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 also go by the name Kelly. I am currently a PhD student in Computer Science at Emory University, under the supervision of Dr. Carl Yang in Emory Graph Mining Lab. I have also been working with Dr. Eugene Agichtein in Emory Information Retrieval Lab.

Before joining Emory, I got my bachelor’s degree in Software Engineering from Tongji University, where I was working on medical image computer vision with Dr. Lin Zhang.

My current research interests lie in machine learning with an emphasis on graph representation learning and its application to knowledge graphs and multi-modality data.

News

Paper entitled How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation has been accepted for presentation in the 44th European Conference on Information Retrieval.
Invited as the reviewer for AAAI 2022 and Med-NeurIPS 2021.
Empirical study entitled On Positional and Structural Node Features for Graph Neural Networks on Featureless Graphs has been accepted to the KDD 2021 Workshop on Deep Learning on Graphs: Methods and Applications.
Paper entitled Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration has been accepted for presentation in the Research Track of ECML-PKDD 2021.

Interests

Graph Machine Learning
Machine Learning for Multimodal Data
Knowledge Graphs
Commonsense Reasoning

Education

PhD in Computer Science, 2019-now
Emory University
BEng in Software Engineering, 2015-2019
Tongji University

Selected Publications

Hejie Cui, Jiaying Lu, Yao Ge, Carl Yang (2021). How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation. European Conference on Information Retrieval (ECIR).

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. The Thirty-eighth International Conference on Machine Learning (ICML), Workshop on Interpretable Machine Learning in Healthcare (IMLH).

PDF Poster

Xuan Kan, Hejie Cui, Ying Guo, Carl Yang (2021). Effective and Interpretable fMRI Analysis via Functional Brain Network Generation. The Thirty-eighth International Conference on Machine Learning (ICML), Workshop on Interpretable Machine Learning in Healthcare (IMLH).

PDF 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-PKDD).

PDF Code Poster

Hejie Cui, Zijie Lu, Pan Li, Carl Yang (2021). On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs (Spotlight). ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Workshop on Deep Learning on Graphs: Methods and Applications (DLG).

PDF Code Slides

Yanqiao Zhu, Hejie Cui, Lifang He, Lichao Sun, Carl Yang (2021). Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis. The Thirty-eighth International Conference on Machine Learning (ICML), Workshop on Computational Approaches to Mental Health (CAMH).

PDF Poster Video

Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu (2021). Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Workshop on Deep Learning on Graphs: Methods and Applications (DLG).

PDF Slides

Hejie Cui, Xinglong Liu, Ning Huang (2019). Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images. The 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).

PDF Poster DOI Springer

Experience

Research Intern

SenseTime

Dec 2018 – Jul 2019 Beijing, China

Worked as a computer vision algorithm intern in the Intelligent Medical Group
Developed a pulmonary vessel segmentation algorithm, an orthogonal fused U-Net++, for chest CT images
Got a patent filed and one paper accepted to MICCAI (International Conference on Medical Image Computing and Computer Assisted Intervention, tier-1 conference in medical imaging field)

Mitacs Global Research Intern

Queens University

Jul 2018 – Oct 2018 Kingston, Cananda

Project: Improve Center Line Tutor by Deep Learning, directed by Prof. Gabor Fichtinger
Designed a framework for providing real-time feedback in central venous catheterization training
Built an extension for web-cam video images classification using Tensorflow in 3D Slicer
Improved the CNN model by data argumentation and distortions such as deforming, cropping, or brightening

Microsoft University Summer Camp

Microsoft Research Asia

Aug 2017 – Aug 2017 Beijing, China

Designed a workflow for HoloLens mix reality application and built the prototype
Best work award for Microsoft summer camp Hackathon

Software Engineer Intern

SAP

Jun 2017 – Aug 2017 Shanghai, China

Selected as an excellent candidate for SAP vocational training projects and learned ABAP development

National Scholarships (Three times)

Tongji University (China) Sep 2016 – Sep 2018

Awarded to nationwide excellent undergraduates (Top 0.2%)

Services

PC Member:
- 2021 Med-NeurIPS Workshop
- 2021 ICML Workshop on Interpretable Machine Learning in Healthcare
- 2021 ICCV Workshop on Computer Vision for Automated Medical Diagnosis
Conference Reviewer:
- 2021 SDM SIAM International Conference on Data Mining
- 2022 WWW TheWebConf
- 2022 36th AAAI Conference on Artificial Intelligence
- 2020 MICCAI International Conference on Medical Image Computing and Computer Assisted Intervention
Journal Reviewer:
- IEEE Transactions on Big Data
Volunteer:
- 2021 ICML International Conference on Machine Learning
- 2020 ICML International Conference on Machine Learning
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