk, I will argue that advances in deep neural networks present new opportunities that can fundamentally change Internet video delivery. In particular, deep neural networks allow the content delivery network to easily capture the content of the video and thus enable content-aware video delivery. To demonstrate this, I will present NAS, a new Internet video delivery framework that integrates deep neural network based quality enhancements with adaptive streaming.

NAS incorporates a super-resolution deep neural network (DNN) and a deep re-inforcement neural network to optimize the user quality of experience (QoE). It outperforms the current state of the art, dramatically improving visual quality. It improves the average QoE by 43.08% using the same bandwidth budget or saving 17.13% of bandwidth while providing the same user QoE.

Finally, I will talk about our recent research progress in supporting live video and mobile devices in AI-assisted video delivery that demonstrate the possibility of new designs that tightly integrate deep learning into Internet video streaming.

Biography:
Dongsu Han (Member, IEEE) is currently an Associate Professor with the School of Electrical Engineering at KAIST. He received the B.S. degree in computer science from KAIST in 2003 and the Ph.D. degree in computer science from Carnegie Mellon University in 2012. His research interests include networking, distributed systems, and network/system security. He has received Best Paper Award and Community Award from USENIX NSDI. More details about his research can be found at http://ina.kaist.ac.kr.

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https://cuhk.zoom.us/j/93072774638

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Dr. Huaicheng Li

Abstract:
The increasing complexity in storage software and hardware brings new challenges to achieve predictable performance and efficiency. On the one hand, emerging hardware break long-held system design principles and are held back by aged and inflexible system interfaces and usage models, requiring radical rethinking on the software stack to leverage new hardware capabilities for optimal performance. On the other hand, the computing landscape is becoming increasingly heterogeneous and complex, demanding explicit systems-level support to manage hardware-associated complexity and idiosyncrasy, which is unfortunately still largely missing.

In this talk, I will discuss my efforts to build low-latency and cost-efficient datacenter storage systems. By revisiting existing storage interface/abstraction designs and software/hardware responsibility divisions, I will present holistic storage stack designs for cloud datacenters, which deliver orders of magnitude of latency improvement and significantly improved cost-efficiency.

Biography:
Huaicheng is a postdoc at CMU in the Parallel Data Lab (PDL). He received his Ph.D. from University of Chicago. His interests are mainly in Operating Systems and Storage Systems, with a focus on building high-performance and cost-efficient storage infrastructure for datacenters. His research has been recognized by two best paper nominations at FAST (2017 and 2018) and has also made real impact, with production deployment in datacenters, code integration to Linux, and a storage research platform widely used by the research community.

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https://cuhk.zoom.us/j/95132173578

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Dr. Yaoqing Yang

Abstract:
Machine learning (ML) models are increasingly being deployed in safety-critical applications, making their generalization and reliability a problem of urgent societal importance. To date, our understanding of ML is still limited because (i) the narrow problem settings considered in studies and the (often) cherry-picked results lead to incomplete/conflicting conclusions on the failures of ML; (ii) focusing on low-dimensional intuitions results in a limited understanding of the global structure of ML problems. In this talk, I will present several recent results on “generalization metrics” to measure ML models. I will show that (i) generalization metrics such as the connectivity between local minima can quantify global structures of optimization loss landscapes, which can lead to more accurate predictions on test performance than existing metrics; (ii) carefully measuring and characterizing the different phases of loss landscape structures in ML can provide a more complete picture of generalization. Specifically, I show that different phases of learning require different ways to address failures in generalization. Furthermore, most conventional generalization metrics focus on the so-called generalization gap, which is indirect and of limited practical value. I will discuss novel metrics referred to as “shape metrics” that allow us to predict test accuracy directly instead of the generalization gap. I also show that one can use shape metrics to achieve improved compression and out-of-distribution robustness of ML models. I will discuss theoretical results and present large-scale empirical analyses for different quantity/quality of data, different model architectures, and different optimization hyperparameter settings to provide a comprehensive picture of generalization. I will also discuss practical applications of utilizing these generalization metrics to improve ML models’ training, efficiency, and robustness.

