Speaker:

Prof. Stefano Bistarelli
Department of Mathematics and Informatics
University of Perugia
Italy

Abstract:

Nowadays there are more than 1 thousand and an half cryptocurrencies and (public) blockchains with an overall capitalization of more than 300 Billions of USD. The most famous cryptocurrency (and blockchain) is Bitcoin, described in a white-paper written under the pseudonym of “Satoshi Nakamoto”. His invention is an open-source, peer-to-peer digital currency (being electronic, with no physical manifestation). Money transactions do not require a third-party intermediary, such as credit cards issuers. The Bitcoin network is completely decentralised, with all parts of transactions performed by the users of the system. A complete transaction record of every Bitcoin and every Bitcoin user’s encrypted identity is maintained on a public ledger. The seminar will introduce bitcoin and blockchain with a deep view of transactions and some insight on specific application (e-voting).

Speaker’s Bio:

Stefano Bistarelli is Associate Professor of Computer Science at the Department of Mathematics and Informatics at the University of Perugia (Italy) since November 2008. Previously he was Associate Professor at the Department of Sciences at the University “G. d’Annunzio” in Chieti-Pescara since September 2005 and assistant professor in the same department since September 2002. He is also research associate of the Institute of Computer Science and Telematics (IIT) at the CNR (Italian National Research Council) in Pisa since 2002. He obtained his Ph.D. in Computer Science in 2001 that was awarded as the best Theoretical Computer Science and Artificial Intelligence Thesis (awarded respectively by the Italian Chapter of the European Association of Theoretical Computer Science (EATCS) and by the Italian Association for Artificial Intelligence (AI*IA)). In the same year he was also nominated by the IIT-CNR for the Cor Baayen European award and selected as the candidate for Italy for the award. He was PostDocs at University of Padua and at the IIT-CNR in Pisa and visiting researcher at the Chinese University of Hong Kong and at the UCC in Cork. Some collaborations, invited talks or visits involved also others research centres (INRIA, Paris; IC-Park, London; Department of Information Systems and Languages, Barcelona; ILLC, Amsterdam; Computer Science Institute LMU, Monaco; EPFL, Losanna; S.R.I, San Francisco). He has organized and served in the PC of several workshops in the constraints and security fields; he was also chair of the Constraint track at FLAIR and currently of the same track at the SAC ACM symposium. His research interests are related to (soft) constraint programming and solving. He also works on Computer Security and recently on QoS. On these topics he has published more then 100 articles, a book and edited a special issue of a journal on soft constraints. He is also in the editorial board of the electronic version of the Open AI Journal (Bentham Open).

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Dr. Emir Demirovic
School of Computing and Information Systems
University of Melbourne
Australia

Abstract:

We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. Our aim is to develop machine learning algorithms that take into account the underlying combinatorial optimisation problem. While a plethora of sophisticated algorithms and approaches are available in machine learning and optimisation respectively, an established methodology for solving problems which require both machine learning and combinatorial optimisation remains an open question. In this talk, we introduce the problem, discuss its difficulties, and present our progress based on our papers from CPAIOR’19 and IJCAI’19.

Speaker’s Bio:

Dr. Emir Demirovic is an associate lecturer and postdoctoral researcher (research fellow) at the University of Melbourne in Australia. He received his PhD from the Vienna University of Technology (TU Wien) and worked at a production planning and scheduling company MCP for seven months.  Dr. Demirovic’s primary research interest lies in solving complex real-world problems through combinatorial optimisation and combinatorial machine learning, which combines optimisation and machine learning. His work includes both developing general-purpose algorithms and applications. An example of such a problem is to design algorithms to generate high-quality timetables for high schools based on the curriculum, teacher availability, and pedagogical requirements. Another example is to optimise a production plan while only having an estimate of costs rather than precise numbers.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Prof. Chris Willcocks
Durham University
UK

Abstract:

Machine learning scientists often ask the question “What was the distribution from which the dataset was generated from?” and subsequently “How do we learn to transform observations from what we are given, to what is required by the task?”. This seminar highlights successful research where our group took explicit steps to deal with problematic datasets in several different applications, from building robust medical diagnosis systems with a very limited amount of poorly labeled data, to how we hid secret messages in plain sight in tweets without changing the underlying message, how we captured plausible interpolations and successful dockings of proteins despite significant dataset bias, through to recent advances in meta learning to tackle the evolving task distribution in the ongoing anti-counterfeiting arms race.

Speaker’s Bio:

Chris G. Willcocks is a recently appointed Assistant Professor in the Innovative Computing Group at the Department of Computer Science at Durham University in the UK, where he currently teaches the year 3 Machine Learning and year 2 Cyber Security sub-modules. Before 2016, he worked on industrial machine learning projects for P&G, Dyson, Unilever, and the British Government in the areas of Computational Biology, Security, Anti-Counterfeiting and Medical Image Computing. In 2016, he founded the Durham University research spinout company Intogral Limited, where he successfully led research and development commercialisation through to Series A investment, deploying ML models used by large multinationals in diverse markets in Medicine, Pharmaceutics, and Security. Since returning to academia, he has recently published in top journals in Pattern Analysis, Medical Imaging, and Information Security, where his theoretical interests are in Variational Bayesian methods, Riemannian Geometry, Level-set methods, and Meta Learning.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Prof. Sooel Son
Assistant Professor KAIST School of Computing (SoC) and Graduate School of Information Security (GSIS)

Abstract:

Progressive Web App (PWA) is a new generation of Web application designed to provide native app-like browsing experiences even when a browser is offline. PWAs make full use of new HTML5 features which include push notification, cache, and service worker to provide short-latency and rich Web browsing experiences. We conduct the first systematic study of the security and privacy aspects unique to PWAs. We identify security flaws in main browsers as well as design flaws in popular third-party push services, that exacerbate the phishing risk. We introduce a new side-channel attack that infers the victim’s history of visited PWAs. The proposed attack exploits the offline browsing feature of PWAs using a cache. We demonstrate a cryptocurrency mining attack which abuses service workers.

Speaker’s Bio:

Sooel Son is an assistant professor at KAIST School of Computing (SoC) and Graduate School of Information Security (GSIS). He received his Computer Science PhD from The University of Texas at Austin. Before KAIST, he worked on building frameworks that identify invasive Android applications at Google. His research focuses on Web security and privacy problems. He is interested in analyzing Web applications, finding Web vulnerabilities, and implementing new systems to find such vulnerabilities.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Dr. Patrick Groeneveld
Stanford University

Abstract:

In this talk we will analyze how form follows function in physical design. Analyzing recent mobile chips and chips for self-driving cars we can reason about the structure of advanced billion transistor systems. The strength and weaknesses of the hierarchical abstractions will be matched with the sweet spots of the core physical synthesis algorithms. These algorithms are chained in a physical design flow that consists of hundreds of steps, each of which may have unexpected interactions. Trading off multiple conflicting objectives such as area, speed and power is sometimes more an art than a science. The presentation will present the underlying principles that eventually lead to design closure.

Speaker’s Bio:

Before working at Cadence and Synopsys, Patrick Groeneveld was Chief Technologist at Magma Design Automation where he was part of the team that developed a groundbreaking RTL-to-GDS2 synthesis product. Patrick was also a Full Professor of Electrical Engineering at Eindhoven University. He is currently teaching at in the EE department at Stanford University and also serves as finance chair in the Executive Committee of the Design Automation Conference. Patrick received his MSc and PhD degrees from Delft University of Technology in the Netherlands. In his spare time, Patrick enjoys flying airplanes, running, electric vehicles, tinkering and reading useless information.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Dr. Sencun Zhu
Associate Professor
Pennsylvania State University

Abstract:

With the enormous popularity of smartphones, millions of mobile apps are developed to provide rich functionalities for users by accessing certain personal data, leading to great privacy concerns. To address this problem, many approaches have been proposed to detecting privacy disclosures in mobile apps, but they largely fail to automatically determine whether the privacy disclosures are necessary for the functionality of apps.  In this talk, we will introduce LeakDoctor, an analysis system that integrates dynamic response differential analysis with static response taint analysis toautomatically diagnose privacy leaks by judging if a privacy disclosure from an app is necessary for some functionality of the app. Furthermore, we will present the design, implementation, and evaluation of a context-aware real-time mediation system that bridges the semantic gap between GUI foreground interaction and background access, to protect mobile apps from leaking users’ private information.

Speaker’s Bio:

Dr. Sencun Zhu is an associate professor of Department of Computer Science and Engineering at The Pennsylvania State University (PSU). He received the B.S. degree in precision instruments from Tsinghua University, , the M.S. degree in signal processing from the University of Science and Technology of China, Graduate School at Beijing, and the Ph.D. degree in information technology from George Mason University  in 1996, 1999, and 2004, respectively. His research interests include wireless and mobile security, software and network security, fraud detection, and user online safety and privacy. His research has been funded by National Science Foundation, National Security Agency,  and Army Research Office/Lab. He received NSF Career Award in 2007 and a Google Faculty Research Award in 2013. More details of his research can be found in http://www.cse.psu.edu/~sxz16/.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Prof. Karthik Pattabiraman
Associate Professor
ECE Department and CS Department (affiliation)
University of British Columbia (UBC)

Abstract:

Machine learning (ML) has increasingly been adopted in safety-critical systems such as Autonomous vehicles (AVs) and home robotics. In these domains, reliability and safety are important considerations, and hence it is critical to ensure the resilience of ML systems to faults and errors. On the other hand, soft errors are increasing in commodity computer systems due to the effects of technology scaling and manufacturing variations in hardware design. Further, traditional solutions for hardware faults such as Triple-Modular Redundancy are prohibitively expensive in terms of energy consumption, and are hence not practical in this domain. Therefore, there is a compelling need to ensure the resilience of ML applications to soft errors on commodity hardware platforms. In this talk, I will describe two of the projects we worked on in my group at UBC to ensure the  error-resilience of ML applications deployed in the AV domain. I will also talk about some of the challenges in this area, and the work we’re doing to address these challenges.

This is joint work with my students, Nvidia Research, and Los Alamos National Labs.

Speaker’s Bio:

Karthik Pattabiraman received his M.S and PhD. degrees from the University of Illinois at Urbana-Champaign (UIUC) in 2004 and 2009 respectively. After a post-doctoral stint at Microsoft Research (MSR), Karthik joined the University of British Columbia (UBC)  in 2010, where he is now an associate professor of electrical and computer engineering. Karthik’s research interests are in building error-resilient software systems, and in software engineering and security. Karthik has won distinguished paper/runner up awards at the  IEEE/IFIP International Conference  on Dependable Systems and Networks (DSN), 2018, the IEEE International Conference on Software Testing (ICST), 2013, the IEEE/ACM International Conference on Software Engineering (ICSE), 2014, He is a recipient of the distinguished alumni early career award from UIUC’s Computer Science department in 2018,  the NSERC Discovery Accelerator Supplement (DAS) award in 2015, and the 2018 Killam Faculty Research Prize, and 2016 Killam Faculty Research Fellowship at UBC. He also won the William Carter award in 2008 for best PhD thesis in the area of fault-tolerant computing. Karthik is a senior member of the IEEE, and the vice-chair of the IFIP Working Group on  Dependable Computing and Fault-Tolerance (10.4). Find out more about him at:  http://blogs.ubc.ca/karthik

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Dr. Loo Boon Thau
Professor of Computer and Information Science Department
University of Pennsylvania

Abstract:

Declarative networking is a technology that has transformed the way software-defined networking programs are written and deployed. Instead of writing low level code, network operators can write high level specifications that can be verified and compiled into actual implementations. This talk describes 15 years of research in declarative networking, tracing its roots as a domain specific language, to its role in verification, debugging of networks, and commercial use as a declarative network analytics engine. The talk concludes with a peek into the future of declarative networking programming, in the area of examples-guided network synthesis, and infrastructure-aware declarative query processing.

