In-Country Events (Malaysia)

​AI for Ageless Ageing   17 March 2021 | 12:30pm – 13:30pm (Singapore Time/ UTC+8) (on-line presentation via Zoom) Miao Chunyan Chair, School of Computer Science and Engineering  Nanyang Technological University https://dr.ntu.edu.sg/cris/rp/rp00084

Abstract: Singapore is faced with a rapidly ageing population. This talk introduces AI innovations at NTU’s LILY Research Centre that helps pave the way towards an ageless society. The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) at Nanyang Technological University (NTU) focuses on the research and design of AI for ageing technologies that help the senior citizens enjoy an active and independent lifestyle.

LILY is one of the world’s first incubators of inter-disciplinary research ideas to promote an active and independent lifestyle for the elderly, and to establish Singapore as a hub in designing and building technology enabled age-friendly communities.

LILY’s disruptive Artificial intelligence (AI) approaches synergizes human intelligence, artificial intelligence and behavior data analytics towards AI powered successful ageing. For examples, LILY’s game AI incorporate long term real-time analysis of user performance for preventive intervention and can be personalized to suit the therapeutic and needs of elderly individuals with different physical and cognitive conditions. LILY’s ageing in place technologies seeks to develop innovative home AI technologies to meet the elderly residents’ physical, socio and emotional wellbeing needs. LILY’s personalized artificial companion helps reduce the problem of loneliness of the empty nest seniors.

As a result, LILY’s award-winning AI wellness games such as Funknee for knee surgery rehabilitation, Pumpkin Garden for mass screening of Parkinson’s disease and aging-in-place platforms have been well received by over 10,000 senior citizens in Singapore.

About the Speaker: Prof Miao Chunyan is a President’s Chair in Computer Science and the Chair of the School of Computer Science and Engineering (SCSE) at NTU Singapore. She received her PhD degree in Computer Engineering from NTU and was an NSERC Postdoctoral Fellow at Simon Fraser University (SFU), Canada. She was a founding faculty member of the Centre for Digital Media established by The University of British Columbia (UBC) and SFU. She was also a Tan Chin Tuan Engineering Fellow at Harvard and MIT.

Prof Miao has received over 20 Best Paper/innovation awards in Artificial intelligence (AI) and real world AI applications for her impactful research in health, ageing, education and smart services. She is a recipient of the prestigious NRF Investigatorship Award 2018. She also holds major research funding including MOH National Innovation Challenge (NIC) on Ageing award 2018 and NRF AI.SG Health Grand Challenge Award 2019.

She is the Founding Director of the Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Singapore’s first centre focusing on AI empowered solutions to population aging challenges. She is also the Founding Director of the Alibaba-NTU Singapore Joint Research Institute (JRI), Alibaba’s first and largest JRI outside China. She is an Editor/Associate Editor of leading international journals including IJIT, IEEE Big Data, IEEE IoT, IEEE Access and IEEE Service Computing and has served as General Chair/TPC member of international conferences such as ACM KDD, IEEE ICA & ICAA.

She serves on various national committees, including the MOH City for All Ages and Health Tech, the IMDA TechSkills Accelerator (TeSA) and is the Chair of the SCS AI Ethics Review Committee. She was awarded a Public Administration Medal (Bronze) from the President of Singapore in 2016.

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​The Talk of Why 9 February 2021 | 12:30pm – 13:30pm (Singapore Time/ UTC+8) (on-line presentation via Zoom) Hanwang Zhang Nanyang Assistant Professor School of Computer Science and Engineering  Nanyang Technological University https://personal.ntu.edu.sg/hanwangzhang/

Abstract: Correlation is not causality. However, this common sense is often surprisingly ignored in most of today’s computer vision systems, including classification, detection, segmentation, and vision-language models, because they are merely trained on correlated sample-label pairs, and the resultant models are nothing short of a likelihood lookup table --- we cannot expect them to generalize to unseen data distribution, not mentioning to more human-level tasks such as modularization, interpretation, and imagination. What is even more regrettable in our community is that we usually blame the poor generalization for insufficient data, and thus some of us may be trapped in the infinite loop: “make a large dataset”---“over-fitted”---“make a larger one’’. Causality is a new science of data generation, model training, and inference. Only by understanding the data causality, we can remove the spurious bias, disentangle the desired model effects, and modularize reusable features that generalize well. We deeply feel that it is a pressing demand for our CV community to adopt causality and use it as a new mind to re-think the hype of feeding big data into gigantic deep models. The goal of this talk is to provide a comprehensive yet accessible overview of the causality in vision research of MReaL at NTU.

