I am broadly interested in AI for decision-making and envision a future where AI agents coexist with humans to enable complex task automation and decision support. My research themes include:

  • Ensuring the safe and reliable deployment of AI agents in real-world applications, with continuous alignment and adaptation to business and societal objectives.
  • Leveraging AI to simulate human and system behavior (known as “digital twin”), enabling cost-effective, scalable synthetic data generation, experimentation, and testing.
  • Reducing the operational costs of AI systems to enable scalable, low-latency deployment across diverse environments.
  • Optimizing large-scale decision-making systems through AI and reinforcement learning.

I’ve collaborated with organizations like Anheuser-Busch InBev, TikTok, and Liberty Mutual. My work with Anheuser-Busch InBev on developing an innovative experimentation platform earned the Daniel H. Wagner Prize in 2022 (INFORMS news). My research has also received the APS Best Student Paper Prize and RMP Jeff McGill Student Paper Award.

I hold a Ph.D. from MIT and a Bachelor’s in Computer Science from Tsinghua University (Yao Class). I am a founding member of Cimulate.AI, developing Customer-GPT for e-commerce.