Abstract: Every field has data. We use data to discover new knowledge, to interpret the world, to make decisions, and even to predict the future. The recent convergence of big data, cloud computing, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars, treat disease, and keep us safe. At the same time, such capabilities risk leading to biased, inappropriate, or unintended action. The design of data science solutions requires both excellence in the fundamentals of the field and expertise to develop applications which meet human challenges without creating even greater risk.
The Data Science Institute at Columbia University promotes “Data for Good”: using data to address societal challenges and bringing humanistic perspectives as—not after—new science and technology is invented. Started in 2012, the Institute is now a university-level institute representing over 350 affiliated faculty from 16 different schools and institutes across campus. Data science literally touches every corner of the university.
In this talk, I will present the mission of the Institute and highlights of our educational and research activities—all with the aim of ensuring the responsible use of data to benefit society.
Prof. Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Adjunct Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology.
Professor Wing's general research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current interests are in the foundations of security and privacy, with a new focus on trustworthy AI. She was or is on the editorial board of twelve journals, including the Journal of the ACM and Communications of the ACM.
Professor Wing is known for her work on linearizability, behavioral subtyping, attack graphs, and privacy-compliance checkers. Her 2006 seminal essay, titled Computational Thinking is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced.
She is currently a member of: the National Library of Medicine Blue Ribbon Panel; the Science, Engineering, and Technology Advisory Committee for the American Academy for Arts and Sciences; the Board of Trustees for the Institute of Pure and Applied Mathematics; the Advisory Board for the Association for Women in Mathematics; and the Alibaba DAMO Technical Advisory Board. She has been chair and/or a member of many other academic, government, and industry advisory boards. She received the CRA Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).
Prof. Christos Faloutsos is a Professor at Carnegie Mellon University. He is the recipient of the Fredkin Professorship in Artificial Intelligence (2020); he has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award in ICDM 2006, the SIGKDD Innovations Award (2010), the PAKDD Distinguished Contributions Award (2018), 28 ``best paper'' awards (including 7 ``test of time'' awards), and four teaching awards. Eight of his advisees or co-advisees have attracted KDD or SCS dissertation awards. He is an ACM Fellow, he has served as a member of the executive committee of SIGKDD; he has published over 400 refereed articles, 17 book chapters and three monographs. He holds 8 patents (and several more are pending), and he has given over 50 tutorials and over 25 invited distinguished lectures. His research interests include large-scale data mining with emphasis on graphs and time sequences; anomaly detection, tensors, and fractals.
Dr. Divesh Srivastava received the BTech degree from the Indian Institute of Technology, Bombay, and the PhD degree from the University of Wisconsin, Madison. He is the head of the Database Research Department at AT&T Labs-Research. He has served as the associate editor-in-chief of the IEEE Transactions on Knowledge and Data Engineering, and the program committee co-chair of many conferences, including VLDB 2007. He has presented keynote talks at several conferences, including VLDB 2010. His research interests span a variety of topics in data management. He is a fellow of the ACM, on the board of trustees of the VLDB Endowment, and an associate editor of the ACM Transactions on Database Systems. (Based on document published on 16 June 2015).