What Next?

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Professor Simon See is currently the Solution Architecture and Engineering Director, Chief Solution Architect and Global Head for NVIDIA AI Technology Center, NVIDIA Corporation. He is also an Adjunct Professor at Shanghai Jiao Tong University, Conventry University, and Universitas Indonesia (UI). He is also a distinguished fellow in Fudan University. Previously Professor See is also the Chief Scientific Computing Advisor for BGI (China) and has a position in Nanyang Technological University (Singapore) and King-Mong Kung University of Technology (Thailand). Professor See is currently involved in a number of International computational, mathematical science projects and national AI initiatives. Recently Professor Simon has been appointed as the Executive Director of the ASEAN Applied Research Centre (AARC). His research interests are in the area of High-Performance Computing, Big Data, Artificial Intelligence, Machine Learning, Computational Science, Applied Mathematics and Simulation Methodology. Professor See is also leading some of the AI initiatives in Asia Pacific. He is a Steering Committee member of NSCC’s flagship High Performance Computing Conference Supercomputing Asia (SCA) since March 2018. He has published over 200 papers in these areas and has won various awards in the field.

Professor See is a Fellow member of IET, Chairman of TaskForce of IEEE’s CIS Neural Networks Technical Committee, a member of SIAM, IET, AAAI, SCS, and also on the Advisory Team of AIP (AI Professional Association), International Advisory Board of Institute of Operations Research & Analytics (IORA), Advisory Team of Machine Intelligence and Data Analytics Research Centre (MIDARC), School of Computing Sciences (India) and Board of Studies, MS Tech Programs of Mahindra University, Editorial Board Member of Journal of Computational and Cognitive Engineering (JCCE), The Industry Advisory Committee for Bachelor of Science in Computer Science in Real-time Interactive Simulation and Interactive Media and Game Design at Singapore Institute of Technology (SIT) and the committee member of more than 50 conferences.

Professor See graduated from the University of Salford (UK) with a Ph.D. in electrical engineering and numerical analysis in 1993. Prior to joining NVIDIA, Professor See worked for SGI, DSO National Lab. of Singapore, IBM, International Simulation Ltd (UK), Sun Microsystems and Oracle. He is also providing consultancy to a number of national research and supercomputing centers.

Towards Practical, Scalable and Private Management of Cloud Data

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Abstract

Due to the widespread use of cloud applications, searching for data from cloud servers is ubiquitous. However, accessing data stored in a cloud server comes with severe privacy concerns owing to numerous attacks and data breaches. Much research has focused on preserving the privacy of data stored in the cloud using various advanced cryptographic techniques. Our goal in this talk is to demonstrate how private access of data can become a practical reality in the near future. Our focus is on supporting oblivious queries and thus hide any associated access patterns on both private and public data. For private data, ORAM (Oblivious RAM) is one of the most popular approaches for supporting oblivious access to encrypted data. However, most existing ORAM datastores are not fault tolerant and hence an application may lose all of its data when failures occur. To achieve fault tolerance, we propose QuORAM, the first datastore to provide oblivious access and fault-tolerant data storage using a quorum-based replication protocol. For public data, PIR (Private Information Retrieval) is the main mechanism proposed in recent years. However, current PIR proposals are inefficient especially with large data sets and require the server to consider data as an array of elements and clients retrieve data using an index into the array. This latter restriction limits the use of PIR in many practical settings, especially for key-value stores, where the client may be interested in a particular key, but does not know the exact location of the data at the server. In this talk we will discuss recent efforts to overcome these limitations, using Fully Homomorphic Encryption (FHE), to improve the performance, scalability and expressiveness of privacy preserving queries on public data.

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Amr El Abbadi is a Distinguished Professor of Computer Science. He received his B. Eng. from Alexandria University, Egypt, and his Ph.D. from Cornell University. His research interests are in the fields of fault-tolerant distributed systems and databases, focusing recently on Cloud data management, blockchain based systems and privacy concerns. Prof. El Abbadi is an ACM Fellow, AAAS Fellow, and IEEE Fellow. He was Chair of the Computer Science Department at UCSB from 2007 to 2011. He served as Associate Graduate Dean at the University of California, Santa Barbara from 2021--2023. He has served as a journal editor for several database journals, including, The VLDB Journal, IEEE Transactions on Computers and The Computer Journal. He has been Program Chair for multiple database and distributed systems conferences, including most recently SIGMOD 2022. He currently serves on the executive committee of the IEEE Technical Committee on Data Engineering (TCDE) and was a board member of the VLDB Endowment from 2002 to 2008. In 2007, Prof. El Abbadi received the UCSB Senate Outstanding Mentorship Award for his excellence in mentoring graduate students. In 2013, his student, Sudipto Das received the SIGMOD Jim Gray Doctoral Dissertation Award. Prof. El Abbadi is also a co-recipient of the Test of Time Award at EDBT/ICDT 2015. He has published over 350 articles in databases and distributed systems and has supervised over 40 PhD students.

Data and AI Markets: Grand Opportunities for Facilitating Sharing, Discovery, and Integration in Data and AI Economies

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Abstract

Data and AI model sharing has been a long-time bottleneck for AI and data economies.  In this talk, I will argue that data and AI model discovery and integration are foundations for effective sharing. I will also revisit why sharing remains a big challenge and why many existing approaches like data warehouses, data lakes, federated databases, and federated learning are still far from enough to solve the problem, particularly for sharing among organizations. Then, I will advocate data and AI markets as a potential grand opportunity for data and AI model sharing at scale, particularly for inter-organization sharing. Using some recent studies, I will demonstrate some exciting technical problems in data and AI model markets for the database and data science communities.  I will also offer my humble views on the future directions on data and AI model markets.

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Dr. Jian Pei is the Arthur S. Pearse Distinguished Professor at Duke University.  He is a trailblazing expert in the domains of data science, data mining, database systems, machine learning, and information retrieval. His pioneering contributions to the development of data science principles and techniques for data-driven and data-intensive applications have garnered him accolades and recognition as a Fellow of the Royal Society of Canada, the Canadian Academy of Engineering, ACM, and IEEE. With a prolific publication record in premier academic venues, Dr. Pei's works have garnered over 123,000 citations. He is the recipient of numerous prestigious awards, including the 2017 ACM SIGKDD Innovation Award and the 2015 ACM SIGKDD Service Award. Dr. Pei's dedication to the field is evident through his past roles as the chair of ACM SIGKDD and the former Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering, as well as the major organizers of many conferences.