About

Biography

I am an AI Research Lead with a Ph.D. in Engineering from the University of Toronto (2025), an M.Eng. from McGill University, and a B.Eng. from Beijing Institute of Technology, where I graduated first in my major. During my academic journey, I also conducted research at UC Berkeley and at the Montreal Institute for Learning Algorithms (MILA).

My research focuses on reinforcement learning, multi-agent systems, and large language model agents, with applications spanning intelligent transportation, decentralized AI, and generative modeling. I have published extensively in Nature, NeurIPS, ICML, IJCAI, EMNLP, ICRA, WACV, IEEE IV, IEEE ITSC, INFORMS and other leading venues, accumulating over 800 citations. I have also supervised students all over the world. I have also established long-term research collaborations with professors and researchers from leading academic institutions and industry labs, including Tsinghua University, Peking University, the University of Toronto, MIT, the University of Wisconsin, Google DeepMind, and Meta Superintelligence.

Beyond academia, I have collaborated widely with companies (including interned or consulting) such as Momenta, Megvii, SINOVATION VENTURES, QCraft, RCT.AI, Huawei, BioMap, Skywork AI, ESAPIENS and several AI related companies, contributing to research incubation, product innovation, and deployment of large-scale AI systems. These experiences have equipped me with both scientific depth and practical perspectives across distributed systems, embodied AI, and LLM-based applications.

Currently, my work centers on collaborative intelligence. I envision a future where the world’s intelligence is co-owned and co-evolved by people, not concentrated in the hands of a few. I believe AGI can only be trusted if every layer of its creation and delivery is open to public oversight. To realize this vision, my team and I are re-building the foundational layers of decentralized AI, developing a transparent, scalable, and participatory ecosystem for collective intelligence.

Research Interests

My research interests span several areas at the intersection of artificial intelligence, transportation, and collective intelligence:

  • Reinforcement Learning and Autonomous Driving: Designing algorithms for safe, efficient, and comfortable decision-making in mixed traffic environments.
  • Multi-Agent Systems and Collective Intelligence: Studying coordination, cooperation, and competition among agents in transportation systems and broader intelligent ecosystems.
  • Intelligent Transportation Systems: Large-scale optimization of traffic flow, signal control, and mobility management using graph reinforcement learning and generative modeling.
  • Distributed and Decentralized AI: Building scalable frameworks for distributed training, inference, and coordination across heterogeneous devices to ensure transparency and reliability.
  • Self-Evolving and Collaborative Agents: Developing agents that learn continuously, share context, and co-evolve in dynamic environments, from autonomous vehicles to digital and robotic systems.
  • AI Fairness, Robustness, and Trust: Ensuring interpretability, bias mitigation, and accountability in intelligent systems deployed in safety-critical domains.

Professional Service

Reviewer

  • NeurIPS, ICML, ICLR
  • CVPR, ICCV, EMNLP, AAAI, ICRA
  • IEEE Transactions on Intelligent Transportation Systems
  • IEEE Transactions on Vehicular Technology
  • Transportation Research Part C: Emerging Technologies
  • Scientific Reports
  • TRB Annual Meeting – Transportation Research Board
  • IEEE International Conference on Intelligent Transportation Systems
  • IEEE Intelligent Vehicles Symposium
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • NeurIPS Workshop on Machine Learning for Autonomous Driving

Skills

  • Programming Languages: Python, C/C++, MATLAB
  • Machine Learning Frameworks: PyTorch, TensorFlow
  • Tools: LaTeX, Git, Docker, ROS
  • Languages: English (Fluent), Mandarin (Native)