Haipeng Chen

Email: hchen23@wm.edu; haipengkeepon@gmail.com
Office: Integrated Science Center, Room 1261


Haipeng Chen is an assistant professor of data science at William & Mary. Previously, he was a CRCS postdoctoral fellow at Harvard University (with Milind Tambe), and a postdoc fellow in the Computer Science Department at Dartmouth College (with V.S. Subrahmanian). He obtained his Ph.D. from Interdisciplinary Graduate School (IGS), Nanyang Technological University (NTU), advised by Bo An and Yeng Chai Soh. He got the B.S. in Physics from University of Science and Technology of China (USTC).

His primary research interest lies in AI for social good. For AI techniques, he focuses on reinforcement learning, combinatorial optimization, and prediction. For social domains, he is interested in public health, environmental sustainability, cybersecurity, and problems with a network structure. His research has been recognized with the best paper nomination at AAMAS-2021, Innovation Demonstration Award runner-up at IJCAI-2019, Champion of the 2017 Microsoft Malmo Collaborative AI Challenge, and finalist of the Reinforcement Learning Competition track at KDD-2019. He has published in premier AI/data science conferences such as AAAI, IJCAI, NeurIPS, AAMAS, UAI, KDD, ICDM, and journals (e.g., IEEE/ACM Transactions, Transportation Research). He served as co-chair for workshops on the theme of AI and social impact, including the ICLR-2021 workshop on Synthetic Data Generation: Quality, Privacy, Bias, IJCAI-2021 workshop on AI for Social Good, and Harvard CRCS workshop on Using AI for Social Good. He regularly serves as a reviewer for premier AI conferences such as AAAI, IJCAI, AAMAS, NeurIPS, ICLR, and ICML. As part of his research agenda, he has collaborated with non-profits such as The Family Van, Mobile Health Map, Safe Place for Youth, and Wadhwani AI. His work has been covered by media such as The Wall Street Journal, Scientific American, Digital Guardian, ScienceBlog, AAAS, 雷锋网, and 凤凰网.

Prospective students

I am looking for highly motivated PhD/masters/undergraduate students to join my lab! If you want to work with me, send me an email with title [Prospective Students], and attach to the email with your 1) CV, 2) transcript, 3) representative publications (optional), and 4) a short intro of your research interest and plan, and why do you want to work with me.
PhD students: 2 fully funded PhD positions are available for Fall 2023.
RAs/interns: I am also looking for undergraduate and masters students to work with me as research assistants or interns.
Note: Due to large volumes of emails and limited bandwidth, unfortunately I won't be able to reply to all the emails. I will only respond when there is a vacancy and I think you will be a good fit to my lab.



