Pengfei Hu

Department of Computer Science, Stevens Institute of Technology

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Gateway South 433

1 Castle Point Terrace, Hoboken, NJ 07030

My name is Pengfei Hu. I’m a 3rd year Ph.D. student in Department of Computer Science at Stevens Institute of Technology, advised by Prof. Yue Ning. Before that, I obtained my master’s degree from the Viterbi School of Engineering, University of Southern California in 2023, where I worked with Prof. Sze-chuan Suen.

My current research concentrates on predictive healthcare on electronic health records (EHR), with a specific focus on augmented (e.g. knowledge-guided and retrieval augmented) language models and their robustness (e.g. generalization and transparent adaptation) under domain shifts. I have also worked on graph neural networks, LLM Agents, and Tool-Integrated Reasoning designs.

Feel free to drop me an email (phu9 at stevens dot edu) if you have any questions about my research, or want to discuss about potential collaborations.

Educations

Stevens Institute of Technology (2023 - Present)
Ph.D. in Computer Science
GPA: 3.95/4.00
Research Focus: EHR modeling, Clinical NLP, Large Language Models.
Advisor: Prof. Yue Ning

University of Southern California (2021 - 2023)
M.S. in Industrial System Engineering
GPA: 4.00/4.00
Research Focus: Anomaly Detection by Wearable Sensor Devices.
Advisor: Prof. Sze-chuan Suen

Nanjing University of Finance and Economics (2016 - 2020)
B.S. in Economic Statistics
GPA: 3.67/4.00
Advisor: Prof. Qinghai Li
Interned at Alibaba Group (2021), Tencent (2020), Didi Global (2019–2020), and PwC Consulting (2019).

Industrial Experience

Oak Ridge National Laboratory (May 2025- Aug. 2025)
Graduate Research Intern, Computational Sciences and Engineering Division
Topic: Reservoir Inflow Forecasting. [Two Workshop and One Journal papers]
Mentor: Fan Ming

News

Jun 16, 2026 I will be joining the AWS AI Lab as an Applied Scientist Intern this fall!
May 04, 2026 Our paper II-KEA won the Best Doctoral Research Poster Award at the iCNS @ Stevens AI Engineering and Science Symposium.
May 01, 2026 Our paper Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference is accepted to ICML 2026. See you in Seoul!
Apr 17, 2026 I passed my Thesis Proposal defense.
Feb 27, 2026 Our paper AGFormer: Adaptive Spatiotemporal graph informed transformer for multi-reservoir inflow forecasting is accepted to Environmental Modelling & Software (IF: 4.6).

Selected Publications

  1. ICML 2026
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    Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference
    Pengfei Hu, Chang Lu, Feifan Liu, and 1 more author
    Accepted by the 43rd International Conference on Machine Learning (ICML), 2026
  2. arXiv
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    UdonCare: Hierarchy Pruning for Unseen Domain Discovery in Predictive Healthcare
    Pengfei Hu, Xiaoxue Han, Fei Wang, and 1 more author
    arXiv preprint arXiv:2506.06977, 2025
  3. IEEE JBHI
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    Bridging Stepwise Lab-Informed Pretraining and Knowledge-Guided Learning for Diagnostic Reasoning
    Pengfei Hu, Chang Lu, Fei Wang, and 1 more author
    IEEE Journal of Biomedical and Health Informatics, 2026
  4. Elsevier EMS
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    AGFormer: Adaptive Spatiotemporal graph informed transformer for multi-reservoir inflow forecasting
    Ming Fan, Pengfei Hu, Xiaoxue Han, and 4 more authors
    Environmental Modelling & Software, 2026
  5. EMNLP 2025
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    No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery
    Xiaoxue Han, Pengfei Hu, Jun-En Ding, and 3 more authors
    In Findings of the Association for Computational Linguistics: EMNLP 2025, 2025