About the Speaker: Hanwang Zhang is a Nanyang Assistant Professor at Nanyang Technological University's School of Computer Science and Engineering. His research interests include Computer Vision, Natural Language Processing, Causal Inference, and their combinations. His work has received numerous awards including the IEEE AI 10’s to watch 2020, TMM Prize Paper Award 2020, Alibaba Innovative Research Award 2019, ACM ToMM Best Paper Award 2018, ACM SIGIR Best Paper Honourable Mention Award 2016, and ACM MM Best Student Paper Award 2012. Hanwang and his team work actively in causal inference for connecting vision and language. For example, their scene graph detection benchmark won the IEEE CVPR Best Paper Finalist 2019 and their visual dialog agent won the 1st place in Visual Dialog Challenge 2019 and 2nd place in 2018/2020.

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​AI and Data-Driven Support for Prevention, Intervention, and Cure in Healthcare 13 January 2021 | 12:30pm – 13:30pm (Singapore Time/ UTC+8) (on-line presentation via Zoom) Beng Chin Ooi Distinguished Professor Department of Computer Science, School of Computing  National University of Singapore Director of Smart Systems Institute (SSI@NUS) Director of NUS AI Innovation and Commercialization Centre in Suzhou https://www.comp.nus.edu.sg/~ooibc

Abstract:While AI and data-driven approaches are still evolving, they are likely to surpass current medical practices in the healthcare domain soon. The potential advantages are not only faster and more accurate analysis, but also the democratization of healthcare services. Notwithstanding, there are some common challenges when applying existing approaches onto the healthcare domain, due to the noise and bias of electronic health records (EHR), complex and heterogeneous feature relations, access control and data privacy and etc. In this talk, I shall discuss our design and implementation strategies: solve common challenges, instill domain knowledge, automate knowledge extraction, and enable system-based global optimization. I discuss our rationale on building a general analytics stack instead of solving individual problems, and explain how these challenges are being addressed. Several detailed technologies from both system and algorithm perspectives in our healthcare data management and analytics framework are also described. I shall also discuss our new translational project on reducing 3H (hyperglycemia, hypertension, hyperlipidemia) problems.

About the Speaker: Beng Chin is a Distinguished Professor of Computer Science, NGS faculty member and Director of Smart Systems Institute (SSI@NUS) at the National University of Singapore (NUS), an adjunct Chair Professor at Zhejiang University, China, and the director of NUS AI Innovation and Commercialization Centre at Suzhou, China. He obtained his BSc (1st Class Honors) and PhD from Monash University, Australia, in 1985 and 1989 respectively. He is a fellow of the ACM , IEEE, and Singapore National Academy of Science (SNAS).

Beng Chin's research interests include database systems, distributed and blockchain systems, and large scale analytics, in the aspects of system architectures, performance issues, security, accuracy and correctness. He works closely with the industry (eg. NUHS, Jurong Health, Tan Tok Seng Hospital, Singapore General Hospital, KK Hospital on healthcare analytics and prediebetes prevention), and exploits IT for efficiency in various appplication domains, including healthcare, finance and smart city.

Beng Chin serves as a non-executive and independent director of ComfortDelgro, a transportation company, and a member of Hangzhou Government AI Development Committee (AI TOP 30)He is a co-founder of yzBigData(2012) for Big Data Management and analytics, and Shentilium Technologies(2016) for AI- and data-driven Financial data analytics, MediLot Technologies(2018) for blockchain based healthcare data management and analytics. Beng Chin was the recipient of ACM SIGMOD 2009 Contributions award, a co-winner of the 2011 Singapore President's Science Award, the recipient of 2012 IEEE Computer Society Kanai award, 2013 NUS Outstanding Researcher Award, 2014 IEEE TCDE CSEE Impact Award, 2016 China Computer Federation (CCF) Overseas Outstanding Contributions Award, and 2020 ACM SIGMOD EF Codd Innovation Award. He was a recipient of VLDB'14 and VLDB’19 Best Paper awards.

