Ling Pan

Ling Pan is a Tenure-Track Assistant Professor in the Department of Electronic and Computer Engineering and the Department of Computer Science and Engineering (by courtesy) at the Hong Kong University of Science and Technology (HKUST) from Spring 2024. Her research focuses on theoretical understanding, algorithmic improvements, and practical application of (reward-maximizing) deep reinforcement learning, (reward-matching) GFlowNets, and multi-agent systems, aiming to develop robust, efficient, and practical intelligent decision-making algorithms. She is also interested in the application of decision-making algorithms in practical problems including scientific discovery, computational sustainability, and the alignment of foundation models. Before joining HKUST, she was a postdoctoral fellow at Mila supervised by Prof. Yoshua Bengio (Turing Award Laureate). She obtained her Ph.D. from the Institute for Interdisciplinary Information Sciences (IIIS) (headed by Prof. Andrew Yao), Tsinghua University in 2022, during which she visited Stanford University advised by Prof. Tengyu Ma, University of Oxford advised by Prof. Shimon Whiteson. She received her B.E. from the School of Computer Science and Engineering, Sun Yat-Sen (Zhongshan) University in 2017. Her work has been recognized by the AAAI New Faculty Highlights program, and she was awarded the Microsoft Research PhD Fellowship (Asia).

Please drop me an email if you are interested in collaborating with me.

Prospective Students: I am actively looking for self-motivated students (including undergraduate/graduate students and research assistants) who are interested in the areas of artificial intelligence and machine learning. I have several PhD/MPhil/RA openings starting in Fall 2025/Spring 2026/Fall 2026 at HKUST. Please drop me an email with your CV (including rankings/publications) if you are interested.

Selected Awards

AAAI New Faculty Highlights, 2025
Outstanding Doctoral Thesis, by Tsinghua University, 2022
Thesis: Towards Robust, Efficient, and Practical Deep Reinforcement Learning Algorithms
The only selected computer science doctoral thesis in IIIS, Tsinghua University
Outstanding Graduate (top 3%), by Tsinghua University, 2022
Also Beijing outstanding graduate and IIIS, Tsinghua University outstanding graduate, 2022
China National Scholarship (top 2%), by Ministry of Education of China, 2014/2015/2016/2021

Professional Activities

Organizer:
- Safe Generative AI Workshop @ NeurIPS 2024
- Agent-based Information Retrieval Workshop @ SIGIR 2024, SIGIR 2025
- GFlowNet Workshop @ Mila 2023
Area Chair:
- International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
- Reinforcement Learning Conference (RLC)
PC member/Reviewer:
- International Conference on Machine Learning (ICML)
- Conference on Neural Information Processing Systems (NeurIPS)
- International Conference on Learning Representations (ICLR)
- AAAI Conference on Artificial Intelligence (AAAI)
- Transactions of Machine Learning Research (TMLR)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Selected Talks

Towards Robust, Efficient, and Practical Decision-Making: From Reward-Maximizing Deep Reinforcement Learning to Reward-Matching GFlowNets
RLChina 2024 Conference, October, 2024
RLChina 2023 Conference, November, 2023
Towards Robust, Efficient, and Practical Reinforcement Learning
Computer Science and Artificial Intelligence Lab (CSAIL), MIT, December, 2021
Berkeley Artificial Intelligence Research (BAIR), UC Berkeley, November, 2021

Selected Grants

MTR Research Funding Scheme, PI, 2024 (≈HK$1.5M)
CCF-Kuaishou Large Model Explorer Fund, PI, 2024 (Acceptance rate: 10.1%)
NSFC Young Scientists Fund, PI, 2024
Tencent Rhino-Bird Focused Research Program, PI, 2024

Teaching

Spring 2025: ELEC6910J Deep Reinforcement Learning
Fall 2024: ELEC2600 Probability and Random Process in Engineering