Wednesday Nite at the Lab
press release: WN@TL goes hybrid both with Zoom and with in-person (Room 1111) presentations. The zoom registration link is still go.wisc.edu/240r59. You can also watch a live web stream at on YouTube.
Oct. 26: "Learning from Societal Data," by Ramya Korlakai Vinayak
Machine learning algorithms learn to make predictions and inferences using large quantities of data. More than half a century of advances in this field have led us to build very good systems that identify spam emails, make our phone cameras detect where to focus when taking pictures, and recommend relevant items or movies on e-commerce platforms. We are now using machine learning algorithms to many critical societal applications in health care, finance, criminal justice systems, and governance to aid in decision making which have far reaching consequences on our lives in the present and the future. However, the nature of the data that we learn from and the consequences of making wrong inferences in these applications are very different.
In many societal applications, the data comprises people from diverse backgrounds. The inferences we can draw from such societal-scale datasets are often severely limited not by the number of people in the data but rather by limited observations available for each individual. Therefore, addressing these challenges due to diversity among the population and the limited observations per individual are critical. My research focuses on tackling these limitations both from theoretical and practical perspectives. In this talk, I will provide a high-level overview of how machine learning algorithms learn from data, what are the key challenges to learning from data from diverse people, and some of the approaches being developed in my research group and from other researchers in the field to tackle them.
Bio: Ramya Vinayak is an assistant professor in the Department of Electrical and Computer Engineering and affiliate faculty in the Department of Computer Science at UW-Madison. Her research focuses on machine learning, statistical inference and crowdsourcing.
Prior to joining UW-Madison she was a postdoctoral researcher at the University of Washington in Seattle. She obtained her Ph.D. and masters degrees in Electrical Engineering at Caltech in Pasadena, California. She grew up in the southern part of India and obtained her undergraduate degree in Electrical Engineering at Indian Institute of Technology Madras before starting her academic research journey. In her spare time, she enjoys hiking, cooking and painting.
Explore More:
Machine learning and Optimization Group at UW-Madison: https://mlopt.ece.
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