Research and career
Wang specializes in the theoretical frameworks that underpin machine learning and reinforcement learning. [3] She joined Princeton University as an assistant professor in 2014. [4] She was the first person to propose stochastic gradient methods for composition optimisation. [1] Her early work used reinforcement to minimize risk in financial portfolios and help hospitals identify potential complications. [3]
Wang has studied Markov decision processes, a model for reinforcement learning. She uses state compression methods to use empirical data to sketch black box Markov processes. [4]
In 2020, Wang joined the C3.ai Digital Transformation Institute, a consortium of researchers who seek to accelerate the use of artificial intelligence in society. She proposed that reinforcement learning could be used to protect educational establishments from COVID-19. [5] 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. [5] In 2024, she was awarded a United States Department of Defense Multidisciplinary University Research Initiative program to develop AI and reinforcement learning for biological systems. [6] She showed it was possible to use large language models with semantic representation to design MRNA vaccines. [7]
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