Mengdi Wang

Mengdi Wang is a Professor of Electrical and Computer Engineering and at the Center for Statistics and Machine Learning at Princeton University. She holds courtesy appointments in the Department of Computer Science and is affiliated with the Omenn-Darling Bioengineering Institute. As co-director of Princeton AI for Accelerated Invention, she leads initiatives bridging AI and scientific discovery. Her visiting positions include research at DeepMind, Institute for Advanced Study, and Simons Institute.

Education:

• Ph.D. in Electrical Engineering and Computer Science, Massachusetts Institute of Technology (2013)
• B.S. in Automation, Tsinghua University (2007)

Her research focuses on theoretical and applied artificial intelligence, particularly in reinforcement learning, generative models, and AI-driven scientific discovery. She investigates fundamental questions in machine learning theory while developing novel algorithms for healthcare, biotechnology, and intelligent systems. Her work emphasizes provable guarantees, robustness, and real-world adaptability of AI systems.

Recent publications demonstrate strong emphasis on generative AI (20%), autonomous agents (25%), biomedical applications (30%), theoretical foundations (15%), and AI safety (10%). Dominant trends include self-evolving systems, multimodal reasoning, biological sequence modeling, and AI acceleration of scientific workflows.

Awards:

• Donald Eckman Award, AACC (2024)
• WAIC YunFan Award (2022)
• MIT TR35 China Innovator (2018)
• Google Faculty Award (2017)
• NSF CAREER Award (2017)
• MOS Young Researcher Prize (2016)
• Princeton SEAS Innovation Award (2016)

She advises 11 doctoral students including Jiacheng Guo, Runzhe Wang, and Yukang Yang. Her research is supported by:

• Federal: NSF, NIH, ONR, AFOSR, MURI
• Industry: Google, Microsoft, C3.ai
• Biotech: RVAC Medicines, GenMab, FinUP

She leads the Princeton Machine Learning and Optimization Group, co-directs the Princeton AI for Accelerated Invention initiative, and contributes to Princeton's Language+Intelligence Initiative. Her team focuses on developing foundational AI methods and deploying them in scientific domains through interdisciplinary collaboration.