Hejie Cui

Postdoctoral Scholar

Stanford University

Biography

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.

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

Selected Publications

Hejie Cui*, Lingjun Mao*, Xin Liang, Jieyu Zhang, Hui Ren, Quanzheng Li, Xiang Li, Carl Yang (2024). Biomedical Visual Instruction Tuning with Clinician Preference Alignment. The Conference on Neural Information Processing Systems (NeurIPS).

PDF Code Dataset Project Source Document

Yanbang Wang, Hejie Cui, Jon Kleinberg (2024). Microstructures and Accuracy of Graph Recall by Large Language Models. The Conference on Neural Information Processing Systems (NeurIPS). Abstract version accepted to International Conference on Computational Social Science (IC2S2) (Oral).

Hejie Cui, Zhuocheng Shen, Jieyu Zhang, Hui Shao, Lianhui Qin, Joyce C Ho, Carl Yang (2024). LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction. American Medical Informatics Association (AMIA) Annual Symposium (Oral).

Ziyang Zhang, Hejie Cui, Ran Xu, Yuzhang Xie, Joyce C Ho, Carl Yang (2024). TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data. ACM International Conference on Knowledge Discovery and Data Mining (KDD).

Hejie Cui, Xuan Kan, Xiaoxiao Li, Ying Guo, Lifang He, Liang Zhan, Carl Yang (2024). Brain Network Analysis with Graph Neural Network. Tutorial at the IEEE International Symposium on Biomedical Imaging (ISBI).

Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, Wei Jin, Joyce C Ho, Carl Yang (2023). Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models. Annual Meeting of the Association for Computational Linguistics (ACL-Findings).

Hejie Cui*, Xinyu Fang*, Zihan Zhang, Ran Xu, Xuan Kan, Xin Liu, Manling Li, Yangqiu Song, Carl Yang (2023). Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting. Neural Information Processing Systems (NeurIPS).

Hejie Cui, Jiaying Lu, Shiyu Wang, Ran Xu, Wenjing Ma, Shaojun Yu, Yue Yu, Xuan Kan, Chen Ling, Tianfan Fu, Liang Zhao, Joyce C Ho, Fei Wang, Carl Yang (2023). A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises. arXiv preprint.

Xuan Kan, Zimu Li, Hejie Cui, Yue Yu, Ran Xu, Shaojun Yu, Zilong Zhang, Ying Guo, Carl Yang (2023). R-Mixup: Riemannian Mixup for Biological Networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).

Hejie Cui, Rongmei Lin, Nasser Zalmout, Chenwei Zhang, Jingbo Shang, Carl Yang, Xian Li (2023). PV2TEA: Patching Visual Modality to Textual-Established Information Extraction. Annual Meeting of the Association for Computational Linguistics (ACL-Findings).

Yi Yang, Hejie Cui, Carl Yang (2023). PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis. The Conference on Health, Inference, and Learning (CHIL) (Oral).

Hejie Cui, Xuan Kan, Xiaoxiao Li, Lifang He, Liang Zhan, Ying Guo, Carl Yang (2023). Brain Network Analysis with Graph Neural Network. Tutorial at the International Conference on Intelligent Biology and Medicine (ICIBM).

Ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce C Ho, Chao Zhang, Carl Yang (2023). Neighborhood-regularized Self-Training for Learning with Few Labels. The AAAI International Conference on Artificial Intelligence (AAAI) (Oral).

Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang (2022). BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks. IEEE Transactions on Medical Imaging (TMI) (IF: 11.037).

Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, Carl Yang (2022). Brain Network Transformer. The Conference on Neural Information Processing Systems (NeurIPS) (Spotlight).

Hejie Cui, Zijie Lu, Pan Li, Carl Yang (2022). On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs. The International Conference on Information and Knowledge Management (CIKM) (Most Influential CIKM Paper of 2022).

Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang (2022). Interpretable GNNs for Connectome-Based Brain Disorder Analysis. The International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (Oral).

Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang (2022). FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation. The International Conference on Medical Imaging with Deep Learning (MIDL) (Oral).

