SCALE@NTU Research Seminar Jan 2023

This research seminar is organized by Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU). For registration, please visit :

https://wis.ntu.edu.sg/webexe88/owa/REGISTER_NTU.REGISTER?EVENT_ID=OA23012518085215

Abstract : Data Science continues to have a transformative impact on society, by enabling evidence-based decision making, reducing costs and errors, and improving objectivity. The techniques and technologies of data science also have enormous potential for harm if they reinforce inequity or leak private information. As a result, sensitive datasets in the public and private sector are restricted from research use, slowing progress in those areas that have the most to gain: human services in the public sector. Furthermore, the misuse of data science techniques and technologies will disproportionately harm underrepresented groups across race, gender, physical ability, sexual orientation, education, and more. These data equity issues are pervasive, and represent an existential risk for the use of data-driven methods in science and engineering. In this talk, I will describe a framework to think about these issues, and some initial directions where we have made progress.

Speaker : H. V. Jagadish is Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan in Ann Arbor, and Director of the Michigan Institute for Data Science.  Prior to 1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ.

Professor Jagadish is well known for his broad-ranging research on information management, and has over 200 major papers and 37 patents, with an H-index of 94.  He is a fellow of the ACM, "The First Society in Computing," (since 2003) and of AAAS (since 2018).  He served on the board of the Computing Research Association (2009-2018).  He has been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014).  Since 2016, he is Editor of the Morgan & Claypool “Synthesis” Lecture Series on Data Management.  Among his many awards, he won the David E Liddle Research Excellence Award (at the University of Michigan) in 2008, the ACM SIGMOD Contributions Award in 2013, and the Distinguished Faculty Achievement Award (at the University of Michigan) in 2019.  His popular MOOC on Data Science Ethics is available on EdX, Coursera, and Futurelearn.