Speaker’s Bio:

Boon Thau Loo is a Professor in the Computer and Information Science (CIS) department at the University of Pennsylvania. He holds a secondary appointment in the Electrical and Systems Engineering (ESE) department. He is also the Associate Dean of the Master’s and Professional Programs, where he oversees all masters programs at the School of Engineering and Applied Science. He is also currently the interim director of the Distributed Systems Laboratory (DSL), an inter-disciplinary systems research lab bringing together researchers in networking, distributed systems, and security. He received his Ph.D. degree in Computer Science from the University of California at Berkeley in 2006. Prior to his Ph.D, he received his M.S. degree from Stanford University in 2000, and his B.S. degree with highest honors from University of California-Berkeley in 1999. His research focuses on distributed data management systems, Internet-scale query processing, and the application of data-centric techniques and formal methods to the design, analysis and implementation of networked systems. He was awarded the 2006 David J. Sakrison Memorial Prize for the most outstanding dissertation research in the Department of EECS at University of California-Berkeley, and the 2007 ACM SIGMOD Dissertation Award. He is a recipient of the NSF CAREER award (2009), the Air Force Office of Scientific Research (AFOSR) Young Investigator Award (2012) and Penn’s Emerging Inventor of the year award (2018). He has published 100+ peer reviewed publications and has supervised twelve Ph.D. dissertations. His graduated Ph.D. students include 3 tenure-track faculty members and winners of 4 dissertation awards.

In addition to his academic work, he actively participates in entrepreneurial activities involving technology transfer. He is the Chief Scientist at Termaxia, a software-defined storage startup based in Philadelphia that he co-founded in 2015. Termaxia offers low-power high-performance software-defined storage solutions targeting the exabyte-scale storage market, with customers in the US, China, and Southeast Asia. Prior to Termaxia, he co-founded Gencore Systems (Netsil) in 2014, a cloud performance analytics company that spun out of his research team at Penn, commercializing his research on the Scalanytics declarative analytics platform. The company was successfully acquired by Nutanix Inc in 2018. He has also published several papers with industry partners (e.g AT&T, HP Labs, Intel, LogicBlox, Microsoft) applying research on real-world systems that result in actual production deployment and patents.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Prof. Daniel COHEN-OR

Abstract:

An interesting question is whether a machine can assist humans in being creative and inspire a user during the creation of 3D models or a shape in general. One possible means to achieve this is through a design gallery which presents a variety of computed suggestive designs from which the user can pick the ones he likes the best. The ensuing challenge is how to come up with intriguing suggestions which inspire creativity, rather than banal suggestions which stall the design process. In my talk I will discuss about the notion of creative modeling, synthesis of inspiring examples, the analysis of a set, and show a number of recent works that uses Deep Neural Networks that baby step towards this end.

Speaker’s Bio:

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 received the 2005 Eurographics Outstanding Technical Contributions Award. 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, ACM SIGGRAPH Computer Graphics Achievement Award in 2018.

In 2013 he received The People’s Republic of China Friendship Award. In 2015 he has been named a Thomson Reuters Highly Cited Researcher. In 2019 he won The Kadar Family Award for Outstanding Research. His research interests are in computer graphics, in particular, synthesis, processing and modeling techniques.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:
Prof. Yunhai Wang

Abstract:

By providing visual representation of data, visualization can help people carry out some tasks more effectively. Given a data set, however, there are have too many different visualization techniques, where each technique has many parameters to be tweaked. We are asking if it is possible to automatically design a visualization that is best suited to pursue a given task on given input data. We have developed a few techniques to achieve this goal for specific data sets including the selection of line chart or scatter plot for time-series data, a framework for aspect ratio selection, color assignment for scatterplots and a new sampling technique for multi-class scatterplots.

Speaker’s Bio:

Yunhai Wang is a professor in School of Computer Science and Technology at Shandong University. His interests include scientific visualization, information visualization and computer graphics, focusing specifically on automated data visualization. He has published more than 30 papers in international journals and conferences, including 14 papers in IEEE VIS/TVCG.  More detail can be found from http://www.yunhaiwang.org.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:
Dr. Bo Han

 
Abstract:

It is challenging to train deep neural networks robustly with noisy labels, as the capacity of deep neural networks is so high that they can totally overfit on these noisy labels. In this talk, I will introduce three orthogonal techniques in robust deep learning with noisy labels, namely data perspective “estimating the noise transition matrix”; training perspective “training on selected samples”; and regularization perspective “conducting scaled stochastic gradient ascent”. First, as an approximation of real-world corruption, noisy labels are corrupted from ground-truth labels by an unknown noise transition matrix. Thus, the accuracy of classifiers can be improved by estimating this matrix. We present a human-assisted approach called “Masking”. Masking conveys human cognition of invalid class transitions, and naturally speculates the structure of the noise transition matrix. Given the structure information, we only learn the noise transition probability to reduce the estimation burden. Second, motivated by the memorization effects of deep networks, which shows networks fit clean instances first and then noisy ones, we present a new paradigm called “Co-teaching” even combating with extremely noisy labels. We train two networks simultaneously. First, in each mini-batch data, each network filters noisy instances based on the memorization effects. Then, it teaches the remaining instances to its peer network for updating the parameters. To tackle the consensus issue in Co-teaching, we propose a robust learning paradigm called “Co-teaching+”, which bridges the “Update by Disagreement” strategy with the original Co-teaching. Third, deep networks inevitably memorize some noisy labels, which will degrade their generalization. We propose a meta algorithm called “Pumpout” to overcome the problem of memorizing noisy labels. By using scaled stochastic gradient ascent, Pumpout actively squeezes out the negative effects of noisy labels from the training model, instead of passively forgetting these effects. We leverage Pumpout to robustify two representative methods: MentorNet and Backward Correction.

Speaker’s Bio:

Bo Han is a postdoc fellow at RIKEN Center for Advanced Intelligence Project (RIKEN-AIP), advised by Prof. Masashi Sugiyama. He will be a visiting postdoc fellow at Montreal Institute for Learning Algorithms (MILA). He pursued his Ph.D. degree in Computer Science at University of Technology Sydney, advised by Prof. Ivor W. Tsang and Prof. Ling Chen. He was a research intern at RIKEN-AIP, working with Prof. Masashi Sugiyama and Dr. Gang Niu. His current research interests lie in machine learning and its real-world applications. His long-term goal is to develop intelligent systems, which can learn from a massive volume of complex (e.g., weakly-supervised, adversarial, and private) data (e.g, single-/multi-label, ranking, graph and demonstration) automatically. He has published 15 journal articles and conference papers, including MLJ, TNNLS, TKDE articles and NeurIPS, ICML, IJCAI, ECML papers. He has served as program committes of NeurIPS, ICML, ICLR, AISTATS, UAI, AAAI, and ACML. He received the UTS Research Publication Award (2017 and 2018).

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:
Prof. Yung-Hsiang Lu

Abstract:

Computer vision relies vast amounts of data and labels for training and validation. Creating datasets and labels require significant efforts.  A team at Purdue University creates datasets using network cameras that can provide real-time visual data. These cameras can continuously stream live views of national parks, zoos, city halls, streets, university campuses, highways, shopping malls.  The stationary cameras (some of them have PTZ, pan-tilt-zoom) have contextual information (such as time and location) about the visual data. By cross-referencing with other sources of data (such as weather and event calendar), it is possible to label the data automatically.  The run-time system allocates and adjusts computing resources as needed. This system is a foundation for many research topics related to analyzing visual data, such as (1) whether today’s technologies are ready analyzing the versatile data, (2) what computing infrastructure is needed to handle the vast amount of real-time data, (3) where are the performance bottlenecks and how hardware accelerators (such as GPU) can improve performance, (4) how can this system automatically produce labels for machine learning.

Speaker’s Bio:

Yung-Hsiang Lu is a professor in the School of Electrical and Computer Engineering and (by courtesy) the Department of Computer Science of Purdue University. He is an ACM distinguished scientist and ACM distinguished speaker. He is a member in the organizing committee of the IEEE Rebooting Computing Initiative. He is the lead organizer of Low-Power Image Recognition Challenge.  Dr. Lu and three Purdue students founded a technology company using video analytics to improve shoppers’ experience in physical stores. This company receives two Small Business Innovation Research (SBIR-1 and SBIR-2) grants from the National Science Foundation.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:
Prof. HO Tsung-Yi

Abstract:

With the increasing clock frequencies, the timing requirement of Rapid Single Flux Quantum (RSFQ) digital circuits is critical for achieving the correct functionality. To meet this requirement, it is necessary to incorporate length-matching constraint into routing problem. However, the solutions of existing routing algorithms are inherently limited by pre-allocated splitters (SPLs), which complicates the subsequent routing stage under length-matching constraint. To tackle this problem, we reallocate SPLs to fully utilize routing resources to cope with length-matching effectively. Furthermore, we propose the first multi-terminal routing algorithm for RSFQ circuits that integrates SPL reallocation into the routing stage. The experimental results on 16-bit Sklansky adder show that our proposed algorithm achieves routing completion while reducing the required area. Finally, design challenges for the RSFQ routing problem will be covered.

Speaker’s Bio:

Tsung-Yi Ho received his Ph.D. in Electrical Engineering from National Taiwan University in 2005. He is a Professor with the Department of Computer Science of National Tsing Hua University, Hsinchu, Taiwan. His research interests include design automation and test for microfluidic biochips and neuromorphic computing systems. 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 principal investigator of the AI Research Program of Ministry of Science and Technology in Taiwan, 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.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:
Prof. Chee-Kong CHUI

Abstract:

The concept of Cyber-physical Systems (CPS) has gained popularity in recent times. Together with Industry 4.0, there is potential for the healthcare industry to leverage its numerous advantages of digitalization and automation. On the other hand, the introduction of robotic instruments in robot-assisted surgery has led to an increase in the complexity of surgical processes. Adopting CPS approaches could potentially improve processes and results of robot-assisted surgery. In this seminar, I will introduce our framework for adapting existing processes for CPS, and explore its applications in robot-assisted surgery and surgical training.

