Yifei Sun

Machine learning for healthcare

Yifei Sun

MSCS, Stanford University

I am Yifei Sun, an incoming Stanford MSCS student working on machine learning for healthcare, clinical time series, and EHR representation learning.

I completed my undergraduate studies at the University of Toronto. I conducted research under Prof. Rahul G. Krishnan on healthcare AI, generalizable representation learning, and EHR data analysis; with Prof. Sheila McIlraith on reinforcement learning; and Prof. Ashton Anderson on knowledge graphs.

Previously, I spent a year at Cerebras Systems working on compilers as a stack engineer. Before that, I interned at Cannacord Genuity.

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Publications

Publications

  1. Can we generate portable representations for clinical time series data using LLMs?

    Zongliang Ji*, Yifei Sun*, Andre Amaral, Anna Goldenberg, Rahul G. Krishnan. The Fourteenth International Conference on Learning Representations (ICLR 2026).

    PDF

  2. Afriinstruct: Instruction tuning of african languages for diverse tasks

    Kosei Uemura, Mahe Chen, Alex Pejovic, Chika Maduabuchi, Yifei Sun, En-Shiun Annie Lee. Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 13571–13585.

    PDF

* Co-first author

Research

Research Interests

Machine Learning for Health

Building models that reason over noisy, heterogeneous, and clinically meaningful data, with a focus on cross-domain generalization in healthcare settings.

Clinical Foundation Models

Developing robust foundation models for clinical data that support privacy-preserving, cross-institutional training and deployment.

AI Measurement Science

My latest research interests! Designing better ways to evaluate and measure AI performance in clinical applications.

News

Recent Updates

Enjoying my summer!

Attended ICLR 2026 in Rio de Janeiro.

Accepted to Stanford University MSCS program.

First paper accepted to ICLR 2026.

Bio

About Me

I made several pivots in my academic and professional journey.

Finance: I began in financial mathematics (MAEF at UofT), but my internship, even though it was a great experience, showed me I could pursue a different path.

SWE: I spent a rewarding year at Cerebras Systems working on compilers, yet the rapid rise of LLMs made me reconsider whether a traditional software engineering track was the future I wanted.

Research: I moved into research, and while the process has been challenging, it has been deeply inspiring to develop new methods and earn acceptance at top-tier venues. I am driven to contribute work that can make a real impact.