Dr. Sarah Dean
Assistant Professor, Computer Science Department, Cornell University
Time: 12:00 pm – 1:00 pm
Date: Friday, September 6
Location: Building C
Abstract: Modern robotic systems benefit from machine learning and human interactions. In this talk, I will discuss recent and ongoing work on developing algorithmic foundations for learning with and from human interactions. I will start with motivation: a collaboration on building a robot for assistive feeding that adaptively asks for help. The first key algorithmic question is how to decide when to query a human expert. I will describe a recently developed interactive bandit algorithm with favorable regret guarantees. The second key question is how to learn dynamical models of human mental state, like cognitive load or boredom, from partial observations. I will describe a learning algorithm based on ideas from system identification that comes with sample complexity guarantees. This is based on joint work with Rohan Banerjee, Tapomayukh Bhattacharjee, Jinyan Su, Wen Sun, Yahya Sattar, and Yassir Jedra.
Bio: Sarah is an Assistant Professor in the Computer Science Department at Cornell. She is interested in the interplay between optimization, machine learning, and dynamics, and her research focuses on understanding the fundamentals of data-driven control and decision-making. This work is grounded in and inspired by applications ranging from robotics to recommendation systems. Sarah has a PhD in EECS from UC Berkeley and did a postdoc at the University of Washington.