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. My research focuses on the integration of multimodal data mining and knowledge-empowered interpretable model for health informatics. Check my CV for more details. Lately, I am interested in the evaluation of large multimodal models for health and exploring data-centric AI approaches for foundational health models. If our research interests align, I would be delighted to discuss potential collaborations. Please don’t hesitate to reach out via message or email.

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

[2023.09] One paper on visual knowledge extraction with multimodality model prompting is accepted to NeurIPS'23. Thanks to my collaborators.
[2023.09] Two papers on brain network analysis are accepted to PSB'24. Congrats to Alexis (High-Schooler)!
[2023.08] Selected to participate in the 2023 EECS Rising Stars Workshop!
[2023.07] One collaborative paper on dynamic graph is accepted to IEEE-BHI'23.
[2023.06] Our survey paper on healthcare knowledge graph resources, applications, and promises is now accessible online. To be appeared at ICML-IMLH'23.
[2023.05] One collaborative paper on biological data augmentation is accepted to KDD'23.
[2023.05] One paper on multimodality information extraction is accepted to the Findings of ACL'23 and selected as Oral at the KDD Workshop on Knowledge Augmented Methods for NLP (KnowledgeNLP-KDD23).
[2023.04] Our paper on GNN pre-training on brain networks is accepted to CHIL'23 (Oral). Glad to receive a Travel Grant! See you in Boston in June.
[2023.02] I will join the Augmented Learning and Reasoning Group at Microsoft Research Redmond as a Research Intern in Summer 2023.
[2023.01] Two collaborative papers on structure learning got accepted to ISBI'23.
[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 (IF: 11.037).
[2022.09] One collaborative paper on transformer is accepted to NeurIPS'22 (Spotlight).
[2022.08] One paper on GNN node features 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, 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, Joyce Ho, Fei Wang, Carl Yang (2023). A Survey on Knowledge Graphs for Healthcare: Resources, Applications, and Promises. arXiv preprint.

Xuan Kan, Wei Dai, 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).

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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).

PDF Code

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).

PDF Code Project Slides

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).

PDF Code Poster

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).

PDF Code Poster Slides

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).

PDF Code Poster Slides Video

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).

PDF Code Poster

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).

PDF Code Poster

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).

PDF Code Poster

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).

PDF Poster DOI Springer

Experience

Research Intern

Microsoft Research

May 2023 – Aug 2023 Redmond, Washington, United States

Worked in the Augmented Learning and Reasoning Group, 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.

Research Mentorship Program

Google

Feb 2022 – May 2022 Remote

Worked on GNN for recommendation with Masrour Zoghi.

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

Project ``Prototype Workflow for HoloLens Mix-reality Applications’’ got the best work award for Microsoft 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

BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks

University of Rochester, Prof. Fatemeh Nargesian’s Group Apr 2021

Give a talk about our benchmark work on GNNs for brain networks. Slides are available here.

Effective and Interpretable Graph Neural Networks for Graphs Constructed from Multimodality Data

Microsoft Search, Assistant and Intelligence Group Apr 2021

Give a talk about my work on Effective and Interpretable Graph Neural Networks for Graphs Constructed from Multimodality Data. Slides are available here.

Services

Program Committee Member / Reviewer:
- Conference: ICLR'24; NeurIPS'23; ML4H'23; CHIL'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
- Workshop: Temporal Graph Learning NeurIPS'22; Graph-Based Natural Language Processing COLING'22; Interpretable Machine Learning in Healthcare ICML'22,21; Computer Vision for Automated Medical Diagnosis ICCV'21; Medical Imaging meets NeurIPS'21
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: Chairperson, Microsoft Student Club, Tongji University (2017-2018)