Haipeng Chen

Biography

Haipeng Chen is an assistant professor of data science at William & Mary. He directs the W&M Data-Driven Decision Intelligence (D^3i) Lab and serves as the vice chair of the AI Blue Ribbon Collaborative at Center for Telehealth and eHealth Law (CTeL). Previously, he was a CRCS postdoctoral fellow of Computer Science at Harvard University (with Milind Tambe), and a postdoc fellow of Computer Science 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 the University of Science and Technology of China (USTC).

His primary research interest lies in Use-Inspired AI. For AI techniques, he focuses on reinforcement learning, generative AI, and optimization. For application domains, he is interested in health, environment, and physical sciences. 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, ICLR, 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, AIGC-2023 workshop on Reinforcement Learning in the Real World, 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 With Good Reason (radio interview), The Wall Street Journal, Scientific American, Digital Guardian, ScienceBlog, AAAS, 雷锋网, and 凤凰网.

Prospective students

I am constantly looking for highly motivated PhD students (as fully-funded TAs or RAs), master's and undergraduate students (as RAs or remote interns) to join my lab! If you want to work with me, send me an email with the title [Prospective XX Students], where XX is PhD/master's/undergraduate depending on what you apply for. For an informative application, please attach to the email your 1) CV, 2) transcripts for both undergrad and master's (if applicable), and 3) representative publications (optional).
Note: Due to large volumes of emails and limited bandwidth, I won't be able to reply to all the emails. I will respond when there is a vacancy and I think you will be a good fit for my lab (usually within two weeks).

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