Biography:
Dr. Yaoqing Yang is a postdoctoral researcher at the RISE Lab at UC Berkeley. He received his PhD from Carnegie Mellon University and B.S. from Tsinghua University, China. He is currently focusing on machine learning, and his main contributions to machine learning are towards improving reliability and generalization in the face of uncertainty, both in the data and the compute platform. His PhD thesis laid the foundation for an exciting field of research—coded computing—where information-theoretic techniques are developed to address unreliability in computing platforms. His works have won the best paper finalist at ICDCS and have been published multiple times in NeurIPS, CVPR, and IEEE Transactions on Information Theory. He has worked as a research intern at Microsoft, MERL and Bell Labs, and two of his joint CVPR papers with MERL have both received more than 300 citations. He is also the recipient of the 2015 John and Claire Bertucci Fellowship.

Join Zoom Meeting:
https://cuhk.zoom.us/j/99128234597

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Mr. Guodong Zhang

Abstract:
Deep learning has achieved huge successes over the last few years, largely due to three important ideas: deep models with residual connections, parallelism, and gradient-based learning. However, it was shown that (1) deep ResNets behave like ensembles of shallow networks; (2) naively increasing the scale of data parallelism leads to diminishing return; (3) gradient-based learning could converge to spurious fixed points in the multiagent setting.

In this talk, I will present some of my works on understanding and addressing these issues. First, I will give a general recipe for training very deep neural networks without shortcuts. Second, I will present a noisy quadratic model for neural network optimization, which qualitatively predicts scaling properties of a variety of optimizers and in particular suggests that second-order algorithms would benefit more from data parallelism. Third, I will describe a novel algorithm that finds desired equilibria and saves us from converging to spurious fixed points in multi-agent games. In the end, I will conclude with future directions towards building intelligent machines that can learn from experience efficiently and reason about their own decisions.

Biography:
Guodong Zhang is a PhD candidate in the machine learning group at the University of Toronto, advised by Roger Grosse. His research lies at the intersection between machine learning, optimization, and Bayesian statistics. In particular, his research focuses on understanding and improving algorithms for optimization, Bayesian inference, and multi-agent games in the context of deep learning. He has been recognized through the Apple PhD fellowship, Borealis AI fellowship, and many other scholarships. In the past, he has also spent time at Institute for Advanced Study of Princeton and industry research labs (including DeepMind, Google Brain, and Microsoft Research).

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https://cuhk.zoom.us/j/95830950658

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Ms. Jiasi Shen

Abstract:
Software now plays a central role in numerous aspects of human society. Current software development practices involve significant developer effort in all phases of the software life cycle, including the development of new software, detection and elimination of defects and security vulnerabilities in existing software, maintenance of legacy software, and integration of existing software into more contexts, with the quality of the resulting software still leaving much to be desired. The goal of my research is to improve software quality and reduce costs by automating tasks that currently require substantial manual engineering effort.

I present a novel approach for automatic software rejuvenation, which takes an existing program, learns its core functionality as a black box, builds a model that captures this functionality, and uses the model to generate a new program. The new program delivers the same core functionality but is potentially augmented or transformed to operate successfully in different environments. This research enables the rejuvenation and retargeting of existing software and provides a powerful way for developers to express program functionality that adapts flexibly to a variety of contexts. In this talk, I will show how we applied these techniques to two classes of software systems, specifically database-backed programs and stream-processing computations, and discuss the broader implications of these approaches.

Biography:
Jiasi Shen is a Ph.D. candidate at MIT EECS advised by Professor Martin Rinard. She received her bachelor’s degree from Peking University. Her main research interests are in programming languages and software engineering. She was named an EECS Rising Star in 2020.

