Hejie Cui

Doctoral Student in CS

Emory University

Biography

Hi there! This is Hejie Cui (pronounced as “Her-jay Tsuee”, 崔鹤洁 in Chinese). I am currently a final-year PhD student in Computer Science 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 lies at the intersection of machine learning, data mining, and health informatics. I aim to leverage advanced AI techniques to create innovative solutions that can support data-driven decision-making in healthcare.

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.

News

[2024.04] Happy to be selected for a poster presentation at the CHIL Doctoral Symposium!
[2024.03] Our work on multi-agent LLM reasoning for few-shot disease prediction using electronic health records is available online.
[2024.03]: our survey on Domain Speciliazation of LLM is honorably mentioned by the 2024 Economic Report of the President from the White House.
[2024.02] Our new paper on analyzing LLM behavior on graph recall from human cognition perspective is now online. Check it out here!
[2024.01] We will be conducting a new tutorial for BrainGB at the ISBI 2024 conference in Athens, Greece at the end of this May.
[2024.01] An extended abstract 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).

Interests

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

Education

Ph.D. in Computer Science, 2019-now
Emory University
B.Eng. in Software Engineering, 2015-2019
Tongji University

Selected Publications

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. arXiv preprint.

Yanbang Wang, Hejie Cui, Jon Kleinberg (2024). Microstructures and Accuracy of Graph Recall by Large Language Models. arXiv preprint. Abstract version accepted to International Conference on Computational Social Science (IC2S2) 2024.

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. arXiv preprint.

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 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

Talks

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)