Speaker:

Professor WEI Lan
Associate Professor
University of Waterloo

Abstract:
Given its high mobility, high breakdown voltage and decent thermal conductivity, GaN technologies have shown great promise for high-power high-frequency (HP-HF), rapidly rising as a front runner for mm-wave to THz analog/RF circuits for IoT and 5G/6G wireless communication. Meanwhile, it is also heavily explored for power electronic applications for fast charging, data center, and electric vehicles. As GaN technology continues to improve, challenges of high design cost and sub-optimal system performance emerge as bottlenecks preventing the technology from wide scale deployment. Accurate, scalable and efficient compact model is key to overcome such challenges.

This presentation will provide a brief overview of the family of MVSG GaN compact model, including models for GaN HEMT, GaN multi-channel diodes and GaN transmission-line resistors.  The model formulation and various features will be introduced. Application examples will also be demonstrated, showing the potentials of this group of physics-based compact models.

Biography:

Prof. Lan Wei received her B.S. in Microelectronics from Peking University, China (2001), M.S and Ph. D. in Electrical Engineering from Stanford University, USA (2007 and 2010, respectively). She is currently an Associate Professor at the University of Waterloo, Canada. She has intensive experience in device physics-based compact modeling including silicon and GaN technologies, device-circuit interactive design and optimization, integrated nanoelectronic systems with low-dimensional materials, cryogenic CMOS device modeling and circuit design for quantum computing.  She has authored/co-authored more than 90 peered reviewed publications and served on the technical program committees including IEDM, ICCAD, DATE, ISQED, BCICTS, etc.

Enquiries:

Professor YU Bei (byu@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor Emir Demirovic

Assistant professor

TU Delft (Netherlands)

Abstract:
Decision trees are an effective and concise way of conveying information, easily understood by virtually everyone regardless of the topic. Given the recent interest in explainable AI and related fields, decision trees stand out as a popular choice. From the algorithmic side, the unique structure of decision trees is interesting since it may be exploited to obtain much more efficient algorithms than structure-oblivious approaches.

In this talk, I will give an overview of the research we have been doing on leveraging the decision tree structure from three disjoint angles: 1) machine learning with constraints, where the goal is construct the optimal regression/decision tree representing tabular data whilst potentially respecting different types of constraints such as fairness, 2) controller/policy verification, where the aim is to verify that a decision tree controller satisfies desired properties in continuous time, and 3) explaining the unsatisfiability of a combinatorial optimisation problem on a graph, by representing proofs of unsatisfiability as a tree using graph-specific concepts. We show that for each of these problems, exploiting the decision tree structure is important in obtain orders of magnitude runtime improvements and/or interpretability.

The talk summarises about half a dozen of our papers (AAAI’21/24, JMLR’22, NeurIPS’22/23, ICML’23/24) and is meant to be accessible to all backgrounds, with plenty of time for discussion!

Biography:

Emir Demirovic is an assistant professor at TU Delft (Netherlands). He leads the Constraint Solving (“ConSol”) research group, which advances combinatorial optimisation algorithms for a wide range of (real-world) problems, and co-directs the explainable AI in transportation lab (“XAIT”) as part of the Delft AI Labs. Prior to his appointment at TU Delft, Emir worked at the University of Melbourne, Vienna University of Technology, National Institute of Informatics (Tokyo), and at a production planning and scheduling company.

The focus point of Emir’s current work is solving techniques based on constraint programming, optimising decision trees, and explainable methods for combinatorial optimisation. He is also interested in industrial applications, robust/resilient optimisation, and the integration of optimisation and machine learning. He publishes in leading AI conferences (e.g., AAAI, NeurIPS) and specialised venues (e.g., CP, CPAIOR), attends scientific events such as Dagstuhl seminars, Lorentz workshops, and the Simons-Berkeley programme, and frequently organises incoming and outgoing visits, e.g., EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien.

Enquiries:

Professor LEE Ho Man Jimmy (jlee@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor Frank Liu
Professor and Inaugural Director, School of Data Science
Old Dominion University

Abstract:

Old Dominion University is a large public university located in the southwest coast of Virginia in the US. First established as a branch of College of William and Mary, its root can be traced to the 17th century England. School of Data Science is a newly established academic unit in Old Dominion University to encourage interdisciplinary research and education across the campus, as well as the region. I will give a brief introduction to the data science program, followed by open floor for Q&A and discussions.

Biography:

Frank Liu is a Professor of Computer Science and ECE at Old Dominion University. He is the founding director of the School of Data Science, with research experience spans academia, national laboratories, and corporate research labs. He is a Fellow of IEEE.

Enquiries:

Professor YOUNG Fung Yu (fyyoung@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor HSIEH Chang-Yu

Professor, College of Pharmaceutical Sciences

Zhejiang University

Abstract:
In recent years, generative AI has gained significant traction as a tool for designing novel molecules for therapeutic purposes. Advanced deep learning techniques have been increasingly adapted for drug design, yielding varying levels of success. In this seminar, I will provide an overview of this emerging field, highlighting the key challenges in applying generative AI to drug design and presenting our proposed solutions. Specifically, we combine principles from physics and chemistry with deep learning methods to discover more realistic drug candidates within the vast chemical space. Our results are supported by benchmark studies and validated through experimental wet lab testing.

Biography:

Dr. Chang-Yu (Kim) Hsieh is the QiuShi Engineering Professor at the College of Pharmaceutical Sciences, Zhejiang University. Before joining Zhejiang University, he led the Theory Division at Tencent Quantum Lab in Shenzhen, focusing on AI and quantum simulation for drug and material discovery. Prior to that, he was a postdoctoral researcher in the Department of Chemistry at MIT. His primary research interests lie in leveraging advanced computing technologies, including AI and quantum computing, to simulate and model material and molecular properties.

Enquiries:

Professor HENG Pheng Ann (pheng@cse.cuhk.edu.hk)

Ms. NG Man Nga Vivien (vivien@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor ZHANG Qirun
Assistant Professor, School of Computer Science
Georgia Institute of Technology

Abstract:
SMT formulas are first-order formulas extended with various theories. SMT solvers are fundamental tools for many program analysis and software engineering problems. The effectiveness and scalability of SMT solvers influence the performance of the underlying client analyzers. The most popular approach to improving SMT solving is by developing new constraint-solving algorithms. In this talk, we will discuss a new perspective on improving SMT solving via compiler optimization. Our basic idea involves translating SMT formulas to LLVM IR and leveraging LLVM optimization passes to simplify the IR. Then, we translate the simplified IR back to SMT formulas. In addition, this strategy can be extended to enhance the solving of unbounded SMT theories by utilizing their bounded counterparts.

