Machine learning models: From birth to serving the real worldDate & Time: 08/01/2020, 4:00 pm
Venue: Faculty Hall, Indian Institute of Science
A Machine Learning (ML) Model is born by fitting a function around examples sampled from an unknown distribution, and is designed to generalize to other examples drawn from the same distribution. The machine learning edifice largely rests on this foundation. However, modern ML models trained for challenging tasks like object detection, speech recognition, and language understanding require huge amounts of labeled data and leave a large carbon footprint. In return, a model once trained needs to serve many diverse real-world settings that do not perfectly match its birth setting. In this talk, I will discuss current research on propping the ML edifice against such shifting foundation. We will span over current props like calibration, out-of-sample detection, domain adaptation, domain generalization, and robustness and reflect on some futuristic topics.
Prof. Sunita Sarawagi is Institute Chair Professor in Computer Science and Engineering at IIT-Bombay. Prof. Sarawagi received her B.Tech. in Computer Science from the Indian Institute of Technology, Kharagpur in May 1991. She received her M.S. and Ph.D. in Computer Science from the University of California at Berkeley where she studied under Michael Stonebraker.
Following her Ph.D., Sarawagi did stints at IBM Almaden Research Center as research scholar, Carnegie Mellon as visiting faculty, and joined IIT-Bombay in 1999. Between July 2014 and July 2016 Prof. Sarawagi was Visiting Scientist at Google Inc. in Mountain View where she worked on deep learning models for personalizing and diversifying YouTube and Play recommendations, improving Duo’s conversation assistance engine, and extracting attributes of classes from the Knowledge Graph.
Among her many awards are IBM Faculty award (2003 and 2008); Fellow of the Indian National Academy of Engineering (INAE) (2013); PAKDD Most Influential Paper Award 2014 for the paper: “Discriminative Methods for Multi-labeled Classification Shantanu Godbole and Sunita Sarawagi in PAKDD 2004”. Prof. Sarawagi has several patents to her name. They include a patent for “Database System and Method Employing Data Cube Operator for Group-By Operations” and a patent for “Efficient evaluation of queries with mining predicates”.
The Infosys Prize 2019 in Engineering and Computer Science is awarded to Prof. Sunita Sarawagi for her research in databases, data mining, machine learning and natural language processing, and for important applications of these research techniques. The prize recognizes her pioneering work in developing information extraction techniques for unstructured data.