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Benjamin Eysenbach is an Assistant Professor in the Department of Computer Science at Princeton University. His research focuses on reinforcement learning, intelligent decision-making, and the development of algorithms that enable AI systems to learn from their environments in an unsupervised manner. With a PhD in machine learning from Carnegie Mellon University, he has worked on several high-profile projects, including those at Google Brain. His notable contributions include advancements in self-supervised control methods and innovations in AI training techniques. Eysenbach has received multiple awards for his research and mentorship, including the NeurIPS Paper Award and the NSF CAREER Award. He is actively involved in organizing workshops and tutorials on generative AI and reinforcement learning at major conferences.
Princeton University • Princeton, NJ
Conducts research in AI and reinforcement learning, teaches courses in computer science, and mentors students in the lab.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.