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https://cuhk.zoom.us/j/91743099396

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. Wen Wujie

Abstract:
Thanks to recent model innovation and hardware advancement, machine learning (ML) has now achieved extraordinary success in many fields ranging from daily image classification, object detection, to security- sensitive biometric authentication and autonomous vehicles. To facilitate fast and secure end-to-end machine learning services, extensive studies have been conducted on ML hardware acceleration and data or model-incurred adversarial attacks. Different from these existing efforts, in this talk, we will present a new understanding of the efficiency and security challenges in accelerated ML services. The talk starts with the development of the very first “machine vision” (NOT “human vision”) guided image compression framework tailored for fast cloud-based machine learning services with guaranteed accuracy, inspired by an insightful understanding about the difference between machine learning (or “machine vision”) and human vision on image perception. Then we will discuss “StegoNet”- a new breed stegomalware taking advantage of machine learning service as a stealthy channel to conceal malicious intent (malware). Unlike existing attacks focusing only on misleading ML outcomes, “StegoNet” for the first time achieves far more diversified adversarial goals without compromising ML service quality. Our research prospects will be also given at the end of this talk, offering the audiences an alternative thinking about developing efficient and secure machine learning services.

Biography:
Wujie Wen is an assistant professor in the Department of Electrical and Computer Engineering at Lehigh University. He received his Ph.D. from University of Pittsburgh in 2015. He earned his B.S. and M.S. degrees in electronic engineering from Beijing Jiaotong University and Tsinghua University, Beijing, China, in 2006 and 2010, respectively. He was an assistant professor in the ECE department of Florida International University, Miami, FL, during 2015-2019. Before joining academia, he also worked with AMD and Broadcom for various engineer and intern positions. His research interests include reliable and secure deep learning, energy-efficient computing, electronic design automation and emerging memory systems design. His works have been published widely across venues in design automation, security, machine learning/AI etc., including HPCA, DAC, ICCAD, DATE, ICPP, HOST, ACSAC, CVPR, ECCV, AAAI etc. He received best paper nominations from ASP-DAC2018, ICCAD2018, DATE2016 and DAC2014. Dr Wen served as the General Chair of ISVLSI 2019 (Miami), Technical Program Chair of ISVLSI 2018 (Hong Kong), as well as the program committee for many conferences such as DAC, ICCAD, DATE, etc. He is an associated editor of Neurocomputing and IEEE Circuit and Systems (CAS) Magazine. His research projects are currently sponsored by US National Science Foundation, Air Force Research Laboratory and Florida Center for Cybersecurity etc.

Join Zoom Meeting:
https://cuhk.zoom.us/j/98308617940

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. LI Yu

Abstract:
In this talk, I will give an overview of the research in our group. Essentially, we are developing new machine learning methods to resolve the problems in computational biology and health informatics, from sequence analysis, biomolecular structure prediction, and functional annotation to disease modeling, drug discovery, drug effect prediction, and combating antimicrobial resistance. We will show how to formulate problems in the biology and health field into machine learning problems, how to resolve them using cutting-edge machine learning techniques, and how the result could benefit the biology and healthcare field in return.

Biography:
Yu Li is an Assistant Professor in the Department of Computer Science and Engineering at CUHK. His main research interest is to develop novel and new machine learning methods, mainly deep learning methods, for solving the computational problems in healthcare and biology, understanding the principles behind the bio-world, and eventually improving people’s health and wellness. He obtained his PhD in computer science from KAUST in Saudi Arabia, in Oct 2020. He obtained MS degree in computer science from KAUST at 2016. Before that, he got the Bachelor degree in Biosciences from University of Science and Technology of China (USTC).

Join Zoom Meeting:
https://cuhk.zoom.us/j/98928672713

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Mr. Dennis Lee

Abstract:
With the ever increasing demand and aging population, it is envisioned that adoption of AI technology will support Hospital Authority to tackle various strategic service challenges and deliver improvements. HA has setup AI Strategy Framework two years ago and begun setup process & infrastructure to support AI development and delivery. The establishment of AI Lab and AI delivery center is aimed to flourish AI innovations by engaging internal and external collaboration for Proof of Concept development; and also to build data and integration pipeline to validate AI solution and integrate into the HA services at scale.

By leverage 3 platforms to (1) Improve awareness of HA staff (2) Match AI supply and Demand (3) data pipeline for timely prediction, we can gradually scale AI innovations and solution in Hospital Authority. Over the past year, many clinical and non-clinical Proof of Concept has been developed and validated. The AI Chest X-ray pilot project has been implemented for General Outpatient Clinics and Emergency Department with the aim to reduce report turnaround time and provide decision support for abnormal chest x-ray imaging.