Biography:

Qirun Zhang is an Assistant Professor in the School of Computer Science at Georgia Tech. His general research areas are programming languages and software engineering, focusing on developing new program analysis frameworks to improve software reliability. He received a PLDI 2020 Distinguished Paper Award, an FSE 2023 Distinguished Paper 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:

Professor LYU Rung Tsong Michael (lyu@cse.cuhk.edu.hk)

Ms. NG Man Nga Vivien (vivien@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor ZHANG Yizhe

Associate Professor, School of Computer Science and Engineering,

Nanjing University of Science and Technology

Abstract:
With advancements in deep learning and AI techniques, medical image segmentation has experienced rapid development over the past decade. Modern DL-based models, utilizing large labeled datasets, often produce impressive benchmark results. However, practical issues, such as reliability and trustworthiness, persist when these models are implemented in hospitals and medical facilities.

This talk addresses two related aspects of medical image segmentation for improving model deployment: model evaluation and test-time methods. First, we will discuss our recent work on deployment-centric model evaluation, evaluation of foundation models and related techniques. Next, we will cover a series of test-time methods that we have developed to improve video segmentation consistency, enhance the quality of medical image segmentation, and more recently, advance segmenting anything in medical images.

Finally, we will briefly highlight several other projects from my group and discuss directions in medical image segmentation research that we find promising and important.

Biography:

Yizhe Zhang, Ph.D., is an associate professor at Nanjing University of Science and Technology. He received his Ph.D. from the University of Notre Dame in the United States. Before returning to Nanjing, he was a senior research engineer at Qualcomm AI Research, San Diego, where he worked on efficient video segmentation and the spatiotemporal consistency of segmentation. He has conducted research on topics such as active learning, semi-supervised learning, model design, training and evaluation in medical image segmentation. As the first author, he has published papers in conferences and journals including MICCAI, Medical Image Analysis, IEEE TMI, BIBM, ICCV, AAAI, and WACV. As a key contributor, he was involved in biomedical image modeling and analysis work that won the 2017 Cozzarelli Prize awarded by the National Academy of Sciences.

Enquiries:

Professor HENG Pheng Ann (pheng@cse.cuhk.edu.hk)

Ms. NG Man Nga Vivien (vivien@cse.cuhk.edu.hk)

Mr. WONG O Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Mr. YANG Yuqing
Ph.D. candidate, Department of Computer Science and Engineering
The Ohio State University

Abstract:

The Super App computing paradigm, debuted in 2017 by world’s social computing giant WeChat, has revolutionized the mobile app architecture. By integrating a standalone execution engine for light-weight miniapp packages, the super apps allow third-party developers to integrate customized services to billions of super app users. Simultaneously, with the powerful features provided by super apps comes the imminent risk from attackers, who actively attempt to exploit the super app ecosystem, inflicting privacy and losses of billions of users, as well as millions of developers.

In this talk, Yuqing will dissect the concept of super app paradigm with a specific focus on the security risks from super app vulnerabilities and miniapp malware. First, he will discuss communication channel vulnerabilities we identified in front-ends and back-ends, followed by a dissection of miniapp malware against miniapp vetting, and malicious behaviors against the platform prior and after the miniapp vetting process. In the end, he will discuss mitigation countermeasures and open problems to improve the security and privacy in the realm of super apps.

Biography:
Yuqing Yang is a third-year PhD candidate at the Department of Computer Science and Engineering of The Ohio State University. His research interest primarily focuses on vulnerability and malware detection in mobile and web security, particularly in super apps. His papers have been published in prestigious conferences, such as ACM CCS, SIGMETRICS, and ICSE. He was a reviewer for many top-tier journals and conferences, including TIFS, TOSEM, DSN, USENIX Security, IEEE Security & Privacy, and ACM CCS. His researches have also been acknowledged by top super app vendors, including Tencent and Baidu.

Enquiries:

Professor MENG Wei (wei@cse.cuhk.edu.hk)

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Dr. XU Yilun
Ph.D. at Massachusetts Institute of Technology (MIT)
Research Scientist, NVIDIA Research  (from 2024 July)

Abstract:
Physics-inspired generative models such as diffusion models constitute a powerful family of generative models. The advantages of models in this family come from relatively stable training process and high capacity. A number of possible improvements remain possible. In this talk, I will discuss the enhancement and design of physics-inspired generative models. I will first present a sampling algorithm that combines the best of previous samplers, greatly accelerating the generation speed of text-to-image Stable Diffusion models. Additionally, I will discuss sampling methods to promote diversity in finite samples, by adding mutual repulsion forces between samples in the generative process. Secondly, I will discuss a training framework that introduces learnable discrete latent into continuous diffusion models. These latent simplify complex noise-to-data mappings and reduce the curvature of generative trajectories. Finally, I will introduce Poisson Flow Generative Models (PFGM), a new generative model arising from electrostatic theory, rivalling leading diffusion models. The extended version, PFGM++, places diffusion models and PFGM under the same framework and introduces new, better models. Several algorithms discussed in the talk are the state-of-the-art methods across standard benchmarks.

Biography:

Yilun Xu is an incoming research scientist at NVIDIA Research. He obtained his Ph.D. from MIT CSAIL in 2024, and his B.S. from Peking University in 2020. His research focuses on machine learning, with a current emphasis on new family of physics-inspired generative models, as well as the development of training and sampling algorithms for diffusion models. Previously, he has done research aimed on bridging information theory and machine learning.

Enquiries:

Professor HENG Pheng Ann (pheng@cse.cuhk.edu.hk)

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

** ALL ARE WELCOME **

Speaker:

Professor Roland Yap
National University of Singapore (NUS)

Abstract:
Solving finite domain constraints, e.g., Constraint Satisfaction Problems (CSP), is an intractable problem which nevertheless is one of practical significance. Due to the intractability, in practice, inference techniques usually local consistencies are used which combine neatly with search heuristics. In general, a constraint may either be a binary or non-binary relation and the typical consistency used is either Arc Consistency (for binary) and Generalised Arc Consistency (for non-binary). The natural form for many constraints is as a non-binary constraint (having more than two variables). However, it is known that binary CSPs are also NP-complete. For a long time, most efforts have been placed on non-binary techniques as they were believed to be more efficient.

In this talk, we will revisit the question of binary vs non-binary. We show why the reason behind why binary approaches were believed to be inefficient. Then we show that this belief is mistaken and binary approaches through better encodings and algorithms can outperform existing non-binary techniques. We will discuss improvements to old encodings as well as present new encodings and associated algorithms.

Biography:
Roland Yap is an Associate Professor in the Department of Computer Science, National University of Singapore, Singapore. He received his PhD from Monash University, Australia. He has pioneering work in the development of Constraint Logic Programming languages and the field of Constraint Programming. Together with Christian Bessiere, Jean-Charles Régin, and Yuanlin Zhang, their work on (Generalized) Arc Consistency was awarded the AI Journal Classic Paper Award in 2022. His current research interests include AI, Big Data, Constraints, Operating Systems, Programming Languages, Security and Social Networks.

