Tengfei Ma (马腾飞)

Tengfei Ma (马腾飞)

Assistant Professor, Department of Biomedical Informatics, Stony Brook University

Tengfei Ma (马腾飞)

Assistant Professor of Biomedical Informatics, Stony Brook University

I am an assistant professor in the Department of Biomedical Informatics, with affiliations in Computer Science and AI Innovation Institute (AI3) at Stony Brook University.

Previously, I was a staff research scientist at IBM T. J. Watson Research Center (2016–2023) and a researcher at IBM Research–Tokyo (2015–2016). I received my Ph.D. from the University of Tokyo, M.S. from Peking University, and B.E. from Tsinghua University.

My research interests include machine learning, natural language processing and biomedical informatics, with a current focus on deep graph learning. Profiles: BMI · AI Institute · Google Scholar

Email: Tengfei.Ma (at) stonybrook (dot) edu  or  Tengfei.Ma (at) stonybrookmedicine (dot) edu

Research directions

Graph Neural Networks Scalable and dynamic graph learning, graph foundation models, and learning with geometry and topology. Time Series Learning Interpretable models, shape-based representations, foundation models, and causality-aware anomaly detection. Language & Intelligence Language model transparency and trustworthiness, summarization, representation learning, and AI for code. AI for Healthcare Electronic health records, medication recommendation, drug discovery, and computational wound healing.

More about my research →

News & updates

  • 10/2026NewGlad to give an invited talk on “Seeing Structure in Time: Interpretable, Generalizable, and Causality-Aware Time Series Learning” at NJIT.
  • 05/2026NewTwo papers are accepted by KDD, one is accepted by AMIA as a poster.
  • 04/2026NewOne paper about LLM activation sparsification is accepted by ICML 2026.
  • 04/2026NewI co-organized the workshop “Principled Design for Trustworthy AI - Interpretability, Robustness, and Safety across Modalities” at ICLR 2026.
  • 04/2026NewOne paper about hyperbolic EHR modeling for EHR-QA is accepted by ACL 2026 (Findings).
  • 02/2026New“Graph Concept Bottleneck Models” is accepted by TMLR.

All news →

Let's connect

Interested in working together?

I welcome collaborations with motivated students and researchers with strong backgrounds in machine learning and mathematics.

In SBU, I take students from CS, BMI and AMS. Students from any of these departments are welcome to contact me regarding collaboration opportunities or RA positions. For master students, unfortunately I do not have RA positions for you, but you are welcome to do an independent study (e.g. CSE593) in my lab for credits.