Hejie Cui, Jiaying Lu, Yao Ge, Carl Yang (2022). How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation. The European Conference on Information Retrieval (ECIR).

Xuan Kan, Hejie Cui, Carl Yang (2021). Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML).

Hejie Cui, Xinglong Liu, Ning Huang (2019). Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images. The International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).

Experience

Research Intern

Microsoft Research

May 2023 – Aug 2023 Redmond, Washington, United States

Worked on graph recommendation in the joint supervision of augmented learning and reasoning team, office of applied AI research, and MSAI.

Applied Scientist Intern

Amazon

May 2022 – Aug 2022 Seattle, Washington, United States

Worked on multimodality knowledge extraction in the product knowledge graph team.

Mitacs Global Research Intern

Queens University

Jul 2018 – Oct 2018 Kingston, Ontario, Canada

Visited Perk Lab and worked on video detection for surgery training with Prof. Gabor Fichtinger.

Microsoft University Summer Camp

Microsoft Research Asia

Aug 2017 – Aug 2017 Beijing, China

our project on prototyping workflow for HoloLens mix-reality got the best work award of Microsoft student summer camp hackathon.

SAP Student Training and Rotation Program

SAP

Jun 2017 – Aug 2017 Shanghai, China

Worked on ABAP database.

Selected Accomplishments

Rising Star of Michigan AI Symposium, 2024
Doctoral Forum of the Conference on Health, Inference, and Learning, 2024
Rising Star in EECS, 2023
KDD Student Travel Grant Award, 2023
NeurIPS AI4Science Travel Award, 2022
NSF Student Travel Grant Award for CIKM, 2022
Laney Graduate Student Council Research Grant, Emory University, 2022
MICCAI Student Travel Grant Award, 2022
Award for CRA-WP Grad Cohort for Women, 2021
Mitacs Globalink Research Award, 2018
Valedictorian of the School of Software Engineering Class 2019, Tongji University, 2019
Outstanding Graduate Award, the City of Shanghai, 2019
Outstanding Graduate Award, Tongji University, 2019
Tongji University Programming Competition, Silver Prize, 2017
National Scholarship, 2016, 2017, 2018

Talks

Towards Trustworthy Foundation Model Adaptation and Evaluation for Healthcare
University of Michigan AI Symposium. Oct 2024
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
The University of British Columbia. Oct 2024
Advancing Healthcare with Multimodal Structural Knowledge
University of California, Los Angeles. Oct 2023
Stanford University. Nov 2023
Massachusetts General Hospital, Harvard Medical School. Nov 2023
Zhejiang University. Dec 2023
Shanghai Jiao Tong University, Fudan University, Tongji University. Dec 2023
Peking University. Jan 2024
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
University of Rochester. Apr 2021
Effective and Interpretable Graph Neural Networks for Graphs Constructed from Multimodality Data
Microsoft Search, Assistant and Intelligence (MSAI). Apr 2021

Services

Program Committee Member / Reviewer:
- Conference: ACL'24, ICML'24, ICLR'24; NeurIPS'23; ML4H'23; CHIL'24,23; SIGKDD'23,22; ICDM'22; WWW'22; AAAI'23,22,21; SDM'22; LOG'22; MICCAI'23,22,20;
- Journal: TKDE, TMI, IEEE BigData, Health Data Science
- Workshop: Interpretable Machine Learning in Healthcare ICML'22,21; Computer Vision for Automated Medical Diagnosis ICCV'21; Medical Imaging meets NeurIPS'21; Temporal Graph Learning NeurIPS'22; Graph-Based Natural Language Processing COLING'22
Organizer:
- Neural Networks for Brain Connectome Analysis: Theories, Methods, and Applications Workshop IEEE BigData'22
- Federated Learning with Graph Data Workshop CIKM'22
Volunteer:
- Conference: CHIL'23; CIKM'22; ICML'21,20
Teaching:
- CS 253 Data Structures and Algorithms, TA (Spring 2021)
- CS 584 Natural Language Processing for Biomedical Applications, Head TA (Fall 2020)
- CS 584 Deep Learning, Head TA (Spring 2020)
Leadership: Chair, Microsoft Student Club, Tongji University (2017-2018)