Enquiries:

Professor Jimmy Lee (jlee@cse.cuhk.edu.hk)

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:

Professor Roland Yap
National University of Singapore (NUS)

Abstract:
Memory errors are often the root cause of security vulnerabilities and exploitation in low level languages such as C/C++. We will first overview the difficulties of dealing with memory errors and existing techniques for detecting and preventing memory errors. This talk will focus on the challenging problem of given a closed source binary, how to harden the binary against memory errors. We introduce RedFat, a binary rewriter which hardens x86_64 binaries against memory errors. The challenge is that without source code, it becomes difficult to have reliable instrumentation and also at the binary level much of the semantics of the original code has dissapeared. To deal with the problem of missing semantics while yet giving more hardening where possible, RedFat uses a complementary error detection methodology. It combines low fat pointers with red zones. RedFat makes minimal assumptions about the binary and is able to operate on stripped and non-PIC binaries. It is also language agnostic and has been evaluated on C / C++ / Fortran benchmarks.

Biography:
Roland Yap is an Associate Professor in the Department of Computer Science, National University of Singapore, Singapore. He received his PhD from Monash University, Australia. He has pioneering work in the development of Constraint Logic Programming languages and the field of Constraint Programming. Together with Christian Bessiere, Jean-Charles Régin, and Yuanlin Zhang, their work on (Generalized) Arc Consistency was awarded the AI Journal Classic Paper Award in 2022. His current research interests include AI, Big Data, Constraints, Operating Systems, Programming Languages, Security and Social Networks.

Enquiries:

Professor Jimmy Lee (jlee@cse.cuhk.edu.hk)

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:

Mr. WANG Zhengrong
Postdoc researcher
University of California, Los Angeles (UCLA)

Abstract:
In the last two decades, computer architects have heavily relied on specialization and scaling up to continue performance and energy efficiency improvement as Moore’s law fading away. The former customizes the system for particular program behaviors (e.g., the neural engine in Apple chips to accelerate machine learning), while the latter evolves into massive multi-core systems (e.g., 96 cores for AMD EPYC 9654 CPU).

This works until we hit the “memory wall” – as modern systems continue to scale up, data movements have become increasingly the bottleneck. Unfortunately, conventional memory systems are extremely inefficient in reducing data movements, suffering from excessive NoC traffic and limited off-chip bandwidth to bring the data to computing cores.

These inefficiencies originate from the essential core-centric view: the memory hierarchy simply reacts to individual requests from the core but is unaware of high-level program behaviors. This makes the hardware oblivious, as they must guess highly irregular and transient memory semantics from the primitive memory abstraction of simple load and store instructions.

This calls for a fundamental redesign of the memory interface to express rich memory semantics, so that the memory system can promptly adjust to evolving program behaviors and efficiently orchestrate data and computation together throughout the entire system. For example, simple computations can be directly associated with memory requests and naturally distributed across the memory hierarchy without bringing all the data to the core. More importantly, the new interface should integrate seamlessly with conventional von Neumann ISAs, enabling end-to-end memory specialization while maintaining generality and transparency. Overall, in this talk, I will discuss our solution to enable general memory specialization for massive multi-core systems that unlock order-of-magnitude speedup/energy efficiency on plain-C programs. Such data-computation orchestration is the key to continuing the performance and energy efficiency scaling.

Biography:
Zhengrong is currently a post-doc researcher at UCLA. His research aims to build general, automatic, and end-to-end near-data acceleration by revolutionizing the orchestration between data and computation throughout the entire system. His open-source work has been accepted by multiple top-tier conferences in computer architecture, including ISCA, MICRO, ASPLOS, HPCA, and awarded Best Paper Runner-Ups as well as IEEE Micro Top Pick Honorable Mentions. He is also one of the maintainers of gem5, a widely used cycle accurate simulator in computer architecture.

Enquiries:

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:

Dr. CHEN Cheng
Postdoctoral Research Fellow
Harvard Medical School

Abstract:
Artificial Intelligence (AI) is catalyzing a paradigm shift in healthcare, promising to reshape the landscape of patient care. At the heart of this transformation is medical imaging, where AI-enabled technologies hold substantial promise for precise and personalized image-based diagnosis and treatment. Despite these advances, these models often underperform at real-world deployment, particularly due to the heterogeneous data distributions and varying modalities in healthcare applications. In this talk, I will introduce our work dedicated to tackling these real-world challenges to advance model generalizability and multimodal robustness. First, I will show how we can leverage generative networks and model adaptation to generalize models under data distribution shifts. Next, I will describe how to achieve robust multimodal learning with missing modalities and with imaging and non-imaging clinical information. Finally, I will present our work that extends to large-scale datasets and more diverse modalities based on foundation model for generalizable multimodal representation learning.

Biography:
Dr. Cheng CHEN is a postdoc research fellow at the Center for Advanced Medical Computing and Analysis, Harvard Medical School. She obtained her Ph.D. degree in Computer Science and Engineering at The Chinese University of Hong Kong in 2021. She received her M.S. and B.S. degrees from Johns Hopkins University and Zhejiang University, respectively. Her research interests lie in the interdisciplinary area of AI and healthcare, with a focus on generalizable, robust, and multimodal medical image analysis. She has over 25 papers published at top AI and medical imaging venues, reaching over 2300 Google Scholar citations with an h-index of 16. Her first-authored papers have been recognized as an ESI “Highly cited paper”, selected as oral presentations, and received travel awards from AAAI and MICCAI. She has been named one of the Global Top 80 Chinese Young Female Scholars in AI and won the MICCAI Federated Brain Tumor Segmentation Challenge. She also serves as Area Chair of MICCAI 2024 and reviewer for multiple top journals and conferences.

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:

Dr. XIAO Liang
Postdoctoral Fellow
NTT Research

Abstract:
Modern cryptography has evolved beyond its initial focus on information privacy and has become deeply integrated into various aspects of computer science. An extraordinary example in this regard is the “love-hate” relationship between Cryptography and Quantum Computing, which stands among the central topics of today’s theoretical computer science (TCS) research. On the one hand, quantum techniques (e.g., Shor’s algorithm) jeopardize the foundational assumptions for Cryptography; on the other hand, the unique features of quantum information (e.g., Heisenberg’s Uncertainty Principle) enable new cryptographic functionalities that were provably impossible in a classical world. A key focus of this talk will be my effort in re-establishing the quantum theory for central cryptography tasks like Secure Multi-Party Computation (MPC) and Zero-Knowledge (ZK) Proofs, underscoring the role of this interdisciplinary field as a fertile ground for both classical and quantum TCS innovations.

As for the “classical” aspect of my research, I will discuss my pursuits in designing concurrently-secure, black-box MPC (and ZK) protocols, addressing the inherent tension between security and efficiency. I will also talk about my passion for leveraging cryptography for system/network security tasks, instantiating my belief in bridging theoretical research with real-world applications.

The presentation will culminate with an outline of a future research agenda, as well as my aspirations to contribute to the CSE department, including the designs of a new course on mathematical tools for TCS, a new course on quantum cryptography, and a semi-annual “Crypto-Plus” seminar in Hong Kong.