Speaker’s Bio:

Chee-Kong CHUI received the Ph.D. degree from the University of Tokyo, Tokyo, Japan. He is currently an Associate Professor in the Department of Mechanical Engineering, National University of Singapore. He has written and contributed to over 100 articles in journals and conferences. He is inventor/co-inventor of seven US patents, and has several patents pending.

Chui is interested in research and development of engineering systems and science for medicine. He collaborates with clinicians to design and develop new medical devices and robot-assisted systems. His research focus on immersive media involves the provision of haptics and visual cues to assist humans in the training of hand-eye coordination, and furthermore, to augment the human hand-eye coordination in a mixed reality environment and intelligence augmentation. He creates mathematical models and conduct in-vivo and ex-vivo experiments to study tissue biomechanics and tool-tissue interactions. As well, he develop new algorithms in computer vision and graphics. His medical imaging research focuses on measuring and characterizing the material properties of biological tissues.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:
Prof. Edith NGAI

Abstract:

Cities around the world are currently under quick transition towards low carbon environment, high quality of living, and resource efficient economy. Internet of Things (IoT) and big data are powering the smart cities of the future by addressing societal challenges, such as air quality, transportation, and energy efficiency. In this talk, we will present our research project, called Green IoT, which provides artificial intelligence and open data for sustainable development. In this project, we developed an intelligent IoT system for air pollution monitoring in Uppsala, Sweden. We will present the system design, testbed development, and data analytics for urban monitoring and prediction. We will further present how distributed machine learning can provide intelligence and resilience for the IoT systems. Finally, we will highlight our on-going research activities on data analytics and machine learning for decision support in smart cities.

Speaker’s Bio:

Edith Ngai is currently an Associate Professor in Department of Information Technology, Uppsala University, Sweden. She received her PhD from The Chinese University of Hong Kong in 2007. She was a post-doc in Imperial College London, United Kingdom in 2007-2008. Her research interests include Internet-of-Things, mobile cloud computing, network security and privacy, smart city and urban informatics. She was a guest researcher at Ericsson Research Sweden in 2015-2017. Previously, she was a visiting researcher in Simon Fraser University, Tsinghua University, and UCLA. Edith was a VINNMER Fellow (2009) awarded by Swedish Governmental Research Funding Agency VINNOVA. She served as TPC members in various international conferences, including IEEE ICDCS, IEEE ICC, IEEE Infocom, IEEE Globecom, IEEE/ACM IWQoS, and IEEE CloudCom, etc. She was a program chair of ACM womENcourage 2015, TPC co-chair of IEEE SmartCity 2015, IEEE ISSNIP 2015, and ICNC 2018 Network Algorithm and Performance Evaluation Symposium. She is an Associate Editor of IEEE Internet of Things Journal, IEEE Transactions of Industrial Informatics, and IEEE Access. Edith is a Senior Member of ACM and IEEE.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:
Dr. Sinno Jialin PAN
Provost’s Chair Associate Professor
Nanyang Technological University

Abstract:

Recently, supervised-learning algorithms such as deep learning models have made a great impact on our society, but it has become clear that they also have important limitations. First, the learning of supervised models relies heavily on the size and quality of the annotated training data. However, in many real-world applications, there is a serious lack of annotation, making it impossible to obtain high-quality models. Second, models trained by many of today’s supervised-learning algorithms are domain specific, causing them to perform poorly when the domains change. Transfer learning is a promising technique to address the aforementioned limitations of supervised learning. In this talk, I will present what I have done on transfer learning and my current research focuses.

Speaker’s Bio:

Dr Sinno Jialin Pan is a Provost’s Chair Associate Professor with the School of Computer Science and Engineering at Nanyang Technological University (NTU), 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 as a Nanyang Assistant Professor (university’s elite assistant professorship), he was a scientist and Lab Head of text analytics with the Data Analytics Department, Institute for Infocomm Research, Singapore from Nov. 2010 to Nov. 2014. He was named to “AI 10 to Watch” by the IEEE Intelligent Systems magazine in 2018. His research interests include transfer learning, and its applications to wireless-sensor-based data mining, text mining, sentiment analysis, and software engineering.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Prof. Steven HOI
Managing Director of Salesforce Research Asia

Abstract:

Artificial Intelligence has been the key to driving the Fourth Industrial Revolution and transforming everyday experiences not only for consumers but also for business worlds. In this talk, I will give an overview of recent advances in deep learning and AI research with application to build the world’s smartest Customer Relationship Management (CRM) at Salesforce. I will share some example works of state-of-the-art deep learning and AI research, ranging from computer vision to NLP and to voice recognition. Finally, I will share some opportunities for research collaboration between academia and industry and full-time AI research scientists and graduate student internship positions at Salesforce Research Asia.

Speaker’s Bio:

Prof Steven Hoi is currently Managing Director of Salesforce Research Asia at Salesforce in Singapore. Prior to joining Salesforce, he was Associate Professor of School of Information Systems at Singapore Management University and Associate Professor of School of Computer Engineering at Nanyang Technological University, Singapore. He received his Bachelor degree in Computer Science from Tsinghua University, Beijing, China, in 2002, and both his Master and PhD degrees in Computer Science and Engineering from Chinese University of Hong Kong, in 2004 and 2006, respectively. His research interests are machine learning and artificial intelligence (especially deep learning and online learning), and their applications to real-world domains, including computer vision and pattern recognition, multimedia information retrieval, social media, web search and mining, computational finance, healthcare and smart nation, etc. He has published over 200 high-quality referred journal and conference papers. He has contributed extensively in academia including Editor-in-Chief (EiC) of Neurocomputing, Associate Editor for IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI), and area chairs/senior PC/TPC for many reputable international conference in various AI areas.  He was the recipient of Lee Kuan Yew Fellowship for research excellence in 2018 and the Lee Kong Chian Fellowship award in 2016. He was elevated to IEEE Fellow for his significant contributions to machine learning for multimedia information retrieval and scalable data analytics.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Dr. Alan MISHCHENKO

Abstract:

In the machine learning research community, it is generally believed that there is a tension between memorization and generalization. In this work, we examine to what extent this tension exists, by exploring if it is possible to generalize by memorizing alone. Although direct memorization with a lookup table obviously does not generalize, we find that introducing depth in the form of a network of support-limited lookup tables leads to generalization that is significantly above chance and closer to those obtained by standard learning algorithms on several tasks derived from MNIST and CIFAR-10. Furthermore, we demonstrate through a series of empirical results that our approach allows for a smooth tradeoff between memorization and generalization and exhibits some of the most salient characteristics of neural networks: depth improves performance; random data can be memorized and yet there is generalization on real data; and memorizing random data is harder in a certain sense than memorizing real data. The extreme simplicity of the algorithm and potential connections with generalization theory point to several interesting directions for future research.

Speaker’s Bio:

Alan graduated with M.S. from Moscow Institute of Physics and Technology (Moscow, Russia) in 1993 and received his Ph.D. from Glushkov Institute of Cybernetics (Kiev, Ukraine) in 1997. In 2002, Alan joined the EECS Department at University of California, Berkeley, where he is currently a full researcher. His research is in computationally efficient logic synthesis and formal verification.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

Speaker:

Dr. Hong XU
Assistant Professor
Department of Computer Science
City University of Hong Kong

Abstract:

The fast-growing AI and big data workloads already empower much of our everyday life, and is set to define our future lifestyle with jaw-dropping new applications on the horizon. Systems research is critical because the recent success of AI and big data is in large part enabled by datacenter-scale computing infrastructures, which employ an army of machines to harness massive datasets in a continuous fashion.

In this talk, I will present my research that focuses on two system foundations to better support AI and big data. First, we build new data intensive systems that execute the data processing pipelines faster with higher resource utilization. Examples include job schedulers for Spark that provide 60% better makespan, and machine learning systems that compress the embedding vectors by over 100x without performance loss for Tencent’s recommendation models. Second, we build new data center network architectures that deliver more performance and flexibility for data communication. Examples include congestion-aware routing that accelerates flow completion times by 2x at the 99%ile tail. From a broader perspective, these solutions show that significant gains can be achieved for AI and big data systems, by exploiting the unique characteristics of upper-layer workloads and the underlying infrastructure. Fresh opportunities await across the boundaries of systems, networking, and machine learning.

Speaker’s Bio:

Hong Xu is an assistant professor in Department of Computer Science, City University of Hong Kong. His research area is computer networking and systems, particularly machine learning/big data systems and data center networks. He received the B.Eng. degree from The Chinese University of Hong Kong in 2007, and the M.A.Sc. and Ph.D. degrees 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 several best paper awards, including the IEEE ICNP 2015 best paper award. He is a senior member of IEEE and member of ACM.

Enquiries: Ms. Tracy Shum at tel. 3943 8438

Speaker:

Prof. Xu Guandong
Professor
University of Technology Sydney

Abstract:

Embedding learning is a widely used machine learning algorithm and has been successfully applied in various data sources, e.g. matrices, sequences, and graphs, and various application tasks, e.g. NLP, code analysis, and recommendations. The major advantage of embedding learning is to derive concise but representative semantics from original data observations. In this talk, we will introduce our recent research work on knowledge graph embedding for recommendations, and source code embedding for code summarization.

Speaker’s Bio:

Dr. Guandong Xu is a Professor at University of Technology Sydney and CUHK visiting Professor, specialising in Data Science, Data Analytics, Recommender Systems, Web Mining, Text mining and NLP, Social Network Analysis, and Social Media Mining. He has published three monographs, dozens of book chapters and edited conference proceedings, and 200+ journal and conference papers in decent journals and conferences. He leads Data Science and Machine Intelligence Lab at UTS. He is the assistant Editor-in-Chief of World Wide Web Journal and has been serving in editorial board or as guest editors for several international journals. He has received a number of Awards from academia and industry community, such as 2018 Top-10 Australian Analytics Leader Award.

Enquiries: Mr. Cyrus Lee at tel. 3943 8440

Speaker:

Dr. Luo Ruibang
Assistant Professor
Department of Computer Science
The University of Hong Kong

Abstract:

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per- nucleotide error rate of ~5–15%. Meeting this demand, we developed Clairvoyante, a multi- task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53% F1-score on 1KP common variants, and 98.65, 92.57, 87.26% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is avail- able open-source (https://github.com/aquaskyline/Clairvoyante), with modules to train, utilize and visualize the model.

Speaker’s Bio:

Dr. Luo joined HKUCS in Jan 2018. He received his B.E. degree in bio-engineering from the South China University of Technology in 2010 and his Ph.D. degree in computational biology from the University of Hong Kong in 2015. He was a postdoctoral fellow in the Center of Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine. Dr. Luo is a researcher working on bioinformatics software and biological, clinical and pharmaceutical projects. His interdisciplinary research results have been published in peer-reviewed journals such as Nature, Nature Biotechnology, and Bioinformatics. His research covers a diversity of topics in computational biology, from technique-driven research, whose aim is to develop algorithms for two fundamental sequence-analysis problems, ‘genome assembly’ and ‘genome alignment’, to hypothesis-driven investigations, such as studying the genetic background of hundreds of cancer cell lines, where the primary aim is to discover and advance clinical knowledge. His research also includes engineering problems for which the accuracy and efficiency of algorithms are crucial, as well as problems for which innovative modeling and analysis of data are more important.