Biography:
Mr. Dennis Lee currently serves as the Senior System Manager for Artificial Intelligence Systems of the Hong Kong Hospital Authority. He current work involve developing the Artificial Intelligence and Big Data Platform to streamline data acquisition for facilitating HA data analysis via Business Intelligence, to develop Hospital Command Center dashboards, and solution deployment for Artificial Intelligence. Mr. Lee has also been leading the Corporate Project management office and as program managers for several large scale system implementations.

Join Zoom Meeting:
https://cuhk.zoom.us/j/95162965909

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Mr. Wenbo Guo

Abstract:
Nowadays, security researchers are increasingly using AI to automate and facilitate security analysis. Although making some meaningful progress, AI has not maximized its capability in security yet due to two challenges. First, existing ML techniques have not reached security professionals’ requirements in critical properties, such as interpretability and adversary-resistancy. Second, Security data imposes many new technical challenges, which break the assumptions of existing ML Models and thus jeopardize their efficacy.

In this talk, I will describe my research efforts to address the above challenges, with a primary focus on strengthening the interpretability of blackbox deep learning models and deep reinforcement learning policies. Regarding deep neural networks, I will describe an explanation method for deep learning-based security applications and demonstrate how security analysts could benefit from this method to establish trust in blackbox models and conduct efficient finetuning. As for DRL policies, I will introduce a novel approach to draw critical states/actions of a DRL agent and show how to utilize the above explanations to scrutinize policy weaknesses, remediate policy errors, and even defend against adversarial attacks. Finally, I will conclude by highlighting my future plan towards strengthening the trustworthiness of advanced ML techniques and maximizing their capability in cyber defenses.

Biography:
Wenbo Guo is a Ph.D. Candidate at Penn State, advised by Professor Xinyu Xing. His research interests are machine learning and cybersecurity. His work includes strengthening the fundamental properties of machine learning models and designing customized machine learning models to handle security-unique challenges. He is a recipient of the IBM Ph.D. Fellowship (2020-2022), Facebook/Baidu Ph.D. Fellowship Finalist (2020), and ACM CCS Outstanding Paper Award (2018). His research has been featured by multiple mainstream media and has appeared in a diverse set of top-tier venues in security, machine learning, and data mining. Going beyond academic research, he also actively participates in many world-class cybersecurity competitions and has won the 2018 DEFCON/GeekPwn AI challenge finalist award.

Join Zoom Meeting:
https://cuhk.zoom.us/j/95859338221

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. Wayne Luk

Abstract:
This talk describes recent research on meta-programming techniques for mapping high-level descriptions to multiple hardware platforms. The purpose is to enhance design productivity and maintainability. Our approach is based on decoupling functional concerns from optimisation concerns, allowing separate descriptions to be independently maintained by two types of experts: application experts focus on algorithmic behaviour, while platform experts focus on the mapping process. Our approach supports customisable optimisations to rapidly capture a wide range of mapping strategies targeting multiple hardware platforms, and reusable strategies to allow optimisations to be described once and applied to multiple applications. Examples will be provided to illustrate how the proposed approach can map a single high-level program into multi-core processors and reconfigurable hardware platforms.

Biography:
Wayne Luk is Professor of Computer Engineering with Imperial College London and the Director of the EPSRC Centre for doctoral training in High Performance Embedded and Distributed Systems. His research focuses on theory and practice of customizing hardware and software for specific application domains, such as computational finance, climate modelling, and genomic data analysis. He is a fellow of the Royal Academy of Engineering, IEEE, and BCS.

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. XU Hong

Abstract:
As cloud computing becomes ubiquitous, the network stack in this virtualized environment is becoming a focal point of research with unique challenges and opportunities. In this talk, I will introduce our efforts in this space.

First, from an architectural perspective, the network stack remains a part of the guest OS inside a VM in the cloud. I will argue that this legacy architecture is becoming a barrier to innovation/evolution. The tight coupling between the network stack and the guest OS causes many deployment troubles to tenants and management and efficiency problems to the cloud provider. I will present our vision of providing the network stack as a service as a way to address these issues. The idea is to decouple the network stack from the guest OS, and offer it as an independent entity implemented by the cloud provider. I will discuss the design and evaluation of a concrete framework called NetKernel to enable this vision. Then in the second part, I will focus on container communication, which is a common scenario in the cloud. I will present a new system called PipeDevice that adopts a hardware-software co-design approach to enable low-overhead intra-host container communication using commodity FPGA.

