Haipeng Chen

Email: hpchen@seas.harvard.edu; haipengkeepon@gmail.com

I’m on the job market for faculty positions.

Bio: Haipeng Chen is a CRCS postdoctoral fellow at Harvard University from July 2020, working with Professor Milind Tambe. Previously he did his first postdoc in the Department of Computer Science, Dartmouth College, where he worked with Professor V.S. Subrahmanian. He obtained his Ph.D. from Interdisciplinary Graduate School (IGS), Nanyang Technological University (NTU) in 2018, where he was advised by Professor Bo An. He got the B.S. in Physics from University of Science and Technology of China (USTC) in 2013.

His primary research interest lies in AI for social impact. For AI techniques, he focuses on reinforcement learning, combinatorial optimization, and prediction. For social domains, he is interested in public health, cybersecurity, and transportation, particularly in 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.



  • 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.
  • November, 2021 - Our 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.


Note: all my papers with Professor V.S. Subrahmanian are in alphabetical order, where † means I am a lead/co-lead author. Authors of other papers are ordered according to contribution unless otherwise specified.

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









Professional Service