Biography:
Xiao LIANG is currently a Postdoctoral Fellow at NTT Research, specializing in Cryptography. Prior to this role, he gained valuable postdoctoral experience at Rice University and Indiana University Bloomington. His expertise encompasses Zero-Knowledge Protocols, Secure Multi-Party Computation, Non-Malleability, and Digital Signature, with a deliberate effort to establish connections with adjacent domains like System/Network Security. A notable highlight of Xiao’s work is the emphasis on the convergence of cryptography and quantum computing, contributing to the dynamic interdisciplinary advancements in this burgeoning field. His research has consistently resulted in publications at esteemed conferences for both cryptography and theoretical computer science in general, such as FOCS, CRYPTO, and ICALP. Xiao Liang holds a Ph.D. in Computer Science and an M.S. in Applied Mathematics, both earned from Stony Brook University, and a B.S. in Economics from Beijing Institute of Technology.

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:

Ms. HUO Yintong
Ph.D. candidate
The Chinese University of Hong Kong

Abstract:

Software systems are serving various aspects of our daily activities, from search engines to communication platforms. Traditional software reliability engineering (SRE) practices, which heavily rely on manual efforts, encounter challenges due to 1) sheer volume, 2) high variety, and 3) rapid evolution of modern software.  My research is centered on enhancing software reliability through automated fault management. In this talk, I will present my work on intelligent SRE, with a focus on utilizing log data for the three major fault management phases: fault prevention, fault removal, and fault tolerance.

The talk starts with the development of an initial investigation on a semantic-aware log analysis framework tailored for identifying system failures during software operation, so that proper fault tolerance mechanisms can be invoked.  The resulting work, SemParser, is inspired by an insightful understanding of the distinctions between human-written language (log events) and machine-generated tokens (variables).  Then, we will discuss “AutoLog” – a novel log sequence simulation framework leveraging program analysis to overcome the limitations of insufficient log data. Unlike existing log data gathered from a limited number of workloads, AutoLog for the first time acquires far more comprehensive and scalable log datasets, paving the way for proactive and practical anomaly detection solutions.  Finally, I will discuss my recent research progress in LLM-powered SRE that demonstrates the possibility of new designs, which integrate LLMs into resolving real-world software engineering challenges.

My past research has showcased the effectiveness of log-driven methods in advancing SRE. To conclude, I will outline my research roadmap with various directions, which extends from intelligent log operations to diverse applications in software development.

Biography:
HUO Yintong is currently a Ph.D. candidate at the Chinese University of Hong Kong, advised by Michael R. Lyu.  Her research area is intelligent Software Engineering (SE), with a focus on software reliability by promoting automated software development, testing, and operations. She has published 12 papers in all top-tier SE conferences, including ICSE, FSE, ASE, ISSTA, and ISSRE.  She is the recipient of an IEEE Open Software Services Award for the LogPAI project (3k+ Stars, 70k+ Downloads).

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:

Dr. LU Songtao
Senior Research Scientist
Mathematics and Theoretical Computer Science Department
IBM Thomas J. Watson Research Center

Abstract:
As machine learning continues to permeate our daily lives with the deployment of large-scale foundational models across diverse domains, we are witnessing an unprecedented era of data collection and exploration through smart devices. This abundance of data holds the potential to bring groundbreaking advancements across numerous industries and disciplines. However, effectively leveraging and safeguarding this wealth of data requires increasingly advanced mathematical techniques.

My research is centered on designing computationally efficient methods backed by theory to drive adversarial, hierarchical, and scalable machine learning models. In this talk, I will delve into my recent work on developing gradient-based optimization algorithms tailored to address game theory-related machine learning problems. Unlike traditional theories focused on convex/concave problems, my focus lies in nonconvex zero-sum games and Stackelberg games, which are essential for tackling nonconvex objective functions prevalent in neural network training. These advancements not only offer theoretical insights into stabilizing iterative numerical algorithms but also provide more generalizable solutions for downstream learning tasks. I will demonstrate the practical significance of these algorithms in addressing real-world machine learning challenges, including adversarial attacks, data hyper-cleaning, and automatic speech recognition. Furthermore, I will highlight the broader impact of the proposed learning framework on emerging problems, such as multilingual multitask learning, reinforcement learning with human feedback, and multi-agent RL.

Biography:
Songtao Lu is a Senior Research Scientist in the Mathematics and Theoretical Computer Science Department at the IBM Thomas J. Watson Research Center in Yorktown Heights, NY, USA. Additionally, he serves as a principal investigator at the MIT-IBM Watson AI Lab in Cambridge, MA, USA. He obtained his Ph.D. from the Department of Electrical and Computer Engineering at Iowa State University in 2018 and held a Post-Doctoral Associate position at the University of Minnesota Twin Cities from 2018 to 2019. His research primarily focuses on foundational machine learning models and algorithms, with applications in trustworthy learning, meta-learning, and distributed learning. He received the Best Paper Runner-Up Award at UAI in 2022, an Outstanding Paper Award from FL-NeurIPS in 2022, an IBM Entrepreneur Award in 2023, and an IBM Outstanding Research Accomplishment Award. Furthermore, he has multiple papers selected for oral/spotlight/long oral presentations at prestigious machine learning conferences, including ICML, NeurIPS, ICLR, AAAI, and UAI.

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:

Mr. LI Shaohua
Ph.D. Candidate
ETH Zurich

Abstract:
In today’s digital landscape, software governs every critical aspect of our lives: communication, transportation, finance, healthcare, and many more. Consequently, software reliability emerges as a critical pillar for the functioning of our society. Yet, the intricate process from source code to executable binary, integral to software development and deployment, presents substantial challenges to both reliability and security.

In this talk, I will discuss my research on advancing the reliability of modern software systems by detecting and eliminating various defects in code, code analysis, and code compilation. At the code level, I will present my research on designing a general methodology for detecting unstable code in software. At the code analysis level, I will discuss the robustness of current detection tools and introduce a novel validation framework for solidifying their robustness. At the code compilation level, I will present a data-driven program generation approach for validation compilers. Finally, I will conclude the talk with my vision and future research on building reliable software systems.

Biography:
Shaohua Li is a final-year Ph.D. candidate in the Department of Computer Science at ETH Zurich, advised by Prof. Zhendong Su (https://people.inf.ethz.ch/suz/). His research interests are compilers, programming languages, and software engineering, with a particular emphasis on their reliability and security. His research has led to the discovery and fixing of hundreds of critical issues in well-established software and systems, such as OpenSSL, Address Sanitizer, GCC, LLVM, etc. His research has received recognition from both industry and academia, including the 2022 Meta Security Research Award, the 2023 ACM Distinguished Paper Award at OOPSLA, and the 2024 ACM Distinguished Artifact Award at ASPLOS.