Biography:
Hong Xu is an Associate Professor in Department of Computer Science and Engineering, The Chinese University of Hong Kong. His research area is computer networking and systems, particularly big data systems and data center networks. From 2013 to 2020 he was with City University of Hong Kong. He received his B.Eng. from The Chinese University of Hong Kong in 2007, and his M.A.Sc. and Ph.D. from University of Toronto in 2009 and 2013, respectively. He was the recipient of an Early Career Scheme Grant from the Hong Kong Research Grants Council in 2014. He received three best paper awards, including the IEEE ICNP 2015 best paper award. He is a senior member of both IEEE and ACM.

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. Kai Chen
Associate Professor
Department of Computer Science & Engineering, HKUST

Abstract:
Communication overhead poses a significant challenge to distributed DNN training. In this talk, I will overview existing efforts toward this challenge, study their advantages and shortcomings, and further present a novel solution exploiting the domain-specific characteristics of deep learning to optimize the communication overhead of distributed DNN training in a fine-grained manner. Our solution consists of several key innovations beyond prior work, including bounded-loss tolerant transmission, gradient-aware flow scheduling, and order-free per-packet load-balancing, etc., delivering up to 84.3% training acceleration over the best existing solutions. Our proposal by no means provides an ultimate answer to this research problem, instead, we hope it can inspire more critical thinkings on intersection between Networking and AI.

Biography:
Kai Chen is an Associate Professor at HKUST, the Director of Intelligent Networking Systems Lab (iSING Lab) and HKUST-WeChat joint Lab on Artificial Intelligence Technology (WHAT Lab), as well as the PC for a RGC Theme-based Project. He received his BS and MS from University of Science and Technology of China in 2004 and 2007, and PhD from Northwestern University in 2012, respectively. His research interests include Data Center Networking, Cloud Computing, Machine Learning Systems, and Privacy-preserving Computing. His work has been published in various top venues such as SIGCOMM, NSDI and TON, etc., including a SIGCOMM best paper candidate. He is the Steering Committee Chair of APNet, serves on the Program Committees of SIGCOMM, NSDI, INFOCOM, etc., and the Editorial Boards of IEEE/ACM Transactions on Networking, Big Data, and Cloud Computing.

Join Zoom Meeting:
https://cuhk.zoom.us/j/98448863119?pwd=QUJVdzgvU1dnakJkM29ON21Eem9ZZz09

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. Sinno Jialin Pan
Provost’s Chair Associate Professor
School of Computer Science and Engineering
Nanyang Technological University

Abstract:
How to develop a loop to integrate existing knowledge to facilitate deep learning inference and then refine knowledge from the learning process is a crucial research problem. As first-order logic has been proven to be a powerful tool for knowledge representation and reasoning, interest in integrating firstorder logic into deep learning models has grown rapidly in recent years. In this talk, I will introduce our attempts to develop a unified integration framework of first-order logic and deep learning with applications to various joint inference tasks in NLP.

Biography:
Dr. Sinno Jialin Pan is a Provost’s Chair Associate Professor with the School of Computer Science and Engineering at Nanyang Technological University (NTU) in Singapore. He received his Ph.D. degree in computer science from the Hong Kong University of Science and Technology (HKUST) in 2011. Prior to joining NTU, he was a scientist and Lab Head with the Data Analytics Department at Institute for Infocomm Research in Singapore. He joined NTU as a Nanyang Assistant Professor in 2014. He was named to the list of “AI 10 to Watch” by the IEEE Intelligent Systems magazine in 2018. He serves as an Associate Editor for IEEE TPAMI, AIJ, and ACM TIST. His research interests include transfer learning and its real-world applications.

