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 PhD student in Computer Science at Emory University, under the supervision of Prof. Carl Yang in Emory Graph Mining (EGM) Lab. My research interests lie in graph data mining, knowledge acquisition and infusion with deep language/vision models, as well as effective and interpretable graph learning methods for neuroscience and healthcare. Check my CV for more details.

Previously, I got my bachelor’s degree 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

[2023.1.22] Two papers on deep structure learning and hierarchical clustering got accepted to ISBI'23. Congrats to Yue and Wei!
[2022.12.22] Invited as a reviewer for KDD'23 Research Track.
[2022.11.19] One paper on few-shot learning is accepted to AAAI'23 as Oral . Congrats to Ran!
[2022.11.7] Glad to receive NeurIPS AI4Science Travel Award.
[2022.10.21] Benchmark work on Graph Neural Networks for Brain Networks has now been officially accepted to IEEE TMI (IF: 11.037) . Great thanks to my collaborators!
[2022.10.19] One paper on multi-view network analysis got accepted to BIBM'22.
[2022.9.14] One paper on graph transformer got accepted to NeurIPS'22 as Spotlight . See you in New Orleans!
[2022.8.1] One paper on GNN node features for non-attributed graphs got accepted to CIKM'22.
[2022.6.3] One paper on interpretable GNNs got accepted to MICCAI 2022 as Oral .
[2022.6.2] One paper on multi-task meta-learning got accepted to KDD'22.
[2022.5.30] One Paper on network generation guided by task-aware GNNs accepted at MIDL'22 as Oral .
[2022.5.16] Started my research internship at the Amazon Product Knowledge Graph Team.

Interests

Graph Data Mining
Knowledge Graphs
Knowledge Acquisition and Infusion with Deep Language/Vision Models
AI for Health

Education

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

Selected Publications

Ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce C. Ho, Chao Zhang, Carl Yang (2022). Neighborhood-regularized Self-Training for Learning with Few Labels. Proceedings of the AAAI International Conference on Artificial Intelligence (AAAI) (Oral).

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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).

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Gongxu Luo, Chenyang Li, Hejie Cui, Lichao Sun, Lifang He, Carl Yang (2022). Multi-View Brain Network Analysis with Cross-View Missing Network Generation. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM).

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

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

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Yi Yang, Yanqiao Zhu, Hejie Cui, Xuan Kan, Lifang He, Ying Guo, Carl Yang (2022). Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning. Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD).

PDF Slides

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

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Yanqiao Zhu, Hejie Cui, Lifang He, Lichao Sun, Carl Yang (2022). Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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

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Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu (2022). Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning. SIAM International Conference on Data Mining (SDM).

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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. Proceedings of the European Conference on Information Retrieval (ECIR).

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Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang (2021). BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis. International Conference on Machine Learning, Workshop on Interpretable Machine Learning in Healthcare (ICML-IMLH).

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

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

PDF Poster DOI Springer

Experience

Applied Scientist Intern

Amazon

May 2022 – Aug 2022 Seattle, Washington, United States

Worked in the Product Knowledge Graph Team
Project: Attributed value extraction for Amazon product knowledge graph construction

Research Mentorship Program

Google

Feb 2022 – May 2022 Remote

Discussed potential student researcher project on GNN for retrieval/recommendation and wrote up a short proposal.
Mentor: Masrour Zoghi

Research Intern

SenseTime

Dec 2018 – Jul 2019 Beijing, China

Worked in the Intelligent Health Team
Project: Developed a pulmonary vessel segmentation algorithm, an orthogonal fused U-Net++, for chest CT images
Achievement: Got a patent filed and one paper accepted to MICCAI (tier-1 conference in medical imaging)

Mitacs Global Research Intern

Queens University

Jul 2018 – Oct 2018 Kingston, Ontario, Canada

Worked in Prof. Gabor Fichtinger‘s Perk Lab
Project: Improve Center Line Tutor by Deep Learning, directed by Prof. Gabor Fichtinger
Achievement: Designed a framework for providing real-time feedback in central venous catheterization training and built an extension for web-cam video image classification using Tensorflow in 3D Slicer

Microsoft University Summer Camp

Microsoft Research Asia

Aug 2017 – Aug 2017 Beijing, China

Project: Prototype Workflow for HoloLens Mix-reality Applications
Achievement: Best work award for university summer camp Hackathon

SAP Student Training and Rotation Program

SAP

Jun 2017 – Aug 2017 Shanghai, China

Project: ABAP development

Selected Accomplishments

NeurIPS AI4Science Travel Award, 2022
NSF Student Travel Grant Award for CIKM, 2022
Laney Graduate Student Council Travel/Research Grant, Emory University, 2022
MICCAI Student Travel Grant Award, 2022
Fellowship of 2021 CRA-WP Grad Cohort for Women, 2021
Emory University Graduate Fellowship, 2019
Valedictorian of the School of Software Engineering Class 2019, Tongji University, 2019
Outstanding Graduate of Shanghai, 2019
Globalink Research Award for International Undergraduate Research in Canada, 2018
Tongji University Programming Competition, Silver Prize, 2017
National Scholarship of China, 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: SIGKDD'22,23; ICDM'22; WWW'22; AAAI'21,22; MICCAI'20,22,23; SDM'22; LOG'22
- Journal: TKDE, IEEE BigData
- Workshop: Temporal Graph Learning NeurIPS'22; Graph-Based Natural Language Processing COLING'22; Interpretable Machine Learning in Healthcare ICML'21,22; 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:
- International Conference on Machine Learning ICML'20,21
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)