Ling Pan
潘玲
Assistant Professor
Department of Electronic and Computer Engineering
Department of Computer Science and Engineering (by courtesy)
Hong Kong University of Science and Technology
Email: lingpan [@] ust [DOT] hk
I am 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). My research interests mainly include theoretical understanding, algorithmic improvements and practical application of generative flow networks (GFlowNets), reinforcement learning and multi-agent systems.
I focus on developing robust, efficient, and practical intelligent decision-making algorithms. I am also interested in the application of reinforcement learning and GFlowNets in practical problems like computational sustainability and drug discovery.
Prior to that, I was a postdoctoral fellow at Mila supervised by Prof. Yoshua Bengio (Turing Award Laureate). I received my Ph.D. from the Institute for Interdisciplinary Information Sciences (IIIS) (headed by Prof. Andrew Yao), Tsinghua University in 2022, advised by Prof. Longbo Huang. I received my B.E. from the School of Computer Science and Engineering, Sun Yat-Sen (Zhongshan) University, Guangzhou, China in 2017. During my Ph.D., I was fortunate to visit Stanford University advised by Prof. Tengyu Ma, University of Oxford advised by Prof. Shimon Whiteson, and I was a research intern in the Machine Learning Group at Microsoft Research Asia advised by Dr. Wei Chen. I was also a recepient of Microsoft Research Ph.D. 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, machine learning, deep reinforcement learning, generative flow networks, and multi-agent systems. I have several PhD/MPhil/RA openings starting in Spring 2025/Fall 2025 at HKUST. Please drop me an email with your CV (including rankings/publications) if you are interested.
Selected Awards
- 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, 2021
- Microsoft Research Ph.D. Fellowship (Asia), 2020
12 outstanding Ph.D. students in computer science in the Asia-Pacific region - China National Scholarship (top 2%), by Ministry of Education of China, 2016
- China National Scholarship (top 2%), by Ministry of Education of China, 2015
- China National Scholarship (top 2%), by Ministry of Education of China, 2014
Professional Activities
- Organizer:
- Safe Generative AI Workshop @ NeurIPS 2024
- Agent-based Information Retrieval Workshop @ SIGIR 2024
- GFlowNet Workshop @ Mila 2023
- Area Chair:
- International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2025
- PC member/Reviewer:
- International Conference on Learning Representations (ICLR), 2022-2024
- Conference on Neural Information Processing Systems (NeurIPS), 2021-2024
- International Conference on Machine Learning (ICML), 2021-2024
- AAAI Conference on Artificial Intelligence (AAAI), 2021
- Transactions of Machine Learning Research (TMLR)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Selected Talks
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Towards Robust, Efficient, and Practical Decision-Making: From Reward-Maximizing Deep Reinforcement Learning to Reward-Matching GFlowNets
RLChina 2023 Conference, November, 2023
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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
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Regularized Softmax Deep Multi-Agent Q-Learning
Reinforcement Learning China Community (RLChina), May, 2022
AI Time NeurIPS Session (by Tsinghua University), February, 2022
Third International Conference on Distributed Artificial Intelligence, January, 2022
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Softmax Deep Double Deterministic Policy Gradients
IJCAI-Shanghai Artificial Intelligence Industry Association (SAIA) Young Elite Symposium, July, 2021
Second International Conference on Distributed Artificial Intelligence, October, 2020
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A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
First International Conference on Distributed Artificial Intelligence, October, 2019
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
- Fall 2024: ELEC2600 Probability and Random Process in Engineering
- Spring 2024: ELEC6950 Departmental Seminar