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:

Mr. CHEN Yu
Postdoc
École Polytechnique Fédérale de Lausanne (EPFL)

Abstract:
As the scale of the problems we want to solve in real life becomes larger, it is difficult to store the whole input or it takes a very long time to read the entire input. In these cases, the classical algorithms, even when they run in linear time and linear space, may no longer be feasible options as the input size is too large. To deal with this situation, we need to design algorithms that use much smaller space or time than the input size. We call this kind of algorithm a sublinear algorithm. My primary research interest is designing sublinear algorithms for combinatorial problems and proving lower bounds to understand the limits of sublinear computation. I also study graph sparsification problems, an important technique for designing sublinear algorithms on graphs. It is usually used as a pre-processing step to speed up algorithms. In this talk, I’ll cover some of my work in sublinear algorithms and graph sparsifications. I’ll give more details on my recent works about vertex sparsifiers.

Biography:
I’m a postdoc in the theory group at EPFL. I obtained my PhD from University of Pennsylvania, where I was advised by Sampath Kannan and Sanjeev Khanna. Before that, I did my undergraduate study at Shanghai Jiao Tong University. I have a broad interest in various aspects of theoretical computer science and mathematics. Currently, I focus on graph algorithms, especially sublinear algorithms on graph and graph sparsification problems. I receive the Morris and Dorothy Rubinoff Award at University of Pennsylvania and the Best Paper award at SODA’19.

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:

Dr. LI Boyi
Postdoctoral Fellow
Berkeley Artificial Intelligence Research Lab (BAIR), UC Berkeley

Abstract:
The machine learning community has embraced specialized models tailored to specific data domains. However, relying solely on a singular data type might constrain flexibility and generality, requiring additional labeled data and hindering user interaction. To address these challenges, my research objective is to build efficient, generalizable, interactive intelligent systems that learn from the perception of the physical world and their interactions with humans to execute diverse and complex tasks to assist people. These systems should support seamless interactions with humans and computers in digital software environments and tangible real-world contexts by aligning representations from vision and language. In this talk, I will elaborate on my approaches across three dimensions: perception, imagination, and action, focusing on large language models, generative models, and robotics. These findings effectively mitigate the limitations of existing model setups that cannot be overcome by simply scaling up, opening avenues for multimodal representations to unify a wide range of signals within a single, comprehensive model.

Biography:
Boyi Li is a postdoctoral scholar at UC Berkeley, advised by Prof. Jitendra Malik and Prof. Trevor Darrell. She is also a researcher at NVIDIA Research. She received her Ph.D. at Cornell University, advised by Prof. Serge Belongie and Prof. Kilian Q. Weinberger. Her research interest is in machine learning and multimodal systems. Her research aims to develop generalizable algorithms and interactive intelligent systems, focusing on large language models, generative models, and robotics, by aligning representations from multimodal data, such as vision and language.

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:

Dr. JIA Zhenge
Postdoctoral Research Associate
Department of Computer Science and Engineering, University of Notre Dame

Abstract:
The rise in chronic diseases, combined with an aging population and a healthcare professional shortage, has driven the extensive use of mobile and implantable devices for effective management of diverse health conditions. Recent years have seen burgeoning interest in exploiting the rapid advancements in artificial intelligence (AI) to augment these devices’ performance.  This development leads to improved patient outcomes, reduced healthcare costs, and enhanced patient autonomy. However, due to individual differences, a one-for-all AI model cannot always provide the best performance and personalized AI is demanded to tailor the model for each individual.  In addition, compounded by the privacy, security, and safety constraints, model personalization must often be done on the medical device with limited hardware resources. In this talk, I will first illustrate the resource sustainability issues in the development of AI/ML for health, and demonstrate our proposed on-device personalized AI techniques that can potentially transform the landspace of mobile and implantable devices. Additionally, I will showcase the world-first TinyML design contest for health organized at ICCAD 2022 and the next-generation Implantable Cardioverter Defibrillator (ICD) design enabled by our research.

Biography:
Zhenge Jia is currently a postdoctoral research associate in the Department of Computer Science and Engineering at the University of Notre Dame. He obtained his Ph.D. degree in Electrical and Computer Engineering at the University of Pittsburgh in 2022.  He received his B.S. degree with honors in Computer Science at Australian National University in 2017.  His research interests include personalized deep learning and on-device AI for health.  He published more than 15 papers in Nature Machine Intelligence, DAC, ICCAD, TCAD and received the Second Place Award in Ph.D. forum at DAC 2023. He has served on the technical program committee of ICCAD and served as the reviewer for IEEE TC, TCAD TNNLS, JETC, etc.

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:
Dr. Suren Jayasuriya
Assistant Professor
Arizona State University

Abstract:
Acoustic imaging leverages sound to form visual products with applications including biomedical ultrasound and sonar. In particular, synthetic aperture sonar (SAS) has been developed to generate high-resolution imagery of both in-air and underwater environments. In this talk, we explore the application of implicit neural representations and neural rendering for SAS imaging and highlight how such techniques can enhance acoustic imaging for both 2D and 3D reconstructions. Specifically, we discuss challenges of neural rendering applied to acoustic imaging especially when handling the phase of reflected acoustic waves that is critical for high spatial resolution in beamforming. We present two recent works on enhanced 2D circular SAS deconvolution in air as well as a general neural rendering framework for 3D volumetric SAS. This research is the starting point for realizing the next generation of acoustic cameras for a variety of applications in air and water environments for the future.

Biography:
Dr. Suren Jayasuriya is an assistant professor at Arizona State University, in the School of Arts, Media and Engineering (AME) and Electrical, Computer and Energy Engineering (ECEE) since 2018. Before this, he was a postdoctoral fellow at the Robotics Institute at Carnegie Mellon University in 2017. Suren received his Ph.D. in ECE at Cornell University in Jan 2017 and graduated from the University of Pittsburgh in 2012 with a B.S. in Mathematics (with departmental honors) and a B.A. in Philosophy. His research interests range from computational cameras, computer vision and graphics, and acoustic imaging/remote sensing. His website can be found at: https://sites.google.com/asu.edu/imaging-lyceum

Enquiries:

Professor GU Jinwei (jwgu@cse.cuhk.edu.hk)

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:
Mr. LUO Hongyin
Postdoctoral associate
MIT Computer Science and Artificial Intelligence Laboratory

Abstract:
Recent language models have achieved strong generalization ability over a vast range of tasks, but also raised concerns about hallucinations, harmful stereotypes, and lack of reliability in reasoning tasks. Our research emphasizes that the core solution to these problems is improving grounding and reasoning abilities of language models. More specifically, we build trustworthy AI systems that (1) follow an explicit grounding-planning-reasoning pipeline for transparency and reliability, and (2) combine autoregressive generation and first-principal reasoning engines. Integrating large language models with knowledge graphs, entailment models, and program interpreter under a program scaffolding instead of natural language, we have made significantly improved the accuracy, transparency, and efficiency of large language models on a wide range of numeric, symbolic, and natural language tasks.

Biography:
Hongyin LUO is a postdoctoral associate at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received a bachelor’s degree from Tsinghua University in 2016 and obtained a Ph.D. degree in computer science in 2022 at MIT EECS. His research focuses on improving the efficiency, transparency, and reasoning ability of language models. His latest research has combined natural language with different formal reasoning engines, including entailment models and program interpreters. He has built self-trained language understanding models outperforming GPT3-175B with 1/500 computation, retrieval-augmented language models that handle noises from search engines, and natural language embedded programs that achieves accurate reasoning without task-specific examples.