Join Zoom Meeting:
https://cuhk.zoom.us/j/97292230556?pwd=MDVrREkrWnFEMlF6aFRDQzJxQVlFUT09

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Dr. Jinwei Gu
R&D Executive Director
SenseBrain (aka SenseTime USA)

Abstract:
Smart sensing and perception refer to intelligent and efficient ways of measuring, modeling, and understanding of the physical world, which act as the eyes and ears of any AI-based system. Smart sensing and perception sit across the intersection of three related areas – computational imaging, representation learning, and scene understanding. Computational imaging refers to sensing the real world with optimally designed, task-specific, multi-modality sensors and optics which actively probes key visual information. Representation learning refers to learning the transformation from sensors’ raw output to some manifold embedding or feature spaces for further processing. Scene understanding includes both the low-level capture of a 3D scene of its physical properties, as well as high-level semantic perception and understanding of the scene. Advances in this area will not only benefit computer vision tasks but also result in better hardware, such as AI image sensors, AI ISP (Image Signal Processing) chips, and AI camera systems. In this talk, I will present several latest research results including high quality image restoration and accurate depth estimation from time-of-flight sensors or monocular videos, as well as some latest computational photography products in smart phones including under-display cameras, AI image sensors and AI ISP chips. I will also layout several open challenges and future research directions in this area.

Biography:
Jinwei Gu is the R&D Executive Director of SenseBrain (aka SenseTime USA). His current research focuses on low-level computer vision, computational photography, computational imaging, smart visual sensing and perception, and appearance modeling. He obtained his Ph.D. degree in 2010 from Columbia University, and his B.S and M.S. from Tsinghua University, in 2002 and 2005 respectively. Before joining
SenseTime, he was a senior research scientist in NVIDIA Research from 2015 to 2018. Prior to that, he was an assistant professor in Rochester Institute of Technology from 2010 to 2013, and a senior researcher in the media lab of Futurewei Technologies from 2013 to 2015. He serves as an associate editor for IEEE Transactions on Computational Imaging (TCI) and IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), an area chair for ICCV2019, ECCV2020, and CVPR2021, and industry chair for ICCP2020. He is an IEEE senior member since 2018. His research work has been successfully transferred to many products such as NVIDIA CoPilot SDK, DriveIX SDK, as well as super resolution, super night, portrait restoration, RGBW solution which are widely used in many flagship mobile phones.

Join Zoom Meeting:
https://cuhk.zoom.us/j/97322964334?pwd=cGRJdUx1bkxFaENJKzVwcHdQQm5sZz09

Enquiries: Ms. Karen Chan at Tel. 3943 8439

Speaker:
Prof. DOU Qi

Abstract:
With advancements in information technologies and medicine, the operating room has undergone tremendous transformations evolving into a highly complicated environment. These achievements further innovate the surgery procedure and have great promise to enhance the patient’s safety. Within the new generation of operating theatre, the computer-assisted system plays an important role to provide surgeons with reliable contextual support. In this talk, I will present a series of deep learning methods towards interdisciplinary researches at artificial intelligence for surgical robotic perception, for automated surgical workflow analysis, instrument presence detection, surgical tool segmentation, surgical scene perception, etc. The proposed methods cover a wide range of deep learning topics including semi-supervised learning, relational graph learning, learning-based stereo depth estimation, reinforcement learning, etc. The challenges, up-to-date progresses and promising future directions of AI-powered context-aware operating theaters will also be discussed.

Biography:
Prof. DOU Qi is an Assistant Professor with the Department of Computer Science & Engineering, CUHK. Her research interests lie in innovating collaborative intelligent systems that support delivery of high-quality medical diagnosis, intervention and education for next-generation healthcare. Her team pioneers synergistic advancements across artificial intelligence, medical image analysis, surgical data science, and medical robotics, with an impact to support demanding clinical workflows such as robotic minimally invasive surgery.