Beng Chin has served as a PC member for international conferences such as ACM SIGMOD, VLDB, IEEE ICDE, WWW, and SIGKDD, and as Vice PC Chair for ICDE'00,04,06, PC co-Chair for SSD'93 and DASFAA'05, PC Chair for ACM SIGMOD'07, Core DB PC chair for VLDB'08, and PC co-Chair for IEEE ICDE'12, IEEE Big Data'15, BOSS'18, IEEE ICDE'18, Industry track of VLDB'19, BCDL'19 and ACM SoCC'20.

He was an associate editor of VLDB Journal, Springer's Distributed and Parallel Databases and, IEEE Transactions on Knowledge and Data Engineering, Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (TKDE)(2009-2012), and Elsevier's founding co-Editor-in-Chief of Journal of Big Data Research (2013-2015). He is serving as an associate editor of IEEE Transactions on Cloud Computing (TCC) and Communications of ACM (CACM), and the founding editor-in-chief of ACM/IMS Transactions on Data Science (2018 -).

Recording is not available for this talk.

​Deep Learning in Computer Vision 9 December 2020 | 12:30pm – 13:30pm (Singapore Time/ UTC+8) (on-line presentation via Zoom) Chen Change Loy Nanyang Associate Professor School of Computer Science and Engineering Nanyang Technological University (NTU), Singapore NTU Co-Associate Lab Director SenseTime-NTU Joint Research Centre https://personal.ntu.edu.sg/ccloy

Abstract: The rise of deep learning not only leads to a wave of breakthroughs in traditional AI areas, e.g., speech recognition and computer vision, but also opens up many possibilities that are unimaginable before — AI can now play chess games, perform cancer diagnosis, and even drive a car.

In this talk, I will discuss how computer vision and deep learning help us recognize faces and objects, and understand the world. I will also share our on-going efforts in developing new AI technologies with an eye toward how it can be used to solve challenging problems. For example, we will discuss deep networks that can enhance images or hallucinate faces of very low resolution, edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video, and decompose objects in a single image for creative content creation. AI still faces many practical challenges; I will highlight some emerging techniques to address them.

About the Speaker: Chen Change Loy is a Nanyang Associate Professor with the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He is also an Adjunct Associate Professor at the Chinese University of Hong Kong. He received his PhD (2010) in Computer Science from the Queen Mary University of London. Before joining NTU, he served as a Research Assistant Professor at the MMLab of the Chinese University of Hong Kong, from 2013 to 2018. He is the recipient of 2019 Nanyang Associate Professorship (Early Career Award) from Nanyang Technological University. He is recognized by inclusion in the AI 2000 Most Influential Scholar Annual List (AI 2000). His research interests include computer vision and deep learning with a focus on image/video restoration, enhancement, and manipulation. His journal paper on image super-resolution was selected as the `Most Popular Article' by TPAMI in 2016. It remains as one of the top 10 articles to date. He serves as an Associate Editor of the IJCV and TPAMI. He also serves/served as the Area Chair of CVPR 2021, CVPR 2019, BMVC 2019, ECCV 2018, and BMVC 2018. He has co-organized several workshops and challenges at major computer vision conferences. He is a senior member of IEEE.

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​Transforming Digital Transformation via Artificial Intelligence 11 November 2020 | 12:30pm – 13:30pm (Singapore Time/ UTC+8) (on-line presentation via Zoom) Yonggang Wen School of Computer Science and Engineering Nanyang Technological University (NTU), Singapore https://personal.ntu.edu.sg/ygwen/

Abstract: With the Covid-19 pandemic, the need for Digital Transformation has become more urgent than ever. Yet, many companies and organizations are struggling to implement Digital Transformation successfully. In this talk, we will first present a framework to demystify Digital Transformation and illustrate how AI is the main driver behind many successful digital transformation efforts. Following that, we will share how AI empowers digital transformation in both the digital realm (“Business AI”) as well as the physical realm (“Industrial AI”), with two research projects at Nanyang Technological University, Singapore. First, “Business AI” has seen much success and traction in transforming business processes such as sales, marketing and customer services today. In this aspect, we will share a case in point of applying AI techniques to drive sales from video contents, via an AI-powered multi-screen experience. Second, we hope to introduce audience to the wealth of opportunities that remains to be exploited by “Industrial AI”. By employing “Industrial AI” to monitor, optimise and control physical operations and systems, enterprises can be empowered to increase their revenue, lower cost of operations, manage risk and improve their productivity.