Enquiries: Mr. Cyrus Lee at tel. 3943 8440

Speaker:

Dr. Koon-Kiu Yan
Research Scientist
St Jude Children’s Research Hospital
USA

Abstract:

The packing of a linear eukaryotic genome within a cell nucleus is dense and highly organized. Recently, proximity-ligation-based assays such as Hi-C have provided insights into such a complex structure. Understanding the role of 3D genome in gene regulation is thus a major area of research. By capturing the interactions between genomic elements, graph-based approaches present a simple but powerful toolbox to understand the 3D genome. In this talk, I will highlight a few projects that utilize graph-theoretical methods to decipher the 3D genome from Hi-C data, including the quantification of reproducibility in Hi-C data, the detection of the so-called topologically associating domains (TADs), and the interplay between spatial proximity and gene expression.

Speaker’s Bio:

Koon-Kiu Yan got his B.Sc. in math/physics and his M.Phil. in physics from the University of Hong Kong. He earned his Ph.D. degree in Physics from Stony Brook University; his dissertation was on the statistical mechanics of complex networks. He received his postdoc training at Yale University. Since then has been working on developing methods for deciphering the organizational principles of biological systems. He is currently a research scientist in St Jude Children’s Research Hospital.

Enquiries: Mr. Cyrus Lee at tel. 3943 8440

Speaker:

Dr. Brian Y TSUI
University of California San Diego

Abstract:

Information in biological cells is encoded by DNA, and certain genes are switched on and transcribed to RNA to direct the functions of cells. Diseases and traits are often driven by a mutation in DNA and aberrant RNA expression of different genes. Thus, understanding the relationships between DNA, RNA and diseases are critical towards the goal of translating the information in our cells to an understandable format and thus accelerate the process of finding cures to diseases. In the past few years, the biomedical field has generated over 900,000 sequencing experiments, where each experiment captures different combinations of DNA and RNA close to its entirety. This talk will focus on the ambitious goal of creating a machine that can utilize over 900,000 high throughput sequencing experiments to crack the DNA code in a systematic fashion using bioinformatics and natural language processing.

Speaker’s Bio:

Dr. Brian Y Tsui received his Ph.D. degree from University of California, San Diego in February 2019 in the Bioinformatics and System Biology program. His research interests involve using Bioinformatics, High- Performance Computing, and AI to improve healthcare and create a better understanding of biology using high-throughput sequencing technology and electronic health record data. Prior to joining the graduate program, he received his Bachelor degree in Computer Science from the University of California, San Diego with Highest Distinction in 2014. During his undergraduate study, he also initiated a project on enabling various algorithms to run faster by accepting compressed input data. Prior to his undergraduate study, he represented Hong Kong at the Intel Science and Engineering Fair in 2009.

Enquiries: Ms. Tracy Shum at tel. 3943 8438

Speaker:

Dr. Qi DOU
Postdoctoral Research Associate
Department of Computing
Imperial College

Abstract:

In modern healthcare, disease diagnosis, assessment and therapy have been significantly depending on the interpretation of medical images, e.g., CT, MRI, Ultrasound, histology images and endoscopy surgical videos. The exploding amount of biomedical image data collected in nowadays clinical centers offer an unprecedented challenge, as well as enormous opportunities, to develop a new-generation of data analytics techniques for improving patient care and even revolutionizing healthcare industry. In the meanwhile, the momentum in cutting-edge AI systems is towards representation learning and pattern recognition via data-driven approaches. In this talk, I will present a series of deep learning methods towards interdisciplinary researches at artificial intelligence for medical image analysis and surgical robotic perception, for improving lesion detection, anatomy tissue semantic parsing, cancer treatment planning, and surgical scene perception. The proposed methods cover a wide range of deep learning topics including design of network architectures, novel learning strategies, multi-task learning, adversarial training, domain adaptation, etc. The challenges, up-to-date progresses and promising future directions of AI-powered healthcare will also be discussed.

Speaker’s Bio:

Dr. Qi DOU is currently a postdoctoral research associate at the Department of Computing at Imperial College London. Before that, she has received her Ph.D. degree in Computer Science and Engineering at The Chinese University of Hong Kong in July 2018. She got her Bachelor’s degree in Biomedical Engineering at Beihang University in China with honor in 2014. Her research interests are in the development of advanced machine learning methods for medical image analysis, with expertise in deep learning. She has won the Best Paper Award of Medical Image Analysis-MICCAI in 2017, the Best Paper Award of Medical Imaging and Augmented Reality in 2016, and MICCAI Young Scientist Award Runner-up in 2016. She has also won the CUHK Postgraduate Research Output Award 2017 and Best Paper Award of CUHK International Doctoral Forum 2016. She was also the winner of Young Scientist Award at the Hong Kong Institution of Science in 2018. She serves as Area Chair of MIDL’19, PC of IJCAI’19, AAAI’19, IJCAI’18, Reviewer of top journals such as IEEE-TMI, IEEE-TBME, IEEE-CYB, Medical Image Analysis, Pattern Recognition, Neurocomputing, etc.  Her current Google Scholar citation has reached 1500+ with h-index 18.

Enquiries: Ms. Tracy Shum at tel. 3943 8438

**** ALL ARE WELCOME ****

Speaker:

Dr. Ram Chillarege
Founder
Chaillarege Inc.

Abstract:

The software engineering process has always been a passionate subject for decades. Today it is Agile. Yesterday was Iterative. And the day before, Waterfall. But what has always been elusive, is the lack of quantitative methods that connect human intellectual work with the artifacts of software. And thus, we have been doomed to passion and philosophy without the anchor of reason and engineering.

Chillarege’s engineering lifetime is defined by his pursuit of semantics and quantitative methods that can reason about the software engineering process, product, and people. Orthogonal Defect Classification (his invention) was break through in this space decades ago. Today, we use learning techniques and product profiling to rapidly gain insight to drive change. Classical qualitative root cause analysis has been transformed into an analyical science that executes 200 times faster at a fraction of the cost. Code re-factoring and verification are tailored with insight from release history and customer usage patterns. These methods have created savings in select Fortune 500 companies running into tens of $M.

This talk will share some concepts in this work, and illustrate the results from industry case studies. The purpose of the talk is to stimulate a new level of thought on how to manage and guide software engineering into the future.

Speaker’s Bio:

Dr. Ram Chillarege received the IEEE Technical Achievement Award for the invention of Orthogonal Defect Classification (ODC). His consulting practice has helped several Fortune 500 companies implement ODC and build their centers of competency. Cumulative savings from ODC runs upwards of several $100 M. At IBM, Ram founded and ran the Center for Software Engineering. He also formulated the strategy for a corporate wide Testing initiative, developing and deploying a new level of technology to reach 50,000 engineers. Over the past decade, he chaired the IEEE Steering committee for Software Reliability Engineering, and raised the profile of the conference and community. He received a PhD from the University of Illinois at Urbana-Champaign in Electrical and Computer and Engineering. He authored over 50 peer reviewed technical articles and serves on several international committees. Recently he was awarded the IEEE Computer Society Meritorious Service Award. He has a varied set of interests and hobbies: The latest is metal welding and HVAC. Over the past couple decades he funded and developed a magnet school program for an under privileged primary school in rural India.

Speaker:

Dr. Hongyang ZHANG
Stanford University

Abstract:

Dealing with large-scale data from modern social and Web systems has been an interesting challenge for algorithm design and machine learning recently. Formalizing such challenges often require better modeling of the underlying data, as well as better modeling of the optimization paradigm in practice. My research aims to provide new algorithms and better models for these settings.

This talk will show a few results. First, we study non-convex methods and their generalization performance (or sample efficiency) for common ML tasks. We consider over-parameterized models such as matrix and tensor factorizations. This is motivated by the curious observation that in practice neural networks are often trained with more parameters than number of observations. We show that the generalization performance crucially depends on the initialization in this setting. Meanwhile, adding parameters helps optimization by avoiding bad local minima. Next, we consider the problem of predictng the missing entries of tensors. We show that understanding the generalization performance can inform the choice of tensor models for this task. Lastly, we revisit the distance sketching problem on large graphs. We provide new insight on this classic problem by formalizing the structures of social network data. Our results help explain the empirical success that has been achieved by recent work.

Speaker’s Bio:

Hongyang Zhang is a Ph.D. candidate studying CS at Stanford University, co-advised by Ashish Goel and Greg Valiant. His research interests lie in machine learning and algorithms, including topics related to neural networks, matrix and tensor factorizations, non-convex optimization, social network analysis and game theory. He is a co-author on the best paper at COLT’18.

Speaker:

Prof. Bei Yu
Assistant Professor
Department of Computer Science and Engineering
The Chinese University of Hong Kong

Abstract:

Deep neural networks (DNNs) have achieved significant success in a variety of real world applications. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various compression and acceleration techniques have been investigated.

In this talk I will introduce state-of-the-art techniques in DNN accelerating techniques from the following two perspectives: 1) how we can accelerate accurate DNN inference; 2) how we can accelerate inaccurate DNN inference.

Speaker’s Bio:

Prof. Bei Yu received his Ph.D degree from University of Texas at Austin in 2014. He is currently an Assistant Professor in the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He has served in the editorial boards of Integration, the VLSI Journal, IET Cyber-Physical Systems: Theory & Applications, and Editor-in-Chief of IEEE TCCPS Newsletter. He has received five Best Paper Awards from Integration, the VLSI Journal in 2018, ISPD 2017, SPIE Advanced Lithography Conference 2016, ICCAD 2013, and ASPDAC 2012, four other Best Paper Award Nominations at ASPDAC 2019, DAC 2014, ASPDAC 2013, ICCAD 2011, and five ICCAD/ISPD contest awards.

Enquiries: Mr. Cyrus Lee at tel. 3943 8440

**** ALL ARE WELCOME ****

Speaker:

Dr. Tsung-Wei HUANG
Research Assistant Professor
CSL and ECE
University of Illinois at Urbana–Champaign

Abstract:

In this talk, I will present a general-purpose programming system to streamline the building of parallel and distributed applications. The system lets users focus on high-level developments rather than difficult concurrency details, such as workload distribution, job execution, and processing communication. We have successfully applied the system to deal with machine learning, AI systems, graph algorithms, and semiconductor designs. Compared to existing frameworks (Hadoop MapReduce, Apache Spark, hand-written MPI, etc), we are able to both reduce the programming complexity and speed up the workload by more than an order of magnitude. The performance scales from a single multicore machine to a cluster of hundreds of nodes.

Speaker’s Bio:

Dr. Huang is a Research Assistant Professor at CSL and ECE in UIUC. His research focuses on building large and complex software systems. He received many programming contest awards in ACM CADathlon, ACM ICPC, ACM TAU and so on. He won the Gold Medal in the ACM/SIGDA Student Research Competition (SRC) and the Second Place in the ACM SRC Grand Final. He also received the Fellowship and the Outstanding Graduate Research Award from the ECE department of UIUC. So far, his research projects have received thousands of downloads and are being used in many industrial and academia projects.