Enquiries: Miss Karen Chan at Tel. 3943 8439

Speaker:
Prof. HO Tsung Yi

Abstract:
Advances in microfluidic technologies have led to the emergence of biochip devices for automating laboratory procedures in biochemistry and molecular biology. Corresponding systems are revolutionizing a diverse range of applications, e.g. point-of-care clinical diagnostics, drug discovery, and DNA sequencing–with an increasing market. However, continued growth (and larger revenues resulting from technology adoption by pharmaceutical and healthcare companies) depends on advances in chip integration and design-automation tools. Thus, there is a need to deliver the same level of design automation support to the biochip designer that the semiconductor industry now takes for granted. In particular, the design of efficient design automation algorithms for implementing biochemistry protocols to ensure that biochips are as versatile as the macro-labs that they are intended to replace. This talk will first describe technology platforms for accomplishing “biochemistry on a chip”, and introduce the audience to both the droplet-based “digital” microfluidics based on electrowetting actuation and flow-based “continuous” microfluidics based on microvalve technology. Next, the presenter will describe system-level synthesis includes operation scheduling and resource binding algorithms, and physical-level synthesis includes placement and routing optimizations. Moreover, control synthesis and sensor feedback-based cyberphysical adaptation will be presented. In this way, the audience will see how a “biochip compiler” can translate protocol descriptions provided by an end user (e.g., a chemist or a nurse at a doctor’s clinic) to a set of optimized and executable fluidic instructions that will run on the underlying microfluidic platform. Finally, present status and future challenges of open-source microfluidic ecosystem will be covered.

Biography:
Tsung-Yi Ho received his Ph.D. in Electrical Engineering from National Taiwan University in 2005. His research interests include several areas of computing and emerging technologies, especially in design automation of microfluidic biochips. He has been the recipient of the Invitational Fellowship of the Japan Society for the Promotion of Science (JSPS), the Humboldt Research Fellowship by the Alexander von Humboldt Foundation, the Hans Fischer Fellowship by the Institute of Advanced Study of the Technische Universität München, and the International Visiting Research Scholarship by the Peter Wall Institute of Advanced Study of the University of British Columbia. He was a recipient of the Best Paper Awards at the VLSI Test Symposium (VTS) in 2013 and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2015. He served as a Distinguished Visitor of the IEEE Computer Society for 2013-2015, a Distinguished Lecturer of the IEEE Circuits and Systems Society for 2016-2017, the Chair of the IEEE Computer Society Tainan Chapter for 2013-2015, and the Chair of the ACM SIGDA Taiwan Chapter for 2014-2015. Currently, he serves as the Program Director of both EDA and AI Research Programs of Ministry of Science and Technology in Taiwan, VP Technical Activities of IEEE CEDA, an ACM Distinguished Speaker, and Associate Editor of the ACM Journal on Emerging Technologies in Computing Systems, ACM Transactions on Design Automation of Electronic Systems, ACM Transactions on Embedded Computing Systems, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, and IEEE Transactions on Very Large Scale Integration Systems, Guest Editor of IEEE Design & Test of Computers, and the Technical Program Committees of major conferences, including DAC, ICCAD, DATE, ASP-DAC, ISPD, ICCD, etc. He is a Distinguished Member of ACM.

Enquiries: Miss Karen Chan at Tel. 3943 8439

Speaker:
Prof. FARNIA Farzan

Abstract:
Generative Adversarial Networks (GANs) represent a game between two machine players designed to learn the distribution of observed data.

Since their introduction in 2014, GANs have achieved state-of-the-art performance on a wide array of machine learning tasks. However, their success has been observed to heavily depend on the minimax optimization algorithm used for their training. This dependence is commonly attributed to the convergence speed of the underlying optimization algorithm. In this seminar, we focus on the generalization properties of GANs and present theoretical and numerical evidence that the minimax optimization algorithm also plays a key role in the successful generalization of the learned GAN model from training samples to unseen data. To this end, we analyze the generalization behavior of standard gradient-based minimax optimization algorithms through the lens of algorithmic stability. We leverage the algorithmic stability framework to compare the generalization performance of standard simultaneous-update and non-simultaneous-update gradient-based algorithms. Our theoretical analysis suggests the superiority of simultaneous-update algorithms in achieving a smaller generalization error for the trained GAN model.

Finally, we present numerical results demonstrating the role of simultaneous-update minimax optimization algorithms in the proper generalization of trained GAN models.