About the Speaker: Dr. Yonggang Wen is an IEEE Fellow and the full Professor of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He also serves as the Associate Dean (Research) at College of Engineering, NTU Singapore. Previously he served as the acting Director for Nanyang Technopreneurship Center (NTC) (2017-2019) and the Assistant Chair (Innovation) at the School of Computer Science and Engineering (2016-2018). He received his PhD degree in Electrical Engineering and Computer Science (minor in Western Literature) from Massachusetts Institute of Technology (MIT), Cambridge, USA, in 2008. Dr. Wen has published over 200 papers in top journals and prestigious conferences. His systems research has gained global recognitions. His work in Multi-Screen Cloud Social TV has been featured by global media (more than 1600 news articles from over 29 countries) and received ASEAN ICT Award 2013 (Gold Medal). His work on Cognitive Digital Twin for Data Centre Life-Cycle Management, has won the 2015 Data Centre Dynamics Awards – APAC (the ‘Oscar’ award of data centre industry), 2016 ASEAN ICT Awards (Gold Medal) and 2020 IEEE TCCPS Industrial Technical Excellence Award. He is the sole winner of 2017 Nanyang Award for Innovation and Entrepreneurship, the highest recognition at NTU. He is a co-recipient of multiple Best Paper Awards from top journals, including 2019 IEEE TCSVT and 2015 IEEE Multimedia, and at international conferences, including 2016 IEEE Globecom, 2016 IEEE Infocom MuSIC Workshop, 2015 EAI Chinacom, 2014 IEEE WCSP, 2013 IEEE Globecom and 2012 IEEE EUC. He serves or has served on editorial boards for IEEE Communications Survey & Tutorials, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Wireless Communication, IEEE Transactions on Signal and Information Processing over Networks, IEEE Access Journal and Elsevier Ad Hoc Networks, and was elected as the Chair for IEEE ComSoc Multimedia Communication Technical Committee (2014-2016). His research interests include cloud computing, green data center, big data analytics, multimedia network and mobile computing.

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Speech Information Processing: A Story of Artificial Intelligence 14 October 2020 | 12:30pm – 13:30pm (Singapore Time/ UTC+8) (on-line presentation via Zoom) Haizhou Li Department of Electrical and Computer Engineering National University of Singapore (NUS), Singapore

Abstract: Artificial intelligence is transforming our lives and the way we work. It also provides tremendous opportunities for business to upgrade their products and services. In this talk, we will use speech information processing as an example to discuss artificial intelligence from a historical perspective. Professor Li will also share his 30 years of use-inspired research in the field, that has empowered the industry and benefited the Asian society.

About the Speaker: Haizhou Li is a Professor at the Department of Electrical and Computer Engineering, National University of Singapore. His research interests include speech information processing, natural language processing, and neuromorphic computing. Professor Li has served as the Editor-in-Chief of IEEE/ACM Transactions on Audio, Speech and Language Processing (2015-2018), the President of the International Speech Communication Association (ISCA, 2015-2017), and the President of Asia Pacific Signal and Information Processing Association (APSIPA, 2015-2016). Professor Li was the recipient of National Infocomm Awards 2002, President's Technology Award 2013, and MTI Innovation Activist Gold Award 2015 in Singapore. He was named one of the two Nokia Visiting Professors in 2009 by Nokia Foundation, IEEE Fellow in 2014 for leadership in multilingual, speaker and language recognition, ISCA Fellow in 2018 for contributions to multilingual speech information processing, and Bremen Excellence Chair Professor in 2019.

