Biography.

Research Directions. My research focuses on AI for health, with the goal of developing trustworthy and translational AI to address biomedical challenges.

1. Trustworthy LLMs in Healthcare: I develop methods to improve explainability, uncertainty quantification, and risk control for biomedical LLMs and multi-agent clinical systems.
2. Foundation Models for Longitudinal Health Data: I design foundation models for irregular and longitudinal healthcare data, including electronic medical records, laboratory measurements, and biosignals.
3. Interpretable AI for Population Health: I develop probabilistic models and scalable statistical inference methods to learn interpretable representations from electronic health records and genotype data, supporting phenotyping, incidence prediction, and gene discovery.
4. Generative AI for Biology: I develop foundation models for irregular single-cell RNA-seq time-series data and study domain adaptation methods that leverage unlabeled data.

Lab Opening. We are hiring Ph.D. students and research interns interested in AI (e.g., LLMs, RL，Agents) and biomedical applications!

Collaboration with Me. I am open to external opportunities for invited talks, research collaborations, and part-time consulting employment.

---

✈ News and Travel

[Apr 2026] I presented an oral talk, Toward Trustworthy LLMs in Healthcare with Medical Ontologies, at the OSC Research Symposium & NSF AI Workshop.

[Mar 2026] I presented an oral talk and poster, Toward Trustworthy LLMs in Healthcare with Medical Ontologies, at the 2026 Global AI Youth Forum.

[Jan 2026] PheCode-guided multi-modal topic modeling of electronic health records improves disease incidence prediction and GWAS discovery from UK Biobank is accepted at Briefings in Bioinformatics!

[Dec 2025] I was awarded the NVIDIA Academic Grant Program Award (Fall 2025).

[Dec 2025] SMI: Semantic Medical ID for Hierarchy-Aware Concept Representation is accepted at the NeurIPS 2025 ResponsibleFM Workshop.

[Oct 2025] Our TrajGPT work is accepted at IEEE Journal of Biomedical and Health Informatics (J-BHI).

[Sep 2025] Our TimelyGPT work is accepted at journal Health Information Science and Systems.

[Aug 2025] I joined School of EECS at Ohio University as Assistant Professor.

[May 2025] I Completed my Ph.D. degree at McGill University.

[Nov 2024] Our TimelyGPT work at ACM BCB won the Risking Star Award.

[Oct 2024] TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis will appear at Neurips 2024 workshop TSALM.

[Sep 2024] TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare will appear at ACM-BCB with oral presentation!

[Sep 2024] I present an abstract of the work at the CARTaGENE group in Scientific Meeting of the Canadian Translational Geroscience Network.

[Aug 2024] BiTimelyGPT: Bidirectional Generative Pre-training for Improving Healthcare Time-series Representation Learning published at MLHC 2024 as a poster paper!

[Apr 2024] I present a tutorial of my KDD work MixEHR-Seed in Workshop - Genomic Medicine, Therapeutics and Health.

[May 2023] I present an abstract of MixEHR-SAGE work in ISMB GLBIO 2023.

[Apr 2023] I receive financial support from Quebec Government via Quebec Doctoral Scholarship (FRQNT) !

[May 2022] MixEHR-Seed: Automatic phenotyping by a seed-guided topic model published at KDD and won health day best paper award!

---

🏅 Honors and Awards

Grant

• NVIDIA Academic Grant Program Award, 2025

Award

• ACM BCB Rising Scholar Award, 2024
• KDD Health Day Best Paper Award, 2022

As Ph.D.

• Quebec Doctoral Scholarship FRQNT, 2023
• Faculty of Science Grad Supplement Award, 2022
• Jackie Cheung Graduate Award, 2020
• SOCS Grad Stimulus Initiative Award, 2020
• Grad Excellence Award in Computer Science, 2020
• McGill PhD Stipend, 2020