Enquiries: Ms. Tracy Shum at tel. 3943 8438

**** ALL ARE WELCOME ****

Speaker:

Prof. Roberto Pietrantuono
Assistant Professor
Computer Engineering at the University of Naples “Federico II”

Abstract:

In the next decade, software systems are likely to be: deployed in a cyber-physical world, autonomous, self adaptive, driven by artificial intelligence, decentralized and subject to very frequent releases and unanticipated evolution.  It is arguable whether the traditional software engineering paradigm or more agile variants, based on foreseeable operating conditions, can cope with the highly dynamic characteristics of future software systems. This short seminar will highlight the main characteristics and challenges of next generation software systems from the point of view of the software engineering concepts and methodologies needed to engineer them.

Speaker’s Bio:

Roberto Pietrantuono is Assistant Professor at University of Naples “Federico II”, where he teaches software engineering. His research interests are in the areas of software reliability engineering, software testing, and verification of critical software systems. He has co-authored over 60 papers in these research areas. He is a Co-Founder of Critiware s.r.l., a spin-off company working in critical systems engineering, and Senior Member of IEEE.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Stefano Russo
Professor
Computer Engineering at the University of Naples “Federico II”

Abstract:

The term Software Engineering is reported to have been coined in 1968. Nowadays, software has become pervasive, and many software systems are among the most complex systems ever built by the human being. This short seminar will “fly over” the fundamental concepts, the stages, the achievements and some broken promises of the discipline in its first 50 years, trying to figure out if it is mature enough to keep the pace of evolution of next generation software systems.

Speaker’s Bio:

Stefano Russo is Professor of Computer Engineering at the University of Naples “Federico II”, where he leads Dependable Systems and Software Engineering Research Team (www.dessert.unina.it), teaching courses on Software Engineering and on Distributed Systems. He co-authored over 160 papers on software engineering, software dependability, middleware technologies, and mobile computing. He is associate editor of the IEEE Transactions on Services Computing, and Senior Member of IEEE.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Dr. Qi DOU
Postdoctoral Research Associate
Department of Computing Imperial College London

Abstract:

In modern healthcare, disease diagnosis, assessment and therapy have been significantly depending on the interpretation of medical images, e.g., CT, MRI, Ultrasound, histology images and endoscopy surgical videos. The exploding amount of biomedical image data collected in nowadays clinical centers offer an unprecedented challenge, as well as enormous opportunities, to develop a new-generation of data analytics techniques for improving patient care and even revolutionizing healthcare industry. In the meanwhile, the momentum in cutting-edge AI systems is towards representation learning and pattern recognition via data-driven approaches. In this talk, I will present a series of deep learning methods towards interdisciplinary researches at artificial intelligence and medical image analysis, for improving lesion detection, anatomical structure segmentation and quantification, cancer diagnosis and therapy. The proposed methods cover a wide range of deep learning topics including design of network architectures, novel learning strategies, multi-task learning, adversarial training, domain adaptation, etc. The challenges, up-to-date progresses and promising future directions of AI-powered healthcare will also be discussed.

Speaker’s Bio:

Dr. Qi DOU is currently a postdoctoral research associate at the Department of Computing at Imperial College London. Before that, she has received her Ph.D. degree in Computer Science and Engineering at The Chinese University of Hong Kong in July 2018, and was a postdoctoral research fellow in the same lab for three months. She got her Bachelor’s degree in Biomedical Engineering at Beihang University in China with honor in 2014. Her research interests are in the development of advanced machine learning methods for medical image analysis, with expertise in deep learning. She has won the Best Paper Award of Medical Image Analysis-MICCAI in 2017, the Best Paper Award of Medical Imaging and Augmented Reality in 2016, and MICCAI Young Scientist Award Runner-up in 2016. She has also won the CUHK Postgraduate Research Output Award 2017 and Best Paper Award of CUHK International Doctoral Forum 2016. She was also the winner of Young Scientist Award at the Hong Kong Institution of Science in 2018. She has published 30+ papers in top conferences and journals on the topic of deep learning for medical data analysis. She serves as Area Chair of MIDL’19, PC of IJCAI’19, AAAI’19, IJCAI’18, Reviewer of journals such as IEEE-TMI, IEEE-TBME, IEEE-CYB, Medical Image Analysis, Pattern Recognition, Neurocomputing.  Her current Google Scholar citation has reached 1300+ with h-index 17.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Yi CHANG
Dean
School of Artificial Intelligence
Jilin University

Abstract:

Relevance ranking of search engine is a billion-dollar challenge, while there is a disadvantage of backwardness in web search competition. Learning to rank algorithms could effectively improve relevance ranking, yet it is a systematic effort to continuously improve the relevance of a search engine. In this talk, I will introduce the background and the most recent advances in this topic, in particular, three key techniques: ranking functions, semantic matching features and query rewriting. The major part of this talk is based on our ACM KDD’2016 Best Paper Award.

Speaker’s Bio:

Prof. Yi Chang is the Dean of the newly built School of Artificial Intelligence, Jilin University, where is actively looking for tenure-track faculty candidates at different levels. He was a Technical Vice President at Huawei Research America from 2016 to 2018, where he was in charge of knowledge graph, question answering and vertical search technologies within Huawei. Before that, he was a research director at Yahoo Research from 2006 to 2016, and in charge of relevance of Yahoo’s web search engine and vertical search engines. He has broad research interests on information retrieval, data mining and artificial intelligence. He has published more than 100 research papers in premium conferences or journals, and received the Best Paper Award on ACM WSDM’2016, the Best Paper Award on ACM KDD’2016 separately. He has actively involved in multiple academia services: he successfully chaired ACM WSDM’2018, and he will chair SIGIR’2020 in Xi’An, China. He was elected as an ACM Distinguished Scientist, due to his contributions to intelligent algorithms for search engines.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Michael R. Lyu
Professor and Chairman
Department of Computer Science and Engineering
The Chinese University of Hong Kong

Abstract:

“Software is eating the world, but A.I. is going to eat software.” We have already witnessed software engineering shaping every last facet of our 21st century existence.  We currently see the coming of A.I. storms from the horizon.  In this talk I will try to connect A.I. with Software Reliability Engineering (SRE).  On one hand, A.I. techniques, empowered by data-driven machine learning algorithms, can enhance SRE tasks with new paradigms.  On the other hand, SRE techniques are essential to modern A.I. applications.  Regarding the first aspect, we have investigated on the application of A.I. approaches and machine learning techniques to SRE tasks based on three major data sources: code, user review, and log. I will explain the machine learning procedure for these data sources and describe our recently achieved methodologies in performing the relevant tasks.  Regarding the second aspect, I will examine how the conventional SRE techniques, fault avoidance, fault removal, fault tolerance, and fault prediction, can be applied to A.I. software, and present some of our current findings.

Speaker’s Bio:

Michael Rung-Tsong Lyu is a Professor and Chairman of Computer Science and Engineering Department at The Chinese University of Hong Kong. He worked at the Jet Propulsion Laboratory, the University of Iowa, Bellcore, and Bell Laboratories. His research interests include software reliability engineering, distributed systems, fault-tolerant computing, service computing, multimedia information retrieval, and machine learning. He has published 500 refereed journal and conference papers in these areas, which recorded 32000 Google Scholar citations and h-index of 85. He served as an Associate Editor of IEEE Transactions on Reliability, IEEE Transactions on Knowledge and Data Engineering (TKDE), Journal of Information Science and Engineering, and IEEE Transactions on Services Computing. He is currently on the editorial boards of ACM Transactions on Software Engineering and Methodology (TOSEM), IEEE Access, and Software Testing, Verification and Reliability Journal (STVR). He was elected to IEEE Fellow (2004), AAAS Fellow (2007), Croucher Senior Research Fellow (2008), IEEE Reliability Society Engineer of the Year (2010), ACM Fellow (2015), and received the Overseas Outstanding Contribution Award from China Computer Federation in 2018.  Prof. Lyu received his B.Sc. from National Taiwan University, his M.Sc. from University of California, Santa Barbara, and his Ph.D. in Computer Science from University of California, Los Angeles.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Naehyuck CHANG
Professor
School of Electrical Engineering
Korea Advanced Institute of Science and Engineering (KAIST)
Korea

Abstract:

Power consumption became one of the most critical limiting factors in modern electronics systems design from Internet of Things to high-performance computing systems as the device, and circuit techniques are being matured. By contrast, system-level low-power design avoids the inefficient use of devices and circuits by exploiting the application characteristics and user behaviors. As a result, it provides opportunities to further reduce the total system power consumption beyond the limit of the devices and circuits. Recently, it has been shown that low-power electronics design methodologies can also efficiently reduce power consumption of the physical world, that is, power-aware CPS (cyber-physical systems) design extends the scope of low-power design of electronics systems to physical worlds such as vehicle drivetrain, building HVAC (heat, ventilation and air conditioning), power grid (generation, transmission, and distribution), etc.

Introducing several breakthroughs in cross-layer low-power design that we have developed, this talk demonstrates how we extend the scope of system-level low-power design from embedded computing systems to CPS. In general, physical worlds’ power consumption is orders of magnitude higher than that of cyber worlds, and thus low-power CPS indeeds achieves holistic power saving. More specifically, we introduce our targets of low-power design ranging from CPU, memory and interconnects to energy harvesting, energy storage, electric vehicles, and drones. This talk will inspire the current and future low-power CPS with an emphasis on physical worlds within the framework of Design Automation of Things.

Speaker’s Bio:

Naehyuck Chang received the B.S., M.S., and Ph.D. degrees from the Department of Control and Instrumentation, Seoul National University, Korea. He was a professor at the Department of Computer Science and Engineering, Seoul National University, from 1997 to 2014. He served as a Vice Dean of the College of Engineering, Seoul National University, from 2011 to 2013. He has been a professor at the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Korea, since 2014. His current research interests include low-power systems and Design Automation of Things. He was a recipient of the 2014 International Symposium on Low Power Electronics and Design (ISLPED) Best Paper Award, the 2011 SAE Vincent Bendix Automotive Electronics Engineering Award, the 2011 Sinyang Academic Award, the 2009 IEEE SSCS International SoC Design Conference Seoul Chapter Award, and ISLPED Low-Power Design Contest Awards in 2002, 2003, 2004, 2007, 2012, and 2014. He served as the Chair and Past Chair for ACM Special Interest Group on Design Automation (ACM SIGDA.) He was a TPC Co-Chair of the Design Automation Conference 2016, the Asia and South Pacific Design Automation Conference 2015, the International Conference on Computer Design (ICCD) 2014, the International Conference on Hardware/Software Codesign and System Synthesis 2012, and ISLPED 2009 and the General Co-Chair of VLSI-SoC 2015, ICCD 2015 and 2014, and ISLPED 2011. He is the Editor-in-Chief of the ACM Transactions on Design Automation of Electronics Systems (ACM TODAES.) He serves(ed) as an Associate Editor for the IEEE Transactions on Very Large Scale Integration, the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ACM Transactions on Embedded Computing Systems, IEEE Embedded Systems Letters, and IEEE Transactions on Circuits And Systems I. He is currently one of the IEEE Council of Electronics Design Automation (CEDA) Distinguished Lecturers. Naehyuck Chang is a Fellow of ACM (2015) and IEEE (2012.)