Biography:
Farzan Farnia is an Assistant Professor of Computer Science and Engineering at The Chinese University of Hong Kong. Prior to joining CUHK, he was a postdoctoral research associate at the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, from 2019-2021. He received his master’s and PhD degrees in electrical engineering from Stanford University and his bachelor’s degrees in electrical engineering and mathematics from Sharif University of Technology. At Stanford, he was a graduate research assistant at the Information Systems Laboratory advised by Professor David Tse. Farzan’s research interests span statistical learning theory, information theory, and convex optimization. He has been the recipient of the Stanford Graduate Fellowship (Sequoia CapitalFellowship) between 2013-2016 and the Numerical Technology Founders Prize as the second top performer of Stanford’s electrical engineering PhD qualifying exams in 2014.

Enquiries: Miss Karen Chan at Tel. 3943 8439

Speaker:
Prof. CHAN Siu On
Assistant Professor
Department of Computer Science and Engineering, CUHK

Abstract:
This talk will summarize my works in two unrelated areas in complexity theory: distributional learning and extended formulation.

(1) Distributional Learning: Much of the work on distributional learning assumes the input samples are identically and independently distributed. A few recent works relax this assumption and instead assume the samples to be drawn as a trajectory from a Markov chain. Previous works by Wolfer and Kontorovich suggested that learning and identity test problems on ergodic chains can be reduced to the corresponding problems with i.i.d. samples. We show how to further reduce essentially every learning and identity testing problem on the (arguably most general) class of irreducible chans, by introducing the concept of k-cover time. The concept of k-cover time is a natural generalization of the usual notion of cover time.

The tight analysis of the sample complexity for reversible chains relies on a previous work by Ding-Lee-Peres. Their analysis relies on the so-called generalized second Ray-Knight isomorphism theorem, that connects the local time of a continuous-time reversible Markov chain to the Gaussian free field. It is natural to ask whether similar analysis can be generalized to general chains. We will discuss our ongoing work towards this goal.

(2) Extended formulation: Extended formulation lower bounds aim to show that linear programs (or other convex programs) need to be large in solving certain problems, such as constraint satisfaction. A natural open problem is whether refuting unsatisfiable 3-SAT instances requires linear programs of exponential size, and whether such a lower bound holds for every “downstream” NP-hard problem. I will discuss our ongoing work towards relating extended formulation lower bounds, using techniques from resolution lower bounds.

Biography:
Siu On CHAN graduated from the Chinese University of Hong Kong. He got his MSc at the University of Toronto and PhD at UC Berkeley. He was a postdoc at Microsoft Research New England. He is now an Assistant Professor at the Chinese University of Hong Kong. He is interested in the complexity of constraint satisfaction and learning algorithms. He won a Best Paper Award and a Best Student Paper Award at STOC 2013.

Enquiries: Miss Karen Chan at Tel. 3943 8439

Speaker:
Prof. YU Bei

Abstract:
Deep neural networks (DNNs) are currently widely used for many artificial intelligence AI applications with state-of-the-art accuracy, but they come at the cost of high computational complexity. Therefore, techniques that enable efficient computing of deep neural networks to improve key metrics—such as energy efficiency, throughput, and latency—without sacrificing accuracy are critical. This talk provides a structured treatment of the key principles and techniques for enabling efficient computing of DNNs, including implementation level, model level, and compilation level techniques.

Biography:
Bei Yu is currently an Associate Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He received PhD degree from Electrical and Computer Engineering, the University of Texas at Austin in 2014. His current research interests include machine learning with applications in VLSI CAD and computer vision. He has served as TPC Chair of 1st ACM/IEEE Workshop on Machine Learning for CAD (MLCAD), served in the program committees of DAC, ICCAD, DATE, ASPDAC, ISPD, the editorial boards of ACM Transactions on Design Automation of Electronic Systems (TODAES), Integration, the VLSI Journal. He is Editor of the IEEE TCCPS Newsletter.

Prof. Yu received seven Best Paper Awards from ASPDAC 2021 & 2012, ICTAI 2019, Integration, the VLSI Journal in 2018, ISPD 2017, SPIE Advanced Lithography Conference 2016, ICCAD 2013, six other Best Paper Award Nominations (DATE 2021, ICCAD 2020, ASPDAC 2019, DAC 2014, ASPDAC 2013, and ICCAD 2011), six ICCAD/ISPD contest awards.

Enquiries: Miss Karen Chan at Tel. 3943 8439