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Mengdi Wang is a Professor in the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning at Princeton University. He is also an affiliated faculty member in the Department of Computer Science and part of Princeton's Machine Learning Theory Group. His research primarily revolves around machine learning, reinforcement learning, generative artificial intelligence, and their applications in scientific intelligence systems. Mengdi obtained his Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2013, where he was advised by Dimitri P. Bertsekas and affiliated with the Laboratory for Information Decision Systems. He holds a Bachelor of Science degree from Tsinghua University. Over the years, he has received multiple accolades, including the Young Researcher Prize from the Mathematical Optimization Society in 2016, the NSF Career Award in 2017, and the Google Faculty Award the same year. Recently, he was honored with the AACC’s Donald Eckman Award in 2024. He actively contributes to the academic community as a Program Chair for ICLR 2023 and serves on the senior program committees for various renowned conferences such as NeurIPS, ICML, and COLT.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.