Professor Wenfei Fan is the Chair of Web Data Management at the University of Edinburgh, UK, and the Chief Scientist of Shenzhen Institute of Computing Science, China. He is a Fellow of the Royal Society (FRS), a Fellow of the Royal Society of Edinburgh (FRSE), a Member of the Academy of Europe (MAE), an ACM Fellow (FACM), and a Foreign Member of Chinese Academy of Sciences. He received his PhD from the University of Pennsylvania (USA), and his MSc and BSc from Peking University (China). He is a recipient of Royal Society Wolfson Research Merit Award in 2018, ERC Advanced Fellowship in 2015, the Roger Needham Award in 2008 (UK), Yangtze River Scholar in 2007 (China), the Outstanding Overseas Young Scholar Award in 2003 (China), the Career Award in 2001 (USA), and several Test-of-Time and Best Paper Awards (Alberto O. Mendelzon Test-of-Time Award of ACM PODS 2015 and 2010, Best Paper Awards for SIGMOD 2017, VLDB 2010, ICDE 2007 and Computer Networks 2002). His current research interests include database theory and systems, in particular big data, data quality, data sharing, parallel computation, query languages and recommender systems.

Big data is typically characterized with 4V's: Volume, Velocity, Veracity and Value. When it comes to big graphs, these challenges become even more staggering. A number of questions remain open. What parallel computation model should we adopt to scale with big graphs? Does parallel processing suffice to cope with the volume of big graphs? Is there a systematic method for developing effective incremental algorithms in response to frequent updates? Is it possible to uniformly query relational databases and graphs in, e.g., SQL? How can we unify logic rules and machine learning models, to improve the quality of graph-structured data? This talk aims to incite interest in these topics, and raises as many questions as it answers.

Yuanyuan Yang received the BEng and MS degrees in computer science and engineering from Tsinghua University, Beijing, China, and the MSE and PhD degrees in computer science from Johns Hopkins University, Baltimore, Maryland, USA. Dr. Yang is a SUNY Distinguished Professor in the Department of Electrical & Computer Engineering and Department of Computer Science at Stony Brook University, New York, USA. She is currently on leave serving as a Program Director at the US National Science Foundation. She has served as the Associate Dean for Academic Affairs of College of Engineering and Applied Sciences at Stony Brook University and a Division Director of New York State Center of Excellence in Wireless and Information Technology. Dr. Yang is internationally recognized for her contributions in parallel & distributed computer architectures and systems. She was named an IEEE Fellow in 2009 for contributions to parallel and distributed computing. Her current research interests include parallel computer architecture, network-based computing, cloud computing, edge computing and mobile computing. She has published over 460 scientific papers in leading refereed journals and conferences. Dr. Yang is currently the Editor-in-Chief for IEEE Transactions on Cloud Computing and an Associate Editor for ACM Computing Surveys and IEEE Transactions on Parallel and Distributed Systems. She has served as the Associated Editor-in-Chief for IEEE Transactions on Computers and IEEE Transactions on Cloud Computing, and an Associated Editor for IEEE Transactions on Computers and IEEE Transactions on Parallel and Distributed Systems.

This talk presents an emerging pervasive edge computing paradigm where heterogeneous edge devices (e.g., smartphones, tablets, IoT and vehicles) can collaborate to sense, process data and create many novel applications at network edge. We propose a data centric design where data become self-sufficient entities that are stored, referenced independently from their producers. This enables us to design efficient and robust data discovery, retrieval and caching mechanisms. We also consider secure data caching by applying blockchain technology to the edge, and architecture support for edge nodes by FPGA to combat the heterogeneity among edge nodes. The future research agenda including scalable data discovery, cache management, autonomous processing, trust, security and privacy, incentives and semantic data naming will be discussed.

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

**Abstract TEXT**

Milind Tambe is Gordon McKay Professor of Computer Science and Director of Center for Research in Computation and Society at Harvard University; concurrently, he is also Director "AI for Social Good" at Google Research India. He is a recipient of the IJCAI John McCarthy Award, ACM/SIGAI Autonomous Agents Research Award from AAMAS, AAAI Robert S Engelmore Memorial Lecture award, INFORMS Wagner prize, Rist Prize of the Military Operations Research Society, Columbus Fellowship Foundation Homeland security award, over 25 best papers or honorable mentions at conferences such as AAMAS, AAAI, IJCAI and meritorious commendations from agencies such as the US Coast Guard and the Los Angeles Airport. Prof. Tambe is a fellow of AAAI and ACM.

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. I focus on the problems of public health and conservation, and address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. I will present results from work around the globe in using AI for HIV prevention, Maternal and Child care interventions, TB prevention and COVID modeling, as well as for wildlife conservation. Achieving social impact in these domains often requires methodological advances. To that end, I will highlight key research advances in multiagent reasoning and learning, in particular in, computational game theory, restless bandits and influence maximization in social networks.In pushing this research agenda, our ultimate goal is to facilitate local communities and non-profits to directly benefit from advances in AI tools and techniques.