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​AI for Societal Good 9 September 2020 | 12:30pm – 13:30pm (Singapore Time/ UTC+8) (on-line presentation via Zoom) Prof Yew-Soon Ong President’s Chair Professor in Computer Science  Director, Singtel-NTU Cognitive & Artificial Intelligence Joint Lab Director, Data Science and Artificial Intelligence Research Center Nanyang Technological University (NTU), Singapore Chief Artificial Intelligence Scientist Agency for Science, Technology and Research (A-Star), Singapore https://personal.ntu.edu.sg/asysong/

Abstract: Technological innovation has powered societal progress since the beginning of human history. Countries and cities that succeeded in exploiting technology have surged forward. Our present era is no different. Technology is constantly evolving, in particular, Artificial Intelligence (AI) has progressed dramatically, and will become a big part of our lives, our society, education and our livelihood. For example, AI algorithms can pick out visual ques and nuances of people with special needs while purposed for their social and emotional learning outcomes. They power no-coding digital platforms that enable teachers or anyone who is interested, but with little or no programming background to create AI Apps that uplift education through personalisation, they optimise high value factory production lines, and they work in the background of digital applications and services. In contrast, there continue to exist key scientific challenges that require foremost attention for the concept of AI to more widely trusted, accepted, and integrated within the fabric of society. These challenges are demarcated using five unique Rs (AIR5) that form the five pillars of future AI Research, R1: rationalizability, R2: resilience, R3: reproducibility, R4: realism, and R5: responsibility. Just as air serves as the basic element of biological life, AIR5 marks some of the basic elements of artificial life and bedrock for any AI based digital applications and services for the society.

About the Speaker: Yew-Soon Ong (Fellow of IEEE) received the Ph.D. degree in artificial intelligence in complex design from the University of Southampton, U.K., in 2003. He is President’s Chair Professor in Computer Science at Nanyang Technological University (NTU), and holds the position of Chief Artificial Intelligence Scientist of the Agency for Science, Technology and Research Singapore. At NTU, he serves as Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab, and Director of the Data Science and Artificial Intelligence Research Center. His research interest in artificial and computational intelligence. He is founding Editor-in-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence and AE of IEEE TNNLS, the IEEE Cybernetics, IEEE TEVC, IEEE TAI and others. He has received several IEEE outstanding paper awards and was listed as a Thomson Reuters highly cited researcher and among the World's Most Influential Scientific Minds.

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​Benchmarking Graph Neural Networks 13 August 2020 | 12:30pm – 13:30pm (Singapore Time/ UTC+8) (on-line presentation via Zoom) Xavier Bresson School of Computer Science and Engineering Data Science and AI Center (DSAIR) Nanyang Technological University (NTU), Singapore

Abstract: Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes critical to identify key architectures and validate new ideas that generalize to larger, more complex datasets. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new models in the absence of a standardized benchmark with consistent experimental settings. In this work, we introduce a reproducible GNN benchmarking framework, with the facility for researchers to add new models conveniently for arbitrary datasets. We demonstrate the usefulness of our framework by presenting a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs) for a variety of graph tasks with medium-scale datasets.

About the Speaker: Xavier Bresson is associate professor in Computer Science at NTU, Singapore. His research focuses on graph deep learning, a new framework that combines graph theory and deep learning techniques to tackle complex data domains. In 2016, he received the Singaporean NRF Fellowship of USD 2.5M to develop these new techniques. He was also awarded several research grants in the U.S. and Hong Kong. He co-authored one of the most cited works in this field (https://rb.gy/b0koqs) and he has recently introduced with Yoshua Bengio a benchmark (https://rb.gy/9sghmt) to evaluate the graph neural network architectures. He has organized several workshops and tutorials on graph deep learning such as the IPAM 2021 workshop on "Deep Learning and Combinatorial Optimization" (https://rb.gy/jq31fg), the IPAM 2019 workshop on "Deep Geometric Learning of Big Data" (shorturl.at/deKVW), the IPAM 2018 workshop on "New Deep Learning Techniques" (https://rb.gy/hqhskg), and the NeurIPS 2017 tutorial on "Geometric deep learning on graphs and manifolds" (https://rb.gy/hlwtn5). He is regular invited speaker from universities and companies to present this research topic. He was speaker at ICML 2020 workshop on "Graph Representation Learning and Beyond" (https://rb.gy/iiyy6r) and ICLR 2020 workshop on "Deep Neural Models and Differential Equations" (https://rb.gy/39cgih). He has been teaching graduate courses on graph deep learning at NTU and as guest lecturer at NYU for Yann LeCun's course (https://rb.gy/up6hps).

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