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:
Professor HE Xubin
Professor, Department of Computer and Information Sciences
Temple University
Director, Storage Technology and Architecture Research (STAR) Lab
 

Abstract:
Scientific simulations generate large amounts of floating-point data, which are often not very compressible using traditional reduction schemes, such as deduplication or lossless compression. The emergence of lossy floating-point compression holds promise to satisfy the data reduction demand from HPC applications. In this talk, I will share our exploration of lossy compression for HPC scientific data, specifically in three aspects: 1) Understanding and modelling lossy compression schemes on HPC scientific data, and 2) Compression ratio modelling and estimation across error bounds for lossy compression, and 3) Exploring the autoencoder to compress scientific data.

Biography:
Dr. Xubin He is a Professor in the Department of Computer and Information Sciences at Temple University. He is also the Director of the Storage Technology and Architecture Research (STAR) lab. Dr. He received his PhD in Electrical and Computer Engineering from the University of Rhode Island, USA in 2002 and both his MS and BS degrees in Computer Science from Huazhong University of Science and Technology, China, in 1997 and 1995, respectively.  His research interests focus on data storage and I/O systems, including big data, cloud storage, Non-Volatile Storage, and scalability for large storage systems. He has published more than 100 refereed articles in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems (TPDS), Journal of Parallel and Distributed Computing (JPDC), ACM Transactions on Storage, and IEEE Transactions on Dependable and Secure Computing (TDSC), and at various international conferences, including USENIX FAST, USENIX ATC, Eurosys, IEEE/IFIP DSN, IEEE INFOCOM, IEEE IPDPS, MSST, ICPP, MASCOTS, LCN, etc. He is the program co-chair for ccGRID’2024, IPCCC’2017, ICPADS’2016, MSST’2010, general co-chair for IEEE NAS’2009, and general vice co-chair for IPCCC’2018. Dr. He has served as a proposal review panelist for NSF and a committee member for many professional conferences in the field. Dr. He was a recipient of the ORAU Ralph E. Powe Junior Faculty Enhancement Award in 2004, the TTU Chapter Sigma Xi Research Award in 2010 and 2005, TTU ECE Most Outstanding Teaching Faculty Award in 2010, and VCU ECE Outstanding Research Faculty in 2015. He holds one U.S. patent. He is a senior member of the IEEE, a member of the IEEE Computer Society, and USENIX.

Enquiries:

Professor SHAO Zili (shao@cse.cuhk.edu.hk)

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:

Mr. LIU Weiyang
Ph.D. Candidate
Max Planck Institute for Intelligent Systems
University of Cambridge
 

Abstract:
Machine learning (ML) becomes increasingly ubiquitous nowadays, as it enables scalable and accurate decision making in many applications, ranging from autonomous driving to medical diagnosis. Despite its unprecedented success, how to ensure that ML systems are trustworthy and generalize as intended remains a huge challenge. To address this challenge, my research aims to build generalizable ML algorithms through a principled modeling of inductive bias. To this end, I introduce three methods for modeling inductive biases: (1) value-based modeling, (2) data-centric modeling, and (3) structure-guided modeling. While briefly touching upon all three methods, I will focus on my recent efforts in value-based modeling and how it can effectively improve the adaptation of foundation models. Finally, I will conclude by highlighting the critical role of principled inductive bias modeling in unlocking new possibilities in the age of foundation models.

Biography:
Weiyang LIU is currently a final-year PhD student at University of Cambridge and Max Planck Institute for Intelligent Systems, advised by Prof. Adrian Weller and Prof. Bernhard Schölkopf under the Cambridge-Tuebingen Machine Learning Fellowship. His research focuses on the principled modeling of inductive biases to achieve generalizable and reliable machine learning. He has received Baidu Fellowship, Hitachi Fellowship and Qualcomm Innovation Fellowship Finalist. His works have received 2023 IEEE Signal Processing Society Best Paper Award, Best Demo Award at HCOMP 2022 and multiple oral/spotlight presentations at conferences such as ICLR, NeurIPS and CVPR.

Enquiries:

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)

Speaker:

Professor XIE Zhiyao
Assistant Professor, Department of Electronic and Computer Engineering (ECE),
The Hong Kong University of Science and Technology (HKUST)
 

Abstract:
As the integrated circuit (IC) complexity keeps increasing, the chip design cost is skyrocketing. Semiconductor companies are in increasingly greater demand for experienced manpower and stressed with unprecedented turnaround time. Therefore, there is a compelling need for design efficiency improvement through new electronic design automation (EDA) techniques. In this talk, I will present multiple design automation techniques based on machine learning (ML) methods, whose major strength is to explore highly complex correlations based on prior circuit data. These techniques cover various chip-design objectives and design stages, including layout, netlist, register-transfer level (RTL), and micro-architectural level. I will focus on the different challenges in design objective prediction at different stages, and present our customized solutions. In addition, I will share our latest observations in design generation with large language models (LLMs).

Biography:
Zhiyao Xie is an Assistant Professor in the ECE Department at Hong Kong University of Science and Technology. He received his Ph.D. in 2022 from Duke University. His research focuses on electronic design automation (EDA) and machine learning for VLSI design. Zhiyao has received multiple prestigious awards, including the UGC Early Career Award 2023, ACM Outstanding Dissertation Award in EDA 2023, EDAA Outstanding Dissertation Award 2023, MICRO 2021 Best Paper Award, ASP-DAC 2023 Best Paper Award, ACM SIGDA SRF Best Poster Award 2022, etc. During his Ph.D. studies, Zhiyao also worked as a research intern at leading semiconductor companies such as Nvidia, Arm, Cadence, and Synopsys. Now he also serves as the Seminar Chair of IEEE CEDA Hong Kong.

Enquiries:

Professor XU Qiang (qxu@cse.cuhk.edu.hk)

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:
Professor SONG Le
CTO and Chief AI Scientist, BioMap
Professor, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
 

Abstract:
Can we leverage a large amount of unsupervised data to accelerate life science discovery and drug design in industry? In this talk, I will introduce the xTrimo family of large scale pretrained models across a multiscale of biological processes, integrating a huge amount of data from protein sequences, structures, protein-protein interactions and single-cell transcriptomics data. The pretrained models can be used as the foundation to address many predictive problems arising from life science and drug design and achieve SOTA performances.

Biography:
Le Song is the CTO and Chief AI Scientist of BioMap. He directs the research and development of the xTrimo family of foundation models for life sciences, which is the largest model family in the area consisting of more than 100B parameters and achieving SOTA performance in tens of downstream problems. This new technology also led to the first foundation model deal with big pharmaceutical companies (Sanofi) totaling 1B dollar in contract value. Academically, Le Song is full professor in MBZUAI, and was a tenured associate professor of Georgia Tech, and the conference program chair of ICML 2022. He is an expert in machine learning and AI, and has won many best paper awards in leading AI conferences such as NeurIPS, ICML and AISTATS. Recently, his work on using large language models for protein structure predictions has been featured as the cover story in Nature Machine Intelligence.

