Hejie Cui

Doctoral Student in CS

Emory University

Biography

Hi there! This is Hejie Cui (pronounced as “He-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 machine learning, data mining, knowledge graphs, multi-modal learning, as well their applications in neuroscience and healthcare data. 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

[2022.11.19] One paper on few-shot learning got accepted by AAAI 2023. Congrats to Ran!
[2022.11.7] Glad to receive NeurIPS AI4Science Workshop 2022 Travel Award!
[2022.10.21] Our benchmark work “BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks” has now been officially accepted to IEEE TMI (IF: 11.037). Great thanks to my collaborators!
[2022.10.19] Our work on multi-view brain network analysis with cross-view missing network generation got accepted to BIBM 2022.
[2022.9.14] Our work on brain network transformer got accepted to NeurIPS 2022. The previous version was selected as Oral for ICML-IMLH 2022. See you in New Orleans!
[2022.8.1] Short paper on artificial node features for applying GNNs on non-attributed graphs got accepted to CIKM 2022.
[2022.6.3] Paper on brain disease analysis with interpretable GNNs got accepted to MICCAI 2022 (among the top 13% provisionally accepted ones in 1,825 submissions).
[2022.6.2] Our work on cross-data brain networks got accepted to KDD 2022 Health Day.
[2022.5.30] Our work on functional brain network generation guided by task-aware GNNs has been selected for an oral presentation at MIDL 2022.
[2022.5.16] Started my research intern at the Amazon Product Knowledge Graph Team.

Interests

Graph Machine Learning
Knowledge Graphs
AI for Health
Multimodal Learning

Education

PhD in Computer Science, 2019-now
Emory University
BEng 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).

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

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).

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).

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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).

PDF Code Slides

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).

PDF Code Poster Slides Video

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).

PDF Poster Video

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).

PDF Code Poster

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).

PDF Source Document

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).

PDF Code Poster

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).

PDF Poster

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).

PDF Code Poster

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 – Present Seattle, Washington, United States

Work in the Product Knowledge Graph Team
Worked on attributed 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

National Scholarship (Three times)

Tongji University Sep 2016 – Sep 2018

Nationwide highest honor for undergraduates in China

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:
- SIGKDD 2022
- ICDM 2022
- WWW 2022
- AAAI (Main Track and Special Track on AI for Social Impact) 2021, 2022
- MICCAI 2020, 2022
- SDM 2022
- Learning on Graphs Conference (LOG 2022)
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- IEEE Transactions on Big Data (Big Data)
- AI for Science: Progress and Promises @ NeurIPS 2022
- Temporal Graph Learning Workshop @ NeurIPS 2022
- Federated Learning with Graph Data Workshop @ CIKM 2022
- Graph-Based Natural Language Processing Workshop @ COLING 2022
- Interpretable Machine Learning in Healthcare Workshop @ ICML 2021, 2022
- Computer Vision for Automated Medical Diagnosis Workshop @ ICCV 2021
- Medical Imaging meets NeurIPS Workshop @ NeurIPS 2021
Organizer:
- Neural Networks for Brain Connectome Analysis: Theories, Methods, and Applications Workshop @ IEEE BigData 2022
- Federated Learning with Graph Data Workshop @ ACM CIKM 2022
Volunteer:
- International Conference on Machine Learning (ICML 2020/2021)
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)

Hejie Cui

Doctoral Student in CS

Emory University

Biography

News

Interests

Education

Selected Publications

Experience

Applied Scientist Intern

Amazon

Research Mentorship Program

Google

Research Intern

SenseTime

Mitacs Global Research Intern

Queens University

Microsoft University Summer Camp

Microsoft Research Asia

SAP Student Training and Rotation Program

SAP

Funds and Awards

NeurIPS AI4Science Workshop Travel Award

CIKM Student Travel Grant Award

Laney Graduate Student Council (LGSC) Travel / Research Grant

Fellowship of 2021 CRA-WP Grad Cohort for Women

Emory LGS Conference Funding

Globalink Research Award

National Scholarship (Three times)

Recent Posts

Pytorch Geometric Environment

Tmux and Screen 常用指令

Talks

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

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

Services