  • [Call for abstracts] I am organizing a session on Reinforcement Learning Meets Combinatorial Optimization session at INFORMS 2023 this year (in Arizona). If your work is related, please consider submitting an abstract to me at hchen23@wm.edu. Deadline is 05/02/2023.
  • April, 2023 - Our paper "Complex Contagion Influence Maximization: A Reinforcement Learning Approach" is accepted by IJCAI 2023.
  • March, 2023 - Session proposal "Reinforcement Learning Meets Combinatorial Optimization" is accepted by INFORMS 2023 (Phoenix, AZ) AI Cluster.
  • February, 2023 - Paper "Predicting Micronutrient Deficiency with Publicly Available Satellite Data" is accepted by AI Magazine.
  • January, 2023 - Two papers accepted as extended abstract by AAMAS 2023.
  • October, 2022 - Attending INFORMS. Chairing the session "Reinforcement Learning for Decision Making in Networks and Combinatorial Spaces" on Oct 17.
  • October, 2022 - Selected as top 10% of the reviewers for NeurIPS 2022.
  • August, 2022 - Officially joined William & Mary!
  • April, 2022 - Paper "Sequential Vaccine Allocation with Delayed Feedback" is accepted by IJCAI 2022.
  • March, 2022 - Session proposal 'Reinforcement Learning for Decision Making in Networks and Combinatorial Spaces' is accepted by the AI cluster at INFORMS 2022.
  • December, 2021 - Giving a contributed talk at MLPH workshop @NeurIPS on "Demand prediction of mobile clinics using public data".
  • November, 2021 - Two papers, 'Using Public Data to Predict Demand for Mobile Health Clinics' and 'Micronutrient Deficiency Prediction via Publicly Available Satellite Imagery' are accepted by IAAI 2022!
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  • November, 2021 - Paper 'M2P2: Multimodal Persuasion Prediction using Adaptive Fusion' is accepted by IEEE Transactions on Multimedia.
  • September, 2021 - Our paper "Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning" is accepted by NeurIPS-21 as a spotlight!
  • September, 2021 - Our paper "PCAM: Predictive Cyber Alert Management" is accepted by ACM Transactions on Internet Technology.
  • August, 2021 - I am co-organizing the 3rd Workshop on Artificial Intelligence for Social Good@IJCAI2021.
  • May, 2021 - I am co-organizing the Synthetic Data Generation: Quality, Privacy, Bias workshop @ICLR2021.
  • May, 2021 - Two papers are accepted by UAI-21.
  • May, 2021 - Our paper, 'Active Screening for Recurrent Diseases: A Reinforcement Learning Approach' is awarded the best paper nomination by AAMAS-21!
  • March, 2021 - Our workshop proposal, 'International Workshop on Artificial Intelligence for Social Good (AI4SG)' is accepted by IJCAI'2021.
  • December, 2020 - Our paper, 'Active Screening for Recurrent Diseases: A Reinforcement Learning Approach' is accepted by AAMAS'2021.
  • December, 2020 - Our workshop proposal, 'Synthetic Data Generation: Quality, Privacy, Bias' is accepted by ICLR'2021 workshops.
  • December, 2020 - Our paper, 'EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation' is accepted by AAAI'2021.
  • September, 2020 - Organzing Harvard CRCS workshop on Using AI for Social Good!
  • August, 2020 - Our team AMI (Bo An, Haipeng Chen, Xu He, Rundong Wang, and Youzhi Zhang; alphabetically ordered) made it to the finalist (top 25 out of 1000+ teams) in the KDD'20 reinforcement learning competition track!
  • August, 2020 - Our paper, "Using Word Embeddings to Deter Intellectual Property Theft Through Automated Generation of Fake Documents" is accepted by ACM Transactions on Management Information Systems!
  • July, 2020 - Started postdoc at Harvard University!
  • May, 2020 - Our paper, "Learning Behaviors with Uncertain Human Feedback" got accepted to UAI'20. Congrats Xu on his first paper -- well deserved!
  • March, 2020 - Invited to serve as a reviewer of Neurips'20.
  • Feb, 2020 - Invited talk at CS department, University of Illinois at Chicago.
  • December 16, 2019 - Attending Matariki Cybersecurity Workshop.
  • December 8-14, 2019 - Attending Neurips'19 in Vancouver, Canada!
  • November, 2019 - Our paper, "Disclose or Exploit? A Game Theoretic Approach Towards Strategic Decision Making in Cyber Warfare" has been accepted to IEEE Systems Journal.
  • November, 2019 - Our paper, "PIE: A Data-Driven Payoff Inference Engine for Strategic Security Applications" has been accepted to IEEE Transactions on Computational Social Systems.
  • August, 2019 - Our IJCAI 2019 demo paper, "VEST: A System for Vulnerability Exploit Scoring & Timing" has been awarded IJCAI'19 Demonstration Innovation Award runner-up!
  • August, 2019 - Two papers accepted to ICDM 2019, including one regular paper which integrates contextual bandit with TD learning to handle the joint pricing and dispatch problem in ride-hailing platform, a work done when I was an intern at Didi; and a short paper jointly done with George Mason University, which is a follow-up of our KDD paper .
  • August, 2019 - Attending KDD'19 in Anchorage, Alaska!
  • May, 2019 - Attending ISTS VeChain BlockChain Technology Workshop.
  • May, 2019 - Three papers accepted to IJCAI 2019, including two papers on GAN-based tabular data generation with function-preservation and dynamic ETC control with multi-agent deep reinforcement learning accepted as main conference papers, and one demo paper demonstrating a system for vulnerability exploit timing and severity scoring prediction!
  • April, 2019 - Our paper, Using Twitter to Predict when Vulnerabilities will be Exploited, is accepted to KDD 2019 as poster presentation!
  • April, 2019 - Paper DCL-AIM: Decentralized Coordination Learning of Autonomous Intersection Management for Connected and Automated Vehicles has been accepted to Transportation Research, Part C, Emerging Methodologies!


C=Conference, J=Journal, D=Demo, W=Workshop, A=Arxiv preprint, P=Patent (pending)










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