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
Industrial Experience
- Amazon Web Services (
Sep. 2026 - Dec. 2026 ) - Applied Scientist Intern
- Topic: Agentic AI (function calling, computer-use agents).
- 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
| Aug 10, 2026 | Our paper Discovering Hierarchy-Grounded Domains for Clinical Domain Generalization has been accepted to CIKM 2026 as a full research paper! |
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| 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. |
Selected Publications
- CIKM 2026
Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain GeneralizationAccepted by the 35th ACM International Conference on Information and Knowledge Management (CIKM), 2026