Prof. Yu-Xiang Wang receives NSF Award

By Natalia Diaz Amabilis, PR Assistant

Yu-Xiang Wang has received a new NSF Award to support his research in “Optimal and Adaptive Reinforcement Learning with Offline Data and Limited Adaptivity”. Yu-Xiang Wang is a faculty member of the computer science department at UCSB. Prior to joining UCSB, Wang was a scientist with Amazon AI in Palo Alto. Before Amazon, he was with the Machine Learning Department in Carnegie Mellon University. He also teaches as an assistant professor at UCSB in various Computer Science courses.

Reinforcement learning (RL) is one of the fastest-growing research areas in machine learning. RL-based techniques have led to several recent breakthroughs in artificial intelligence, such as beating human champions in the game of Go. The application of RL to real life problems, however, remains limited, even in areas where a large amount of data has already been collected. The crux of the problem is that most existing RL methods require an environment for the agent to interact with, but in real-life applications, it is rarely possible to have access to such an environment. This project aims to address this conundrum by developing algorithms that learn from offline data. The outcome of the research could significantly reduce the overhead of using RL techniques in real-life sequential decision-making problems such as those in power transmission, personalized medicine, scientific discoveries, computer networking and public policy.

Congratulations to Yu-Xiang Wang, the Department of Computer Science can’t wait to see what you accomplish with this award!