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Dr. Hao WANG
Postdoctoral Associate
Computer Science & Artificial Intelligence Lab (CSAIL)
Massachusetts Institute of Technology

Abstract:

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning, and planning require an even higher level of intelligence.  The past few years have seen major advances in many perception tasks using deep learning models. In terms of higher-level inference, however, probabilistic graphical models, with their ability to expressively describe properties of variables and various probabilistic relations among variables, are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, we have been exploring along a research direction, which we call Bayesian deep learning, to tightly integrate deep learning and Bayesian models within a principled probabilistic framework. In this talk, I will present the proposed unified framework and some of our recent work on Bayesian deep learning with various applications including recommendation, social network analysis, healthcare, and representation learning.

Speaker’s Bio:

Dr. Hao Wang is currently a Postdoctoral Associate at the Computer Science & Artificial Intelligence Lab (CSAIL) of MIT, working with Dina Katabi and Tommi Jaakkola. He received his PhD degree from the Hong Kong University of Science and Technology, as the sole recipient of the School of Engineering PhD Research Excellence Award in 2017. He has been a visiting researcher in the Machine Learning Department of Carnegie Mellon University. His research focuses on statistical machine learning, deep learning, and data mining, with broad applications on recommender systems, healthcare, user profiling, social network analysis, text mining, etc. His work on Bayesian deep learning for recommender systems and personalized modeling has inspired hundreds of follow-up works published at top conferences such as AAAI, ICML, IJCAI, KDD, NIPS, SIGIR, and WWW. It has received over 400 citations, becoming the most cited paper at KDD 2015. In 2015, he was awarded the Microsoft Fellowship in Asia and the Baidu Research Fellowship for his innovation on Bayesian deep learning and its applications on data mining and social network analysis.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Jiguang Wang
Assistant Professor
Division of Life Science and Department of Chemical and Biological Engineering
The Hong Kong University of Science and Technology

Abstract:

Recent progression of cancer genome projects has uncovered the mutational landscapes of many cancers, but how cancer cell evolves with and without therapy is still unclear. Scientists believe one major reason of treatment failure is the temporal-spatial dynamics of cancer cells. Actually, cancer cells are constantly evolving, with different groups of cells accumulating distinctive mutations. As the search for more effective cancer diagnostics and therapies continues, remained key questions include a) how to interpret intratumor heterogeneity (ITH); b) how to understand the tumors change over time and how to predict the impact of ITH on tumor progression; and c) how to disentangle the order in which mutations occur. Being able to predict how a tumor will behave based on signs seen early in the course of disease could enable the development of new diagnostics that could better inform treatment planning.

Speaker’s Bio:

Prof Jiguang Wang joined HKUST in 2016, having previously spent five years as a Research Scientist at Columbia University, where he focused on studying cancer genomics and developed a computational method for tracing the evolution of chronic lymphocytic leukemia. In 2015, he was named as an Irving Institute Precision Medicine Fellow. He received his Ph.D. in Applied Mathematics from the Chinese Academy of Sciences. He has substantial contribution to the reconstruction and elucidation of RNA Exosome regulated transcriptome (Nature 2014 and Cell 2015), and the discovery of MGMT fusion in recurrent glioblastoma (Nature Genetics 2016), PIK3CA mutation in multi-focal glioblastoma (Nature Genetics 2017), as well as the METex14 in secondary glioblastoma (Cell 2018).

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Chung-Kuan CHENG
Distinguished Professor at CSE Department
Adjunct Professor at ECE Department
University of California San Diego

Abstract:

I will describe our recent progresses on global placement and routability analysis. For global placement, I will talk about the extension of ePlace in the aspect of the mechanism of the shadow price in primal dual formulation and the meta-parameter tuning. For routability analysis, we encounter complex conditional design rules with shrinking track numbers and increasing pin density. We propose a routing rule management system to identify the tradeoff between the routability and the parameters of design rules. We propose a framework that perform the routability analysis and identify the conflicting rules if the layout deems not routable. The system will allow the designer to optimize pin placement patterns and fine tune the design rules.

Speaker’s Bio:

Chung-Kuan Cheng is with UC San Diego as a Distinguished Professor at CSE Department, and an Adjunct Professor at ECE Department. He has advised 41 Ph.D. graduates and hosted 37 visiting scholars. He is a recipient of the best paper awards, IEEE Trans. on Computer-Aided Design in 1997, and in 2002, the NCR excellence in teaching award, School of Engineering, UCSD in 1991, IEEE Fellow in 2000, IBM Faculty Awards in 2004, 2006, and 2007, the Distinguished Faculty Certificate of Achievement, UJIMA Network, UCSD in 2013, and Cadence Academic Collaboration Award 2016. His research interests include design automation on microelectronic circuits, network optimization, and medical modeling and analysis.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Dr. Tianwei ZHANG
Software engineer
Amazon Web Services

Abstract:

Infrastructure-as-a-Service (IaaS) clouds provide computation and storage services to large enterprises, small businesses and individuals with great elasticity, low cost and high energy efficiency. Cloud customers rent resources in the form of virtual machines (VMs), and deploy their applications and services in the remote datacenters. However, these VMs may face various security threats from different entities. It is important but challenging for cloud providers to create a reliable and secure computation environment for customers.

Current state-of-the-art cloud platforms from the research community and commodity products only provide limited security functionalities, which are far from enough to guarantee the security of VMs. In this talk, I will present my solutions to this challenge in two directions. First I will introduce a general-purpose architectural framework to protect customers’ VMs in IaaS clouds. This framework monitors the security health of VMs in a comprehensive way, and automatically takes actions to mitigate the potential threats that can compromise customers’ desired security properties. I define and verify the necessary hardware-software modules in cloud servers, secure communication protocols, management and security operations to guarantee this trustworthy and unforgeable monitoring service. Then I will present two types of threats: availability threat caused by multi-tenancy resource contention, and confidentiality threat via cache-based side channels. I will introduce two methodologies to defeat these threats with a novel repurposing of existing hardware features. My methodologies can be integrated into my framework, and they together form a secure cloud ecosystem.

Speaker’s Bio:

Dr. Tianwei Zhang is a software engineer at Amazon Web Services. He received his Bachelor’s degree in physics at Peking University, China, in 2011, and the Ph.D degree in Electrical Engineering at Princeton University in 2017, under the supervision of Ruby B. Lee. His research focuses on computer system and architecture security. He is particularly interested in building new frameworks and methodologies to enhance the security of cloud computing environment. He is also interested in verifying and quantifying the designs and mechanisms of security-aware architectures and systems. He has published papers in top-tier architecture and security conferences and journals (ISCA, IEEE micro, IEEE Transactions on Computers, ACSAC, RAID, AsiaCCS) as the first author.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Dr. Liwei WANG
Senior Researcher
Tencent AI Lab at Seattle
USA

Abstract:

Cross-modal learning for images and language is vital to solving many AI applications including image-text retrieval, visual grounding, image captioning and so on. In this talk, I will first introduce our two-branch neural networks for matching images and language in the joint space. I will demonstrate this framework is highly flexible to adapt to various AI tasks.  Second, I will present our recent works of deep generative models that can generate human-like language descriptions. Our approaches can not only generate diverse descriptions conditioned on the image input, but also improve the accuracy of the generation results. Finally, I will introduce my recent efforts in improving traditional AI tasks like captioning and ranking with reinforcement learning.

Speaker’s Bio:

Dr. Liwei Wang is a Senior Researcher in Tencent AI Lab at Seattle, USA. His research focuses on Artificial Intelligence, covering topics in computer vision, natural language processing, deep learning and reinforcement learning. He got his PhD degree in computer science from University of Illinois at Urbana-Champaign in 2018. During his PhD study, he worked with Prof. Svetlana Lazebnik on cross-modal representation learning for general AI tasks and published research works in top AI conferences and journals.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Keith L. Clark
Emeritus Professor
Imperial College London

Abstract:

A TR Program comprises a set of parameterised procedures each of which comprises a sequence of guarded action rules of the form:  G ~> A   Here G is a deductive query to the agent’s dynamic Belief Store (BS) of percept and told facts, ,and A is a tuple of robotic actions or a TR proc. call, including a recursive call. Some or all of the robotic actions may be durative – continuing until stopped. The purpose of some proc call C is to be bring about a state of the robotic environment which can be recognised as having been achieved by a successful evaluation of the guard of the procedure’s C partially instantiate first rule. The guards of later rules represent detectable sub-goal states. When C is called its guards are (optimistically) tested in before/after order until one is found with a guard query that succeeds, typically further instantiating the rule’s action. This rule instance is fired and its action started. It continues whilst the rule’s instantiated guard query continues to be inferable, no earlier guard instance becomes inferable, and the procedure call action remains active. The purpose of the continuing execution of the action is to bring about a detectable super-goal of the rule’s guard, and to eventually achieve the guard of the first rule. This is the procedure’s regression property. In this informal talk a small example use of TR language will be given, and the rule firing and regression semantics precisely but informally defined. However, the use of words such as “eventually” suggests that the semantics of a particular TR procedure may be expressible in Linear Temporal Logic. Preliminary ideas of how this may be done will be given for which constructive criticism is welcome. The long term goal is to be able to use an LTL specification of the behaviour of primitive robotic actions, and of existing TR procedures, to systematically derive the sequence of rules of a new TR procedure given its LTL specification.

Speaker’s Bio:

Keith Clark has Bachelor degrees in both maths and philosophy and a PhD in Computational Logic. He is one of the founders of Logic Programming.  His early research was primarily in the theory and practice of LP. His paper: “Negation as Failure” (1978), giving a semantics to Prolog’s negation operator, has over 3000 citations.

In 1981, inspired by Hoare’s CSP, with a PhD student Steve Gregory, he introduced the concepts of committed choice non-determinism and stream communicating and-parallel sub-proofs into logic programming.   This restriction of the LP concept was then adopted by the Japanese Fifth Generation Project.  This had the goal of building multi-processor knowledge using computers. Unfortunately, the restrictions men it is not a natural tool for building KP applications, and the FGP project failed.  Since 1990 his research emphasis has been on the design, implementation and application of multi-threaded rule based programming languages, with a strong declarative component, for multi-agent and cognitive robotic applications.