Enquiries:

Professor LI Yu (liyu@cse.cuhk.edu.hk)

Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:
Dr. REN Haoxing
Director of Design Automation Research
NVIDIA

Abstract:
This talk explores the transformative potential of Generative AI (GenAI) techniques for EDA and Chip Design. First, we introduce the physical design scaling challenge and propose leveraging GenAI to meet this challenge, particularly in core areas of physical design such as gate sizing and buffering. Using GenAI, we have achieved speed-ups that are multiple orders of magnitude faster than existing commercial tools. Additionally, we delve into the challenges associated with training and inference in GenAI models. To facilitate this, we introduce CircuitOps, an open-source tool that efficiently gathers and processes EDA data for the training and inference phases of GenAI models. Secondly, we explore the application of Large Language Models (a key GenAI technology) to improve industrial chip design productivity.  Instead of directly deploying off-the-shelf commercial or open-source LLMs, we trained domain-adapted LLMs (ChipNeMo) with internal design documents and source code. We evaluated ChipNeMo on three selected LLM applications for chip design: an engineering assistant chatbot, EDA script generation, and bug summarization and analysis. Our results show that domain adaptation techniques enable significant LLM performance improvements over general-purpose base models. We also find that domain adaptation is orthogonal to retrieval augmented generation (RAG). On the engineering assistant application, our best model achieved 20% higher performance than GPT-4 with RAG.

Biography:
Haoxing Ren (Mark) serves as the Director of Design Automation Research at NVIDIA, where he focuses on leveraging machine learning and GPU-accelerated tools to enhance chip design quality and productivity. Prior to joining NVIDIA in 2016, he dedicated 15 years to EDA algorithm research and design methodology innovation at IBM Microelectronics and IBM Research. Mark is widely recognized for his contributions to physical design, AI, and GPU acceleration for EDA, achievements that have earned him several prestigious awards, including the IBM Corporate Award and best paper awards at ISPD, DAC, TCAD, and MLCAD. He holds over twenty patents and has co-authored over 100 papers and books including a book on ML for EDA and several book chapters in physical design and logic synthesis. He holds Bachelor’s and Master’s degrees from Shanghai Jiao Tong University and Rensselaer Polytechnic Institute, respectively, and earned his PhD from the University of Texas at Austin. He is a Fellow of the IEEE.

Enquiries: Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:
Mr. XIONG Yuwen
Ph.D. Candidate
Department of Computer Science, University of Toronto

Abstract:

Developing truly autonomous AI systems like self-driving cars has the potential to transform various industries and improve our daily lives. Accomplishing such a system hinges on two crucial components. First, precise perception of the world is necessary; second, modeling and predicting the world’s dynamics is essential to interact with the real world effectively.

In this talk, I will outline my research efforts in perception and world modeling, focusing on developing scalable deep learning algorithms and models beyond controlled environments.

Regarding perception, I will delve into the development of core deep-learning operators that fundamentally augment the capabilities of deep learning models, followed by discussions on how to perform unsupervised pretraining design unified neural network architectures for efficient and effective image segmentation.

As for world modeling, I will show how to learn prior knowledge of the world and then learn to accurately predict world dynamics at the observational level, both in a scalable and unsupervised manner. Lastly, I will discuss my future research plans to advance perception and world modeling further. This involves integrating multi-modal information into the models and systematically incorporating external knowledge, which is crucial for realizing intelligent autonomous AI systems.

Biography:
Yuwen Xiong is a Ph.D. candidate at the University of Toronto, advised by Professor Raquel Urtasun. He was a research scientist in industrial labs (e.g., Uber ATG, Waabi), conducting cutting-edge research on autonomous driving. Before coming to Toronto, he collaborated with Dr Jifeng Dai at Microsoft Research Asia. Yuwen Xiong’s primary interest lies at the intersection of computer vision, robotics, and machine learning. His long-term vision is to build autonomous AI systems that can learn like humans and operate reliably in the real world. To this end, he leverages his knowledge in the full spectrum of autonomy, including perception, prediction, decision-making, and 3D generation, to create systems that are flexible to handle real-world complexities, robust to uncertainties, and generalizable to novel scenes. He is a recipient of the Canada Graduate Scholarships – Doctoral and the Borealis AI Fellowship. More information about him can be found at https://www.cs.toronto.edu/~yuwen/.

Enquiries: WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:
Professor Moi Hoon Yap
Professor of Image & Vision Computing
Manchester Metropolitan University

Abstract:
This seminar covers introduction of research and education in Department of Computing and Mathematics, Manchester Metropolitan University, UK. It provides a pathway for potential research and education collaboration. Prof. Yap will share her research from conceptual foundation and procedures used in the development of medical and computer vision datasets over the past decade, with a timeline to demonstrate progress. It covers data capturing methods, an overview of research in developing private and public datasets, the related computer vision tasks (the facial micro-expressions challenges and the diabetic foot ulcer challenges) and the future direction of the development of her research. As leading institution and investigator in these fields, her aim is to share the technical challenges that we encountered together with good practices in datasets development, and provide motivation for other researchers to participate in data sharing in this domain. Future research involves call for effort in establishing international consortium to form international repository of medical imaging datasets.

For more details, please refer to:
https://dfu-challenge.github.io/ (DFU challenges)
https://megc2023.github.io/ (Facial micro-expressions Challenges)

Biography:
Prof. Moi Hoon Yap is the Research Lead of Department of Computing and Mathematics, Manchester Metropolitan University, UK. Her leadership in both research and education have attracted international students and research collaborations. She is the lead of Human-Centred Computing Group (20 staff members and 12 research scholars) and with expertise in computer vision and deep learning. As the holder of The Royal Society Industry Fellowship (2016-2022), hosted by Image Metrics Ltd, her research is driven by industrial needs. In addition, her research provides new insights and breakthrough for medical image analysis and facial analysis. Moi Hoon has received research funding from The Royal Society, EU Funding, EPSRC, Innovate UK, Cancer Research UK, and industry partners. She serves as the Associate Editor of the Journal of Computers and Programs in Biomedicine and panel member of UK funding bodies. She is leading the technology development for multiple computer vision projects, created novel datasets for reproducible research and conducted international computer vision challenges.

Enquiries: WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:
Professor Daniel Cohen-Or
Professor
School of Computer Science, Tel Aviv University

Abstract:
Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favourite toy? Here we present a simple approach that allows such creative freedom. Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new “words” in the embedding space of a frozen text-to-image model. These “words” can be composed into natural language sentences, guiding personalized creation in an intuitive way. Notably, we find evidence that a single word embedding is sufficient for capturing unique and varied concepts.

