Mengdi Wang

Mengdi Wang is a theoretical computer scientist who is a professor at Princeton University. Her research considers the fundamental theory that underpins reinforcement and machine learning. She was named one of MIT Technology Review 's 35 Under 35 in 2018.

Early life and education Wang was an undergraduate student at Tsinghua University, where she specialized in automation. At the age of 18, she joined Massachusetts Institute of Technology as a graduate student, where she worked alongside Dimitri Bertsekas. Her doctoral research developed stochastic methods for large-scale linear systems.

Research and career Wang specializes in the theoretical frameworks that underpin machine learning and reinforcement learning. She joined Princeton University as an assistant professor in 2014. She was the first person to propose stochastic gradient methods for composition optimisation. She used system identification and adaptive control to develop strategies to understand the health status of students, and to deploy algorithms that recommend interventions to decision makers. She showed it was possible to use large language models with semantic representation to design MRNA vaccines.

Awards and honors 2016 Mathematical Optimization Society Young Researcher Prize in Continuous Optimization 2016 Princeton SEAS Innovation Award 2017 NSF Career Award 2017 Google Faculty Award 2018 MIT Tech Review 35-Under-35 2024 American Automatic Control Council Donald Eckman Award

Selected publications