He has had visiting positions at Stanford University, UC Santa Cruz, Syracuse University and Uppsala University amongst others. He is currently an Emeritus Professor at Imperial, and an Honorary Professor at University of Queensland and the University of New Soul Wales.  He has consulted for the Japanese Fifth Generation Project, Hewlett Packard, IBM,  Fujitsu and two start-ups. With colleague Frank McCabe, he foundedthe company Logic Programming Associates in 1980. This produced and marketed Prolog systems for micro-computers, offering training and consultancy on their use. The star product was MacProlog,  with primitives for exploiting the Mac GUI for AI applications.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Tiko Kameda
Professor Emeritus
School of Computing Science at Simon Fraser University

Abstract:

Due to many recent disasters such as typhoons, earthquakes, volcanic eruptions, and nuclear accidents, evacuation planning is getting increasing attention. We model evacuation by dynamic flow in networks, where a given number of evacuees is initially located at each vertex. Each edge has a length and a capacity, which is the number of evacuees who can enter it per unit time. We assume the transit time across an edge is proportional to its length. Such a graph can model airplane aisles, rooms and corridors in a building, houses and city streets, cities and inter-city highways, etc. Starting at time 0, all evacuees move towards sinks.

The completion time k-sink problem is to find k sinks in a network such that the evacuation completion time to sinks is minimized. It is somewhat similar to the k-center problem, but here congestion can develop due to the limited edge capacities. In the aggregate time k-sink problem, the objective function is the sum of the evacuation time of every evacuee. Low-degree polynomial time algorithms are known for path, tree and cycle networks, which we will review in this talk.

In the real world, it is likely that the exact values (such as the number of evacuees at the vertices) are unknown. The concept of “regret” was introduced by Kouvelis and Yu in 1997, to model the situations where optimization is required when the exact values are unknown, but are given by upper and lower bounds. A particular instance of the set of evacuee numbers, one for each vertex, is called a “scenario”. The objective of the minmax-regret problem is to find a solution which is as good as any other solution in the worst case, where the actual scenario is the most unfavorable. It can be defined for both completion time and aggregate time objective functions. We review results on the minmax-regret problem for path, tree and cycle networks.

Speaker’s Bio:

Prof. Tiko Kameda is now a Professor Emeritus of School of Computing Science at Simon Fraser University. His current research interest lies mainly in the design and analysis of efficient algorithms for facility location problems, in particular evacuation problems in different networks. In the past, he has worked in the areas of automata theory, system diagnosis, graph problems, combina-torial algorithms, coding theory, database theory, system diagnosis, video-on-demand schemes, distributed computing, etc.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Patrick P. C. Lee
Associate Professor
Department of Computer Science and Engineering
The Chinese University of Hong Kong

Abstract:

Operators heavily rely on network measurement to characterize traffic statistics for effective network management.  However, network measurement remains a missing piece in today’s enterprise and data center networks. On one hand, achieving timely and accurate network measurement is necessary; on the other hand, measurement tasks unavoidably add performance overhead to the packet processing pipeline. In this talk, I will present two novel sketch-based designs that enable space-efficient, high-performance, accurate, and practical network measurement at large scale.  I will present SketchVisor, a framework that maintains high performance of general sketch-based measurement tasks by opportunistically offloading measurement to a fast path. Then I will present SketchLearn, an automated self-learning sketch design that requires limited configuration burdens from operators while maintaining high performance and high accuracy in network measurement.

Speaker’s Bio:

Patrick P. C. Lee is now an Associate Professor of the Department of Computer Science and Engineering at the Chinese University of Hong Kong. He now heads the Applied Distributed Systems Lab and is working very closely with a group of graduate students on different projects in networks and systems.  His research interests are in various applied/systems topics including storage systems, distributed systems and networks, cloud computing, dependability, and security.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Eric LO
Associate Professor
Department of Computer Science and Engineering
The Chinese University of Hong Kong

Abstract:

Blockchain is the technology behind cryptocurrency like Bitcoin. However, many other applications also claim to be disrupted by blockchain, including healthcare, insurance, Internet of Things, etc.  In this talk, I will present the minimal background of blockchain that helps one to judge whether an application really needs blockchain or not.  Furthermore, I will present some research opportunities of blockchain in terms of (i) distributed systems, (ii) security, (iii) database, and (iv) economic.  Lastly, I will present one of my new research projects on blockchain.

Speaker’s Bio:

Eric Lo is an associate professor of Computer Science and Engineering at the Chinese University of Hong Kong (CUHK).  He received his PhD degree from ETH Zurich (Switzerland).  Before returning to Hong Kong, he worked at Google and Microsoft.  His recent research focuses on blockchain.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Xin LI
Professor
Department of Electrical and Computer Engineering at Duke University &
Director
Data Science Research Center (DSRC) at Duke Kunshan University

Abstract:

Big data analytics is an important area that has been continuously growing during the past decade. It has been successfully applied to a variety of commercial applications. This keynote will present novel statistical algorithms and methodologies for several application domains: manufacturing, automobile, etc., where machine learning is playing an extremely important role. Technical challenges, proposed solutions and future directions will be discussed and supported by successful case studies from industrial companies.

Speaker’s Bio:

Xin Li received the Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA in 2005, and the M.S. and B.S. degrees in Electronics Engineering from Fudan University, Shanghai, China in 2001 and 1998, respectively. He is currently a Professor in the Department of Electrical and Computer Engineering at Duke University, Durham, NC, is leading the Institute of Applied Physical Sciences and Engineering (iAPSE), and is the Director of the Data Science Research Center (DSRC) at Duke Kunshan University, Kunshan, Jiangsu, China. In 2005, he co-founded Xigmix Inc. to commercialize his PhD research, and served as the Chief Technical Officer until the company was acquired by Extreme DA in 2007. From 2009 to 2012, he was the Assistant Director for FCRP Focus Research Center for Circuit & System Solutions (C2S2), a national consortium working on next-generation integrated circuit design challenges. His research interests include integrated circuit, signal processing and data analytics. Dr. Xin Li is the Deputy Editor-in-Chief of IEEE TCAD. He was an Associate Editor of IEEE TCAD, IEEE TBME, ACM TODAES, IEEE D&T and IET CPS. He served on the Executive Committee of DAC, ACM SIGDA, IEEE TCCPS, and IEEE TCVLSI. He was the General Chair of ISVLSI, iNIS and FAC, and the Technical Program Chair of CAD/Graphics. He received the NSF CAREER Award in 2012, two IEEE Donald O. Pederson Best Paper Awards in 2013 and 2016, the DAC Best Paper Award in 2010, two ICCAD Best Paper Awards in 2004 and 2011, and the ISIC Best Paper Award in 2014. He also received six Best Paper Nominations from DAC, ICCAD and CICC. He is a Fellow of IEEE.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Xiaopei Liu
Assistant Professor
School of Information Science and Technology
ShanghaiTech University

Abstract:

Fluid behaviors are fascinating, which are of great interests by theorists, practitioners and artists. From the visual perspective, the motion and structure of fluids form the complex and beautiful patterns in nature. Simulating these visual patterns in different scenarios can be very interesting, which has the potential to benefit a lot of studies in different areas. In this talk, I will review our efforts in the past many years on developing new techniques for simulating both classical and quantum fluid flows, with computer graphics techniques to present the simulated results. In particular, for classical fluids, we have been focused on developing kinetic approaches originated from statistical mechanics, which are accurate and efficient in producing a variety of fluid flow phenomena, and we will illustrate how we progress this field. We also believe that such approaches can be appealing as the next-generation simulation technique in the future. For quantum fluids, we more focused on the visual structure, where we have developed new vortex identification methods to uncover the complex structure inside the high-resolution quantum fluid data sets, with real-time visualization and different types of interactions for intuitive exploration.  This is the first visual presentation of large-scale vortex structures in quantum fluids now in the world. Such a series of researches can lead to different kinds of applications, where we are pushing forward to apply our results to scientific study, special effects in movies, new design of unmanned aerial vehicles, urban architecture design, medical diagnosis, as well as training for intelligent robots. Many animation videos for the motion and structure of fluids will be shown during the talk.

Speaker’ Bio:

Prof. Xiaopei Liu is now an assistant professor at School of Information Science and Technology, ShanghaiTech University, affiliated with the center for Virtual Reality and Visual Computing as well as the center for Data Science and Machine Intelligence. He is also the person-in-charge of the Unmanned Aerial Vehicle Computing Lab (UAV-CL) at ShanghaiTech. He obtained his Ph.D. degree on computer science and engineering from The Chinese University of Hong Kong (CUHK), and then worked as a Research Fellow at Nanyang Technological University (NTU) in Singapore, where he started the multi-disciplinary research, and collaborated with School of Mechanical & Aerospace Engineering and Institute of Advanced Studies of NTU for research on fluid simulation and visualization, both on classical and quantum fluids. Most of his publications are top journals and conferences, which cover multiple disciplines, such as ACM TOG, ACM SIGGRAPH Asia, IEEE TVCG, APS PRD, AIP POF, etc. Prof. Xiaopei Liu is now working on physically-based simulation & visualization techniques, with applications to many areas such as fundamental science, visual effects, UAV design, medical diagnosis, as well as robotic learning. He is also conducting research and system-level implementations on low-altitude UAV navigation and its intelligence.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Yu WANG
Tenured Associate Professor
Department of Electronic Engineering
Tsinghua University

Abstract:

Artificial neural networks, which dominate artificial intelligence applications such  as object recognition and speech recognition, are in evolution. To apply neural networks to wider applications, customized hardware are necessary since CPU and GPU are not efficient enough. Numerous architectures are proposed in the past 4 years to boost the energy efficiency of deep learning inference processing, including Tsinghua and Deephi’s effort. In this talk, we will talk about different architectures based on CMOS technologies, including 200GOPS/W FPGA accelerators, about 1-5TOPS/W chips with DDR subsystems, and over 50TOPs/W chips with everything on chip. The possibilities and trends of adopting emerging NVM technology for efficient learning systems, i.e., in-memory-computing, will also be discussed as one of the most promising ways to improve the energy efficiency.

https://nicsefc.ee.tsinghua.edu.cn/projects/neural-network-accelerator/

Speaker’s Bio:

Yu Wang received his B.S. degree in 2002 and Ph.D. degree (with honor) in 2007 from Tsinghua University, Beijing. He is currently a Tenured Associate Professor with the Department of Electronic Engineering, Tsinghua University. His research interests include brain inspired computing, application specific hardware computing, parallel circuit analysis, and power/reliability aware system design methodology. Dr. Wang has authored and coauthored over 200 papers in refereed journals and conferences. He has received Best Paper Award in FPGA 2017, NVMSA17, ISVLSI 2012, and Best Poster Award in HEART 2012 with 9 Best Paper Nominations. He is a recipient of  DAC Under-40 Innovator Award in 2018 and IBM X10 Faculty Award in 2010. He served as TPC chair for ISVLSI 2018, ICFPT 2011 and Finance Chair of ISLPED 2012-2016, and served as program committee member for leading conferences in these areas, including top EDA conferences such as DAC, DATE, ICCAD, ASP-DAC, and top FPGA conferences such as FPGA and FPT. Currently he serves as Co-EIC for SIGDA E-Newsletter, Associate Editor for IEEE Trans on CAS for Video Technology, IEEE Transactions on CAD, and Journal of Circuits, Systems, and Computers. He also serves as guest editor for Integration, the VLSI Journal and IEEE Transactions on Multi-Scale Computing Systems. He is a recipient of NSF China Excellent Young Scholar, and is now serving as ACM distinguished speaker. He is an IEEE/ACM senior member.  He is the co-founder of Deephi Tech (acquired by Xilinx), which is a leading deep learning computing platform proider.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Yun (Eric) LIANG
Associate Professor
School of EECS
Peking University

Abstract:

Heterogeneous systems that couple CPUs with accelerators such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs) have become ubiquitous in the computing world due to their tremendous computing power. However, performance tuning for GPUs and FPGAs is not trivial.  In this talk, I will first present the performance modeling and optimization for FPGAs. I will introduce an accurate performance model for OpenCL workloads on FPGAs and how we accelerate deep learning applications on FPGAs. For the second half of the talk, I will present on-chip storage and machine learning optimization techniques for GPUs.  The proposed techniques leverage on compile-time and run-time techniques to improve the cache performance, register utilization, pipeline utilization and overall performance.

