معرفی
Mengdi Wang is a Professor at Princeton University with primary appointments in the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning, and courtesy appointments in the Department of Computer Science and Omenn-Darling Bioengineering Institute. She co-directs Princeton AI for Accelerated Invention and is affiliated with the Princeton ML Theory Group and Princeton Language+Intelligence Initiative, with prior visiting roles at DeepMind, IAS, and Simons Institute.
Her educational background includes a PhD in Electrical Engineering and Computer Science (with Mathematics minor) from MIT (2013), advised by Dimitri P. Bertsekas at LIDS, and undergraduate studies in Automation at Tsinghua University:
- PhD: MIT, Electrical Engineering and Computer Science (2013)
- Bachelor: Tsinghua University, Automation
Her research establishes theoretical foundations for machine learning with emphasis on reinforcement learning algorithms, generative AI, and large language models. She investigates data-driven stochastic optimization, statistical limits of reinforcement learning, representation learning, and diffusion models, developing provably robust algorithms for complex systems. Her work bridges theoretical guarantees with real-world applications in healthcare, biotech drug discovery, fintech, and scientific acceleration, focusing on how AI can transform discovery processes across disciplines.
Her scientific contributions are recognized by prestigious awards:
- Young Researcher Prize in Continuous Optimization (Mathematical Optimization Society, 2016)
- Princeton SEAS Innovation Award (2016)
- NSF Career Award (2017)
- Google Faculty Award (2017)
- MIT Tech Review 35-Under-35 (China region, 2018)
- WAIC YunFan Award (2022)
- Donald Eckman Award (American Automatic Control Council, 2024)
Professor Wang actively mentors students and recruits undergraduate interns, visitors, and postdocs for her research group. Her work is supported by major grants from NSF, AFOSR, NIH, ONR, Google, Microsoft C3.ai, FinUP, RVAC Medicines, MURI, and GenMab. She serves as Program Chair for ICLR 2023 and Senior Area Chair for NeurIPS, ICML, and COLT, while editing for Harvard Data Science Review and Operations Research.
She leads Princeton AI for Accelerated Invention, which develops AI-driven solutions for scientific discovery, collaborating closely with Princeton's ML Theory Group and Language+Intelligence Initiative to advance algorithmic innovation and interdisciplinary applications.
