Hi! I am a Postdoctoral Scholar working with Professor Nigam Shah on AI for Healthcare at Stanford University. I am also fortunate to receive mentorship from Professor Sanmi Koyejo on Trustworthy Machine Learning. I received my Ph.D. in Computer Science at Emory University, advised by Professor Carl Yang and worked closely with Professor Joyce C Ho on ML for Health. In the past summers, I interned at Microsoft Research and Amazon Science.

Research Interests: My research interests focuses on Large Language Model (LLM) Evaluation and Post-training for Healthcare. I’m happy to mentor a few students on research and especially encourage students from underrepresented groups to reach out. If you are a junior student and want to discuss research and/or look for advice, feel free to schedule a chat via this link.

Previously, I got 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 Professor Lin Zhang. In addition, I am fortunate to work with Professor Tianwei Yu on machine learning for bioinformatics. I also spent a wonderful summer working with Professor Gabor Fichtinger at Queen’s University in Canada through the Mitacs program.

News

• [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.