Speaker’s Bio:

Yun (Eric) Liang is an associate professor in School of EECS, Peking University, China. His research focuses on energy-efficient heterogeneous computing, computer architecture, compilation techniques, electronic design automation and embedded system design. He has authored over 70 scientific publications in premier international journals and conferences in this domain. His research has been recognized by best paper award at FCCM 2011 and ICCAD 2017 and best paper nominations at DAC 2017, ASPDAC 2016, DAC 2012, FPT 2011, CODES+ISSS 2008. Prof Liang serves as Associate Editor for ACM Transactions in Embedded Computing Systems (TECS) and serves in the program committees in the premier conferences in the related domain including (HPCA, PACT, CGO, ICCAD, ICS, CC, DATE, CASES, ASPDAC, ICCD).

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Dani LISCHINSKI
Professor
School of Computer Science and Engineering
The Hebrew University of Jerusalem

Abstract:

The real world exhibits an abundance of non-stationary textures. Examples include textures with large scale structures, as well as spatially variant and inhomogeneous textures. While existing example-based texture synthesis methods can cope well with stationary textures, non-stationary textures still pose a considerable challenge, which remains unresolved. In this work, we propose a new approach for example-based non-stationary texture synthesis. Our approach uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar. Once trained, the fully convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks. We demonstrate that this conceptually simple approach is highly effective for capturing large scale structures, as well as other non-stationary attributes of the input exemplar. As a result, it can cope with challenging textures, which, to our knowledge, no other existing method can handle.

Speaker’s Bio:

Prof. Dani Lischinski received his PhD from Cornell University in 1994, and spent two years as a post-doctoral research associate at the University of Washington. Since 1996, he has been a professor at the School of Computer Science and Engineering of the Hebrew University of Jerusalem, Israel. In 2002/3, he spent a sabbatical year at Pixar Animation Studios. In 2012 he received the Eurographics Outstanding Technical Contributions Award, and served as the Technical Papers Chair for SIGGRAPH Asia 2017.

Prof. Lischinski has made numerous significant contributions in several areas of computer graphics, including photorealistic and non-photorealistic rendering, image-based modeling and rendering, texture synthesis, physically-based animation, high dynamic range tone mapping, and interactive manipulation and editing of images and video.

Joint work with: Yang Zhou, Zhen Zhu, Daniel Cohen-Or, and Hui Huang.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Dr. Vincent W. Zheng
Deputy General Manager
WeBank AI Department
Shenzhen
China

Abstract:

Artificial Intelligence has grown from lab research to real-world applications.  At WeBank, we focus on two goals: scaling-up current banking services with AI and Big Data, and exploring new AI based banking models. In this talk, I will give an overview of our recent exploration into the FINTECH space: to build ultra-high dimensional feature space for personalized recommendation, to empower a federation of organizations to build robots into every aspect of banking services to build banking-application-oriented speech recognition systems, face recognition systems, dialog systems and NLP systems. I will describe the concept and give examples of practice cases in WeBank, a first Internet commercial bank in China with 60 Million customers supported with only 1000 people.

Speaker’s Bio:

Dr. Zheng is the Deputy General Manager of WeBank AI Department, and an Adjunct Senior Research Scientist at Advanced Digital Sciences Center (ADSC), Singapore. He received his Ph.D. degree from the Hong Kong University of Science and Technology in 2011. His research interests focus on mining with heterogeneous and structured data. His work on graph feature engineering is highlighted as IJCAI-ECAI 2018 Early Career Spotlight. His work on deep transfer learning receives the Best Paper Award from ICCSE 2018. He is the Associate Editor of Cognitive Computation. He has served as PCs in many leading data mining and artificial intelligence conferences such as KDD, IJCAI, AAAI, WWW, WSDM. He also co-organized the 1st IEEE ICDM Data Mining Contest in 2007, the International Workshop on Localization Technology and Location-based Service at IEEE CPSCom 2013, and the 1st International Workshop on Large Scale Graph Representation Learning and Applications at ICDM 2018.  He has published over 60 papers in the refereed conferences, journals and book chapters. He holds multiple patents and technical disclosures. He is a member of AAAI and ACM.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Shixia LIU
Associate Professor
Tsinghua University

Abstract:

Machine learning has demonstrated to be highly successful at solving many real-world applications ranging from information retrieval, data mining, and speech recognition, to computer graphics, visualization, and human-computer interaction. However, most users often treat the machine learning model as a “black box” because of its incomprehensible functions and unclear working mechanism. Without a clear understanding of how and why the model works, the development of high-performance models typically relies on a time-consuming trial-and-error procedure. This talk presents the major challenges of explainable machine learning and exemplifies the solutions with several visual analytics techniques and examples, including model understanding and diagnosis.

Speaker’s Bio:

Shixia Liu is a tenured associate professor at Tsinghua University. Her research interests include explainable machine learning, visual text analytics, visual social analytics, and text mining. Before joining Tsinghua University, she worked as a lead researcher at Microsoft Research Asia and a research staff member at IBM China Research Lab. Shixia is one of the Papers Co-Chairs of IEEE VAST 2016 and 2017. She is an associate of IEEE Transactions on Visualization and Computer Graphics and IEEE Transactions on Big data. She is on the editorial board of Information Visualization and Journal of visualization. She was the guest editor of ACM Transactions on Intelligent Systems and Technology and Tsinghua Science and Technology. She was the program co-chair of PacifcVis 2014 and VINCI 2012. Shixia was in the Steering Committee of VINCI 2013. She is on the organizing committee of IEEE VIS 2015 and 2014. She is/was in the Program Committee for CHI 2019, 2018, InfoVis 2015, 2014, VAST 2018, 2015, 2014, KDD 2015, 2014, 2013, ACM Multimedia 2009, SDM 2008, ACM IUI 2011, 2009, PacificVis 2008, 2009, 2010, 2011, PAKDD 2013, VISAPP 2012, 2011, VINCI 2011.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Dr. Jiamou LIU
Lecturer
Department of Computer Science
The University of Auckland

Abstract:

Interpersonal ties are pivotal to individual efficacy, status and performance in an agent society. The establishment of interpersonal ties thus constitutes a pivotal problem in the structural analysis of social networks. Here we explore three important and interrelated themes in social network theory: the center/periphery partition of the network; network dynamics; and social integration of newcomers. We tackle the question: How would a newcomer harness information brokerage to integrate into a network going from periphery to center? We formulate and investigate the problem for both static and dynamic networks. The talk will survey several recent results obtained towards answering these problems.

Note: The talk will start with a brief introduction to the University of Auckland and its Computer Science Department.

Speaker’s Bio:

Jiamou Liu is a lecturer in the Department of Computer Science at the University of Auckland, New Zealand. He was a senior lecturer at the Auckland University of Technology, New Zealand between 2011 and 2015. He obtained PhD degree in theoretical computer science from the University of Auckland and worked as a PostDoc researcher at the Department of Computer Science, Leipzig University, Germany from 2009 to 2010. His current research interests include social network analysis and multiagent systems.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Prof. Yufei TAO
Professor
Department of Computer Science and Engineering
The Chinese University of Hong Kong

Abstract:

Given two sets of entities X and Y, entity matching aims to decide whether x and y represent the same entity for each x in R and y in Y. As the last resort, human experts can be called upon to inspect every (x, y), but this is expensive because the correct verdict could not be determined without investigation efforts dedicated specifically to the two entities x and y involved. It is therefore important to design an algorithm that asks humans to look at only some pairs, and renders the verdicts on the other pairs automatically with good accuracy.

We describe algorithms with non-trivial guarantees on this problem with an active monotone classification approach. We also prove lower bounds that establish the asymptotic optimality of our solutions for a wide range of parameters.

Speaker’s Bio:

Yufei Tao is a Professor in the Department of Computer Science and Engineering, Chinese University of Hong Kong. He is an ACM distinguished scientist. He received the best-paper award at PODS 2018, and the best-paper awards at SIGMOD in 2013 and 2015, respectively. He was a PC co-chair of ICDE 2014, and will serve as the PC chair of PODS 2020. He served as an associate editor for ACM TODS from 2008 to 2015, and for IEEE TKDE from 2012 to 2014.

Enquiries: Ms. Crystal Tam at tel. 3943 8439

**** ALL ARE WELCOME ****

Speaker:

Mr. Wei Guo
Founding Partner UpHonest Capital

Abstract:

“Silicon Valley is an almost $3 trillion neighborhood thanks to companies like Apple, Google, Facebook, Tesla and so on. But it wasn’t always this way.”—- Business Insider

Silicon Valley became a synonymous for technology and innovation. It started from a small area and grew to become the “center of the tech world” (Business Insider) in 100 years. This area is crowded with startups, venture capitalists, talented engineers, and of course some of the biggest technology companies in the world.

This presentation aims to provide a general background about Silicon Valley, and answer questions such as: how did this area become Silicon Valley, the key groups, people, companies, startups that involved in its transformation; what is currently happening in Silicon Valley in terms of investments and entrepreneurship; implications for younger generations – how can young people/students start their careers in innovation, technology, entrepreneurship and so on.

Speaker’s Bio:

Wei is an active early-stage investor in Silicon Valley. He has invested in over 300 Silicon Valley startups. He was one of the few to be cited as a “China Whisperer” by the Washington Post, and was one of the first Chinese investors to be featured on The Information. In 2016, Wei was listed as one of the Global Top 20 Best Cross-Border Early-Stage Investors by ChinaVenture as well as honored as the Most Active Angel Investor by ZAngels. In 2017, In 2017, UpHonest Capital was listed as Top 50 Chinese Best Early-Stage Investment Companies, Wei was ranked as the #1 angel investor in Artificial Intelligence by VentureBeats and was recognized under both the Venture Capital and Big Money Startups categories of the Forbes 30 Under 30. In 2018, Wei was ranked as the #3 top 50 angel investors by Forbes, and was recognized as 40 under 40 by Chuangyebang.

Enquiries: Ms. Crystal Tam at tel. 3943 8439