Hello! I am a
Postdoctoral Researcher at
Stanford University, advised by Prof.
Nigam H. Shah and Prof.
Sanmi Koyejo. I work on Large Language Models (LLMs) Post-training and Evaluation for Healthcare. I received my Ph.D. in
Computer Science at Emory University, advised by Prof.
Carl Yang and worked closely with Prof.
Joyce C Ho on ML for Health. During my PhD, I completed internships at Microsoft Research and Amazon Science.
I obtained my bachelor’s degree (Valedictorian, GPA Ranking: 1/164) in Software Engineering at
Tongji University, where I did my undergraduate thesis research on computer vision supervised by Prof.
Lin Zhang. In addition, I am fortunate to work with Prof.
Tianwei Yu on machine learning for bioinformatics. I also spent a wonderful summer working with Prof.
Gabor Fichtinger at Queen’s University in Canada through the
Mitacs program.
News
- [2025.05] Our work
CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models is accepted to
ICML 2025. Congrats Wei!
- [2025.03] Our work 𝗧𝗜𝗠𝗘𝗥⌛️: a temporal instruction modeling and evaluation framework for longitudinal clinical records is available online. Check out the preprint
here.
- [2025.03] We build 𝗠𝗲𝗱𝗛𝗘𝗟𝗠✨: a comprehensive benchmark evaluating AI on realistic clinical tasks that healthcare professionals 👩⚕️⚕️ perform daily instead of just medical exams. Check out our
HAI blogpost and MedHELM
leaderboard for more details.
- [2025.02] We release three de-identified, longitudinal EHR datasets from Stanford:
more details — now freely available for non-commercial research-use worldwide.
- [2025.02] Pleased to share that our grant proposal 𝗖𝘂𝗿𝗮𝗕𝗲𝗻𝗰𝗵 has been selected for funding through by the
Stanford RAISE Health Seed Grant Program and
Stanford HAI. A big thank you to all the collaborators for their support!
- [2024.12] Served as a Junior Chair at the Foundations Models and Multimodal AI Round Table at ML4H 2024 Symposium. The topic summary can be found
here.
- [2024.09] Our work on clinician preference aligned synthetic instruction generation for visual instruction tuning is accepted to
NeurIPS'24 Datasets and Benchmarks Track.
- [2024.09] Our work on network recall by large language models is accepted to
NeurIPS'24 Research Track. Abstract version is presented on
IC2S2'24 as an Oral.
- [2024.08] Excited to be selected as a Rising Star Spotlight Speaker to give a talk about my research at
University of Michigan 2024 AI Symposium.
- [2024.06] Our work on multi-agent LLM reasoning for EHR-based few-shot disease prediction is accepted to
AMIA Annual Symposium as an Oral.
- [2024.05] Our work on disease subtyping is accepted to
KDD Applied Data Science Track.
- [2024.04] My PhD Work is selected to the
CHIL Doctoral Symposium. Thank you CHIL!
- [2024.04] I successfully defended my dissertation. Officially, Dr. Cui!
- [2024.03] Our survey paper on LLM domain specialization is cited by the
2024 Economic Report of the President!
- [2023.12] Glad to received NSF Student Travel Support Award for the ICDM 2023.
- [2023.09] Our paper on artificial node features on non-attributed graphs has been selected as Most Influential CIKM Papers produced by
Best Paper Digest.
- [2023.09] One work on visual knowledge extraction is accepted to
NeurIPS'23.
- [2023.09] Two work are accepted to
PSB'24. Congrats to Alexis (High-Schooler)!
- [2023.08] Humbled to be selected as a
2023 EECS Rising Star!
- [2023.05] One work on biological data augmentation is accepted to
KDD'23.
- [2023.05] One work on multimodal extraction is accepted to
ACL'23 Findings.
- [2023.04] Our work on brain network pre-training is accepted to
CHIL'23 as an Oral.
- [2022.11] Our paper on few-shot learning is accepted to
AAAI'23 as an Oral.
- [2022.11] Glad to receive
NeurIPS AI4Science Travel Award!
- [2022.10] Our Benchmark paper on Graph Neural Networks for brain networks has now been officially accepted to
IEEE TMI.
- [2022.09] Our paper on brain transformer is accepted to
NeurIPS'22 as an Spotlight.
- [2022.08] One paper on node feature for non-attributed graphs is accepted to
CIKM'22.
- [2022.06] One paper on interpretable GNNs is accepted to
MICCAI'22 as an Oral.
Interests
- Large Language (Vision) Models
- Data Mining and Data Science
- Multimodality Learning
- AI for Health
Education
Ph.D. in Computer Science, 2019-2024
Emory University
B.Eng. in Software Engineering, 2015-2019
Tongji University