Biography:
Daniel Cohen-Or is a professor in the School of Computer Science. He received his B.Sc. cum laude in both mathematics and computer science (1985), and M.Sc. cum laude in computer science (1986) from Ben-Gurion University, and Ph.D. from the Department of Computer Science (1991) at State University of New York at Stony Brook. He was sitting on the editorial board of a number of international journals, and a member of many the program committees of several international conferences. He was the recipient of the Eurographics Outstanding Technical Contributions Award in 2005. In 2013 he received The People’s Republic of China Friendship Award. In 2015 he has been named a Thomson Reuters Highly Cited Researcher? He received the ACM SIGGRAPH Computer Graphics Achievement Award in 2018. In 2019 he won The Kadar Family Award for Outstanding Research. In 2020, he received The Eurographics Distinguished Career Award. His research interests are in computer graphics, in particular, synthesis, processing and modelling techniques.

Enquiries: WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:
Professor Michio Honda
Lecturer (Assistant Professor)
School of Informatics, University of Edinburgh

Abstract:
Datacenter operators and tenants need end-to-end encryption due to workload co-location and untrusted network infrastructure. The status quo is TLS/TCP and QUIC, but those are unfit for datacenters due to unsuitable abstractions and host software overheads. This talk presents our work on a secure datacenter transport protocol (SDP), which allows the use of legacy hardware offload for cryptographic operations available in commodity NICs, while using a new datacenter transport protocol as its basis, such as Homa, allowing operators that currently rely on TLS over TCP to adopt SDP without sacrificing hardware offloading opportunity.

Biography:
Michio Honda is a lecturer (equivalent to assistant professor in US) in the School of Informatics at the University of Edinburgh. His best-known work is identifying TCP extensibility against middlebox interference and building the first TCP/IP network stack for persistent memory. His current research interests include networked storage systems and secure datacenter transport protocols. He is a recipient of IRTF Applied Networking Research Prize (2011), Facebook Research Award (2021) and Google Research Scholar Award (2022).

Enquiries: WONG O-Bong (obong@cse.cuhk.edu.hk)

Speaker:
Professor David Z. Pan
Professor & Silicon Laboratories Endowed Chair
Department of Electrical and Computer Engineering, The University of Texas at Austin

Abstract:

AI for chip design and EDA has received tremendous interests from both academia and industry in recent years. It touches everything that chip designers care about, from power/performance/area (PPA) to cost/yield, turn-around-time, security, among others. It is everywhere, in all levels of design abstractions, testing, verification, DFM, mask synthesis, for digital as well as some aspects of analog/mixed-signal/RF designs as well. It has also been used to tweak the overall design flow and hyper-parameter tuning, but not yet all at once, e.g., generative AI from design specification all the way to layouts, in a correct-by-construction manner. In this talk, I will cover some recent advancement/breakthroughs in AI for chip design/EDA and share my perspectives.

Biography:
Prof. David Pan (Fellow of ACM, IEEE, and SPIE) is a Full Professor and holder of Silicon Laboratories Endowed Chair at the Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin.  His research interests include electronic design automation, synergistic AI and IC co-optimizations, design for manufacturing, hardware security, and design/CAD for analog/mixed-signal and emerging technologies. He has published over 480 refereed journal/conference papers and 9 US patents. He has served in many editorial boards and conference committees, including various leadership roles such as DAC 2024 Technical Program Chair, DAC 2023 Technical Program Co-Chair, ICCAD 2019 General Chair, and ISPD 2008 General Chair. He has received many awards, including 20 Best Paper Awards (from TCAD, DAC, ICCAD, DATE, ASP-DAC, ISPD, HOST, SRC, IBM, etc.), SRC Technical Excellence Award, DAC Top 10 Author Award in Fifth Decade, ASP-DAC Frequently Cited Author Award, NSF CAREER Award, IBM Faculty Award (4 times), and many international CAD contest awards. He has held various advisory, consulting, or visiting positions in academia and industry, such as MIT and Google. He has graduated 52 PhD students and postdocs who have won many awards, including ACM Student Research Competition Grand Finals 1st Place (twice, 2018 and 2021), and Outstanding PhD Dissertation Awards 5 times from ACM/SIGDA and EDAA.

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

Speaker:
Professor Richard T. B. Ma
Associate Professor
School of Computing, National University of Singapore

Abstract:
Due to the long-run and unpredictable nature of stream processing, any statically configured execution of stream jobs fails to process data in a timely and efficient manner. To achieve performance requirements, stream jobs need to be reconfigured dynamically.

In the first part of the talk, we will discuss DRS, a dynamic resource scaling framework for cloud-based stream data analytics systems. DRS overcomes three fundamental challenges: 1) how to model the relationship between the provisioned resources and the application performance, 2) where to best place resources, and 3) how to measure the system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of Jackson open queueing networks and is capable of handling arbitrary operator topologies, possibly with loops, splits, and joins. Extensive experiments with real data show that DRS is capable of detecting sub-optimal resource allocation and making quick and effective resource adjustment.

In the second part of the talk, we present Trisk, a control plane that supports versatile reconfigurations while keeping high efficiency with easy-to-use programming APIs. Trisk enables versatile reconfigurations with usability based on a task-centric abstraction, and encapsulates primitive operations such that reconfigurations can be described by compositing the primitive operations on the abstraction. Trisk adopts a partial pause-and-resume design for efficiency, through which synchronization mechanisms in the native stream systems can further be leveraged. We implement Trisk on Apache Flink and demonstrate its usage and performance under realistic application scenarios. We show that Trisk executes reconfigurations with shorter completion time and comparable latency compared to a state-of-the-art fluid mechanism for state management.

Biography:
Prof. Richard T. B. Ma received the B.Sc. (Hons.) degree in computer science and M.Phil. degree in computer science and engineering from The Chinese University of Hong Kong in 2002 and 2004, respectively, and the Ph.D. degree in electrical engineering from Columbia University in 2010. During his Ph.D. study, he worked as a Research Intern at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, and Telefonica Research, Barcelona, Spain. From 2010–2014, he worked as a Research Scientist at the Advanced Digital Science Center (ADSC), University of Illinois at Urbana–Champaign, Champaign, IL, USA. He is currently an Associate Professor with the School of Computing, National University of Singapore. His current research interests include distributed systems and network economics. He was a recipient of the Best Paper Award Runners-up from the ACM Mobihoc 2020 and a co-recipient of the Best Paper Award from the IEEE IC2E 2013, the IEEE ICNP 2014, and the IEEE Workshop on Smart Data Pricing 2015. He is a Senior Member of ACM and IEEE.

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

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 ?-distinguishable by a one-query quantum algorithm, but O(?k/√n)-indistinguishable by any non-adaptive k-query classical algorithm.

We show that every pair of distributions that is ?-distinguishable by a one-query quantum algorithm is distinguishable with k classical queries and (1) advantage min{?(?√(k/n)), ?(?^2k^2/n)} non-adaptively (i.e., in one round), and (2) advantage ?(?^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:
https://cuhk.zoom.us/j/93836939970
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 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 )