Lingkai Kong

孔令恺

version1.png

lingkaikong@hku.hk

I am a tenure-track Assistant Professor in the School of Computing and Data Science at the University of Hong Kong (HKU), with a co-hosted appointment in the School of Public Health. Previously, I was a postdoctoral fellow at Harvard University, advised by Prof. Milind Tambe. I obtained my Ph.D. in Computational Science and Engineering from the Georgia Institute of Technology, advised by Prof. Chao Zhang.

For more details, please see my CV.

I am actively looking for students and collaborators. If you are interested, please email me with the subject line “[PhD/RA/Intern + Year]” and include your CV and transcript.

My long-term goal is to build generative AI agents that understand the world, work alongside people, and help us make the decisions that matter most. My research pursues this vision along three threads:

Generative Decision-Making (RL × GenAI)

Generative models that act, not just generate. I combine reinforcement learning with diffusion models and LLMs to study reliable generative policies and long-horizon planning under uncertainty.

Multi-Agent Systems

How autonomous agents, together with the people in the loop, learn, cooperate, and strategically interact, and the collective behavior that emerges.

AI for Impact

Translating these methods into deployed systems for high-stakes domains such as public health and sustainability.

Selected publications

  1. 2026
    Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
    Lingkai Kong, Anagha Satish, Hezi Jiang, Akseli Kangaslahti, Andrew Ma, Wenbo Chen, Mingxiao Song, Lily Xu, and Milind Tambe
    In International Conference on Machine Learning (ICML) (Spotlight), 2026
    PDF
  2. 2026
    Reward Shaping for Inference-Time Alignment: A Stackelberg Game Perspective
    Haichuan Wang, Tao Lin, Lingkai Kong, Ce Li, Hezi Jiang, and Milind Tambe
    In International Conference on Machine Learning (ICML), 2026
    PDF
  3. 2025
    Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
    Lingkai Kong, Haichuan Wang, Tonghan Wang, Guojun Xiong, and Milind Tambe
    In Advances in Neural Information Processing Systems (NeurIPS), 2025 (Spotlight)
    PDF
  4. 2025
    Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
    Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortol, Haorui Wang, Dongxia Wu, Aaron Ferber, Yi-An Ma, Carla P. Gomes, and Chao Zhang
    In Artificial Intelligence and Statistics (AISTATS), 2025
    PDF
  5. 2024
    Aligning Large Language Models with Representation Editing: A Control Perspective
    Lingkai Kong, Haorui Wang, Wenhao Mu, Yuanqi Du, Yuchen Zhuang, Yifei Zhou, Yue Song, Rongzhi Zhang, Kai Wang, and Chao Zhang
    In Advances in Neural Information Processing Systems (NeurIPS), 2024
    PDF
  6. 2023
    AdaPlanner: Adaptive Planning from Feedback with Language Models
    Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang
    In Advances in Neural Information Processing Systems (NeurIPS), 2023
    PDF
  7. 2022
    End-to-End Stochastic Optimization with Energy-based Model
    Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, and Chao Zhang
    In Advances in Neural Information Processing Systems (NeurIPS), 2022 (Oral, 181/10411, top 1.76%)
    PDF