Philip Thomas

Interests

Reinforcement learning, decision making, and AI safety.

Research

Professor Thomas's research interests are in reinforcement learning, decision making, and AI safety. He is most interested in designing reinforcement learning algorithms that are more biologically plausible than existing algorithms, or which provide various forms of safety guarantees that make them viable for high-risk applications (e.g., medical applications). Towards these goals he has performed extensive work on (high-confidence) off-policy policy evaluation methods, with preliminary experiments for both digital marketing and medical applications. He has also studied methods for performing deep reinforcement learning without the need for the biologically implausible propagation of information backwards through the neural network.  
 

Biography

Ph.D., Computer Science, University of Massachusetts Amherst (2015). M.Sc. Computer Science, Case Western Reserve University (2009), B.Sc. Computer Science, Case Western Reserve University (2008). Philip spent 2015-2017 as a postdoctoral researcher at Carnegie Mellon University. Philip will join the College of Information and Computer Sciences at the University of Massachusetts, Amherst in Fall 2017 as an Assistant Professor.

Activities & Awards

Professor Thomas has published in top AI conferences and journals, including the prestigious journal Science, and testified to the U.S. House Committee on Financial Services, Task Force on Artificial Intelligence in 2020. He is Co-PI on an Army Research Grant (IoBT), an NSF grant (FMitF), and the Armstrong Award. He has also received significant funding from Adobe Research. Prof. Thomas regularly serves as an area chair for NeurIPS and ICML, and served as the Workshops Co-chair for RLDM 2019. He has served as a reviewer for NeurIPS, ICML, AAAI, IJCAI, UAI, IROS, ICLR, RLDM, Nature, JAIR, MLJ, and JMLR.