At present, most of the research on reinforcement
learning is based on an engineering approach where specific questions come
first, followed by problem-specific engineering or even brute-force solutions, with fundamental theories last. But Mengdi Wang has a different approach. She
starts from the basics of reinforcement learning, tries to understand their fundamental theory and complexity, develops provably efficient algorithms, and eventually
applies them to real-life problems.
Wang is committed to promoting the
theoretical foundation and application of reinforcement learning. The outcomes
of her research can be extended to financial technology, medical artificial
intelligence, robotics, and other applied areas where reinforcement learning
sits at the brain of future complex systems.
After graduating
from Tsinghua University in 2007, Wang went to MIT as a graduate student at age
18. Only 6 years later, she received her MS and Ph.D. in Electrical Engineering
and Computer Science with a minor in Mathematics from MIT, and then joined Princeton
University as an Assistant Professor in the Department of Operations Research
and Financial Engineering.
Her research has
resulted in accelerated optimal-complexity algorithms for a number of
computation challenges, including stochastic composition optimization, non-convex
sparse optimization, online dimension reduction, and Markov Decision Processes
(MDP). Wang was the first person to propose multi-level stochastic gradient
methods for nested composition optimization over a random path. She also created
the first stochastic primal-dual method for the online solution of MDP, which could
provide theoretical proof of the optimal policy and algorithm of a reinforcement
learning system. Her group developed the first sample-optimal reinforcement learning
algorithm, which is provably efficient in using data and learns the optimal
policy on-the-fly after observing a minimal number of samples.
By combining the
ideas of statistics and optimal control systems, Wang’s research group aims to integrate
reinforcement learning into a complex system such as smart medical diagnosis or FinTech,
creating a new perspective on solving risk management, big data analysis, and medical and financial decision making problems. Her research goal is to tackle
the scalability and generalization challenges of reinforcement learning, as
well as the ongoing challenge that AI is over-reliant on massive data.
