Statistics Seminar Speaker: Yueji Chi

Event Layout

Wednesday Apr 09 2025

Statistics Seminar Speaker: Yueji Chi

4:00pm @ 114 Gates Hall/Virtual

This talk is co-sponsored with the Electrical and Computer Engineering Colloquium, and the Center for Data Science for Enterprise and Society

Dr. Yuejie Chi is the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Mellon University, with courtesy appointments in the Machine Learning department and CyLab. She received her Ph.D. and M.A. from Princeton University, and B. Eng. (Hon.) from Tsinghua University, all in Electrical Engineering. Her research interests lie in the theoretical and algorithmic foundations of data science, signal processing, machine learning and inverse problems, with applications in sensing, imaging, decision making, and AI systems. Among others, Dr. Chi received the Presidential Early Career Award for Scientists and Engineers (PECASE), SIAM Activity Group on Imaging Science Best Paper Prize, IEEE Signal Processing Society Young Author Best Paper Award, and the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing. She is an IEEE Fellow (Class of 2023) for contributions to statistical signal processing with low-dimensional structures.

Attend this talk via ZOOM

Talk: Federated Reinforcement Learning: Statistical, Communication and Computation Trade-offs

Abstract: Reinforcement learning (RL), concerning decision making in uncertain environments, lies at the heart of modern artificial intelligence. Due to the high dimensionality, training of RL agents typically requires a significant amount of computation and data to achieve desirable performance. However, data collection can be extremely time-consuming with limited access in real-world applications, especially when performed by a single agent. On the other hand, it is plausible to leverage multiple agents to collect data simultaneously, under the premise that they can learn a global policy collaboratively without the need of sharing local data in a federated manner. This talk addresses the fundamental statistical, communication and computation trade-offs in the algorithmic designs of federated RL algorithms, covering both blessings and curses in the presence of data and task heterogeneities across the agents.