Going Beyond Prediction(s)
Qatar Computing Research Institute / HBKU, Qatar
Recent successes in AI can be attributed to the fact the supervised learning in static prediction tasks is effectively a solved problem. However, predictions on their own are not sufficient for decision making. Using road traffic signal control as a use case, we will highlight how reinforcement learning and its more recent offline variant can serve as a principled way of driving data-driven decision making.
Sanjay Chawla is a Research Director at the Qatar Computing Research Institute (QCRI). Before that he was a professor in the Faculty of Engineering, University of Sydney. His research interests span data mining and machine learning. He was a PC-Cochair of ACM SIGKDD 2021.
Can Federated Learning be Safe?
School of Computer Science
Georgia Institute of Technology
Abstarct: Federated learning (FL) is an emerging distributed collaborative learning paradigm by decoupling the learning task from the centralized server to a decentralized population of edge clients. One of the attractive features of federated learning is its default client privacy, allowing clients to keep their sensitive training data locally and only share local model updates with the federated server. However, recent studies have revealed that such default client privacy is insufficient for protecting the privacy of client training data from both gradient leakage attacks and data poisoning attacks. This keynote will describe gradient leakage attacks and data poisoning attacks, and provide insights for designing effective privacy and security strategies for combating privacy leakage attacks and data poisoning attacks.
Bio: Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large-scale big data-powered artificial intelligence (AI) systems, and machine learning (ML) algorithms and analytics, including performance, availability, privacy, security and trust. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award (2012), and a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, IEEE ICWS. Prof. Liu served on editorial board of over a dozen international journals, including the editor in chief of IEEE Transactions on Service Computing (2013-2016), and the editor in chief of ACM Transactions on Internet Computing (since 2019). Prof. Liu is a frequent keynote speaker in top-tier venues in Big Data, AI and ML systems and applications, Cloud Computing, Services Computing, Privacy, Security and Trust. Her current research is primarily supported by USA National Science Foundation under CISE programs, IBM and CISCO.
Ganos: A Multidimensional, Dynamic, and Scene-Oriented Cloud-Native Spatial Database Engine
Vice President of Alibaba Group
Abstract: Recently, the trend of developing digital twins for smart cities has driven a need for managing large-scale Multidimensional, Dynamic, and Scene-oriented (MDS for short) spatial data. Due to the large data scale and complex data structure, queries over such data are more complicated and expensive than those on traditional spatial data, which poses challenges to the system efficiency and deployment costs. This talk will introduce Ganos, a cloud-native spatial database engine of PolarDB that is developed by Alibaba Cloud, to efficiently manage MDS spatial data. Ganos provides a systematic framework of data models, access methods, and operations for the MDS data. Especially, it optimizes query processing using cloud-native capabilities, and thus provides a new practice of moving from traditional on-premise spatial database to cloud-native spatial database. Ganos has been released since 2018, and it has been applied to many applications in different fields. This talk also shares the lessons learned from the customers and the expectations of modern cloud applications for new spatial database features.
Bio: Feifei Li is currently a Vice President of Alibaba Group, director of the database team of Alibaba Cloud Intelligence, and director of the database lab of DAMO academy. He has won multiple awards from ACM and IEEE and others. He is a recipient of the EDBT 2022 10 Years Test of Time Award, IEEE ICDCS 2020 best paper award, ACM SoCC 2019 Best Paper Award Runner-up, IEEE ICDE 2014 10 Years Most Influential Paper Award, ACM SIGMOD 2016 Best Paper Award, ACM SIGMOD 2015 Best System Demonstration Award, and IEEE ICDE 2004 Best Paper Award. He has been an editor, PC co-chair, and core committee member for many prestigious journals, conferences, and technical meetings. He has led the R&D efforts of building cloud-native database systems and products at Alibaba, such as cloud-native relational database PolarDB and cloud-native data warehouse AnalyticDB which help Alibaba Cloud Database to be named as a worldwide Cloud DBMS leader in the annual magic quadrant market report released by Gartner. He is an ACM/IEEE Fellow and featured on People of ACM.
Mobility and its Role in Enabling Resilient Cyber-Physical-Human Infrastructures
University of California – Irvine, USA
Emerging information technologies such as cyberphysical systems, Internet-of-Things, cloud computing, mobile/wireless networking, and big data technologies are making available new modalities of information and new channels of communication. These technologies have the potential to enable new levels of resilience and efficiencies in community-wide infrastructures and services via smart building services, robust and sustainable water systems and timely access to public safety resources in times of duress. We envision future cyber-physical-human infrastructures (CPHIs) that will provide more efficient operation on a day-to-day basis as well as enhanced situational awareness during extreme events and disasters.
In this talk, we discuss the role of “planned” and “event-driven” mobility in creating resilient CPHIs that require combining technologies at different levels. At the platform level, systems must incorporate intelligent collection and ingest of data from diverse insitu and mobile sensors and sources and timely data exchange across organizations/individuals over heterogeneous communication networks. At the information level, the gathered information is used to extract higher level semantic observations by composing model-driven and AI-driven methods. Adaptation is a fundamental aspect of systems that rapidly transform themselves to meet dynamic needs; we discuss how incorporating mobility is critical to enabling adaptation for resilient community platforms. Drawing on our recent efforts in smart buildings, smartwater platforms and smart firefighting, I will discuss the role of mobility, the Internet-of-Things, and adaptive middleware for community lifelines. This will open up new possibilities for resilient communities of the future.
Nalini Venkatasubramanian is currently a Professor in the School of Information and Computer Science at the University of California Irvine. She has had significant research and industry experience in the areas of distributed systems, adaptive middleware, pervasive and mobile computing, cyberphysical systems, distributed multimedia and formal methods and has over 300 publications in these areas. As the Co-Director for the Center for Emergency Response Technologies at UC Irvine, Nalini’s recent research has focused on enabling resilient, sustainable and scalable observation and analysis of situational information from multimodal input sources; dynamic adaptation of the underlying systems to enable information flow under massive failures and the dissemination of rich notifications to members of the public at large. She is the recipient of the prestigious NSF Career Award, multiple Undergraduate Teaching Excellence Awards and best paper awards. Prof. Venkatasubramanian has served in numerous program and organizing committees of conferences on middleware, distributed systems and multimedia and on the editorial boards of journals. She received and M.S and Ph.D in Computer Science from the University of Illinois in Urbana-Champaign. Her research is supported both by government and industrial sources such as NSF, DHS, ONR, DARPA, Novell, Hewlett-Packard and Nokia. Prior to arriving at UC Irvine, Nalini was a Research Staff Member at the Hewlett-Packard Laboratories in Palo Alto, California.