Hejie Cui

Hejie Cui

Doctoral Student in CS

Emory University


Hi! I am a postdoctoral fellow at Stanford University Shah Lab. I received my Ph.D. in Computer Science and Informatics at Emory University, advised by Prof. Carl Yang. I have also been working closely with Prof. Joyce C Ho on healthcare applications. In the past summers, I interned at Microsoft Research and Amazon Science.

Research Interests: My research focuses on data-centric methods on adapting LLMs/VLLMs for healthcare and domain-specialized LLM alignment. I am open to discussing research ideas and seeking academic collaborations. Drop me an email if interested!

Previously, I got my bachelor’s degree (Valedictorian, GPA Ranking: 1/164) in Software Engineering at Tongji University. During my undergraduate, I worked on computer vision for medical imaging with Prof. Lin Zhang. In addition, I worked with Prof. Tianwei Yu on machine learning for bioinformatics. I also spent a wonderful summer at Prof. Gabor Fichtinger‘s Perk Lab at the School of Computing, Queen’s University in Kingston, Canada through the Mitacs program.


  • [2024.06] Our work on multi-agent LLM reasoning for EHR-based few-shot disease prediction is accepted to AMIA 2024 Annual Symposium. See you in San Francisco!
  • [2024.06] Our work on multimodal instruction-tuning for biomedicine is online. Check it out.
  • [2024.05] Our collaborative work on disease subtyping based on EHR data is accepted to SIGKDD Conference on Knowledge Discovery and Data Mining Applied Data Science Track.
  • [2024.04] Our collaborative work on network recall by large language models is accepted to International Conference on Computational Social Science 2024 IC2S2'24 as an Oral.
  • [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! This annual report is generated by the Council of Economic Advisers in the White House to present an overview of the nation’s economic progress and makes the case for the Biden-Harris Administration’s economic policy priorities.
  • [2024.01] We will be conducting a new tutorial collaborativefor BrainGB at the ISBI 2024 conference in Athens, Greece at the end of this May.
  • [2024.01] A collaborative work is accepted to SPIE Medical Imaging 2024 (Oral).
  • [2023.12] Glad to received NSF Student Travel Support Award for the ICDM 2023.
  • [2023.09] Our paper on artificial node features for GNNs has been consistently ranked as 1st in the 3 consecutive lists of Most Influential CIKM Papers produced by Best Paper Digest ( 2023-01, 2023-04, 2023-09).
  • [2023.09] One paper on visual knowledge extraction is accepted to NeurIPS'23.
  • [2023.09] Two papers are accepted to PSB'24. Congrats to Alexis (High-Schooler)!
  • [2023.08] Humbled to be selected as a Rising Star in EECS this year!
  • [2023.06] Our review paper on healthcare knowledge graph is now online.
  • [2023.05] One collaborative paper on biological data augmentation is accepted to KDD'23.
  • [2023.05] One paper on multimodal extraction is accepted to ACL'23 Findings.
  • [2023.04] Our paper on GNN pre-training on brain networks is accepted to CHIL'23 (Oral).
  • [2022.11] One collaborative paper on few-shot learning is accepted to AAAI'23 (Oral).
  • [2022.11] Glad to receive NeurIPS AI4Science Travel Award!
  • [2022.10] One Benchmark paper on Graph Neural Networks for brain networks has now been officially accepted to IEEE TMI.
  • [2022.09] One collaborative paper on transformer is accepted to NeurIPS'22 (Spotlight).
  • [2022.08] One paper on GNN node feature choices is accepted to CIKM'22.
  • [2022.06] One paper on interpretable GNNs is accepted to MICCAI'22 (Oral).
  • [2022.06] One collaborative paper on meta-learning is accepted to KDD'22 (Health Day).


  • AI for Health
  • Multimodality Machine Learning
  • Brain Networks Analysis
  • Data Science and Mining


  • Ph.D. in Computer Science, 2019-now

    Emory University

  • B.Eng. in Software Engineering, 2015-2019

    Tongji University



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


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


Jun 2017 – Aug 2017 Shanghai, China
Worked on ABAP database.

Selected Accomplishments

  • Rising Stars 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


  • 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


  • 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)