College of Engineering l Department of Electrical and Computer Engineering
Ramya Korlakai Vinayak is an assistant professor in the Dept. of Electrical Computer Engineering at the UW-Madison. Her research interests span the areas of machine learning, statistical inference, and crowdsourcing. Her work focuses on addressing theoretical and practical challenges that arise when learning from societal data. Prior to joining UW-Madison, Ramya was a postdoctoral researcher in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. She received her Ph.D. in Electrical Engineering from Caltech. She is a recipient of the Schlumberger Foundation Faculty of the Future fellowship from 2013-15, and an invited participant at the Rising Stars in EECS workshop in 2019. She obtained her Masters from Caltech and she did her Bachelors in India at IIT Madras.
Learning from Soceital Data
Machine learning algorithms for policy and decision making are becoming ubiquitous. In many societal applications, the inferences we can draw are often severely limited not by the number of subjects in the data but rather by limited observations available for each subject. My research focuses on tackling these limitations both from theoretical and practical perspectives. In this talk, I will provide a high-level overview of these challenges and some of the approaches being developed in my research group to tackle them.