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 Hygeia. 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 Intelligent Information Access Lab.

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

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

News

Our two preliminary papers entitled BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis and Effective and Interpretable fMRI Analysis with Functional Brain Network Generation have been accepted for ICML 2021 Workshop for Interpretable Machine Learning in Healthcare.
Our 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.
Our preliminary paper entitled Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis has been accepted for presentation in ICML 2021 Workshop on Computational Approaches to Mental Health.
I’m excited that our 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.

Selected Publications

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. ICML Workshop on Interpretable Machine Learning in Healthcare.

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

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Xuan Kan, Hejie Cui, Carl Yang (2021). Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration. ECML.

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Hejie Cui, Zijie Lu, Pan Li, Carl Yang (2021). On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs (Spotlight). KDD Workshop on Deep Learning on Graphs.

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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. ICML Workshop on Computational Approaches to Mental Health.

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. KDD Workshop on Deep Learning on Graphs: Methods and Applications.

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Hejie Cui, Xinglong Liu, Ning Huang (2019). Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images. MICCAI.

PDF Poster DOI Springer

Experience

Research Intern

SenseTime

Dec 2018 – Jul 2019 Beijing, China

Pulmonary Vessel Segmentation based on OrthogonalFused U-Net++ of Chest CT Images:

Worked as a intern algorithm engineer in the Intelligent Medical Group.
Developed a pulmonary vessel segmentation algorithm based on my updated network, an orthogonal fused U-Net++, for chest CT images.
Published a patent on my intern work and got one paper accepted by MICCAI (International Conference on Medical Image Computing and Computer Assisted Intervention, which is the tier 1 conference in medical imaging field) 2019 as the first author

Mitacs Global Research Intern

Queens University

Jul 2018 – Oct 2018 Kingston, Cananda

Improve Center Line Tutor by Deep Learning:

Built an extension for classifying web-cam video images using Tensorflow in 3D Slicer
Used Tensorflow in real-time workflow detection for providing real-time feedback in central venous catheterization training.
Made distortion such as deforming, cropping, or brightening in the training inputs in random ways to polish the model, analyzed the influence of each parameters to get the best retrained model.

Software Engineer Intern

SAP

Jul 2017 – Aug 2017 Shanghai, China

Helped to develop SAP ERP system and use the HANA database to process enterprise management data

Services

PC Member:
- 2022 AAAI Conference on Artificial Intelligence
- 2021 ICML Workshop on Interpretable Machine Learning in Healthcare
- 2021 ICCV Workshop on Computer Vision for Automated Medical Diagnosis
- 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 in Biomedical Applications, Head TA, Spring 2020