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William and Flora Hewlett Foundation Fellow
Indian Institute of Science (India)
Computer Science
Learning to Make Choices in the Era of Big Data

Shivani Agarwal is an assistant professor and a Ramanujan Fellow at the Indian Institute of Science. Her interests lie in machine learning, the science of learning predictive models from data. She is best known for her foundational work on ranking and other new machine learning problems, as well as in applications of machine learning methods in the life sciences; for example, her recent work on predicting anticancer drug response.

At Radcliffe, Agarwal is studying computational models that can be used to understand how people make choices in the face of increasingly vast amounts of data. In particular Agarwal hopes to bring together techniques from machine learning, statistics, social choice theory, psychology, and economics to construct compact models of choice and ranking behavior that incorporate key features of human decision making. Such models could also help to shed light on how the human brain processes choice.

Agarwal studied mathematics at St. Stephen’s College, University of Delhi, and computer science as a Jawaharlal Nehru Memorial Trust Cambridge Scholar at Trinity College, University of Cambridge, and she received her PhD in computer science at the University of Illinois at Urbana–Champaign. She was a postdoctoral lecturer at the Massachusetts Institute of Technology, where she received a National Science Foundation grant for her work on ranking in machine learning. She is an associate of the Indian Academy of Sciences and of the International Centre for Theoretical Sciences and codirects the Indo–US Joint Center for Advanced Research in Machine Learning, Game Theory and Optimization. 

2015–2016 Radcliffe Institute Fellows

This information is accurate as of the fellowship year indicated for each fellow.
Photo by Tony Rinaldo