Hejie Cui

Hejie Cui

Doctoral Student in CS

Emory University


Hi there! This is Hejie Cui (pronounced as “He-jay Tsuee”, 崔鹤洁 in Chinese). I also go by the name Callie. 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 worked on medical image computer vision with Dr. Lin Zhang for my undergraduate thesis. I have also luckily worked with Dr. Tianwei Yu on machine learning for bioinformatics and Dr. Gabor Fichtinger on deep learning for web-cam videos.

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


  • 2022.04.15 Gave a talk on our work of BrainGB to Dr. Fatemeh Nargesian‘s group at the University of Rochester. Slides are available here. Thanks Draco for inviting me.
  • 2022.04.01 Gave a talk on Effective and Interpretable Graph Neural Networks for Graphs Constructed from Multimodality Data at Microsoft Search, Assistant & Intelligence Group. Slides are available here. Thanks Kunho for inviting me.
  • 2022.03.31 Paper entitled Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis got accepted to EMBC 2022.
  • 2022.2.28 Paper entitle FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation got accepted to MIDL 2022.
  • 2022.2.7 Glad to be accepted to the 2022A class of Google’s CS Research Mentorship Program (CSRMP)!
  • 2021.12.19 Paper entitled Structure-Enhanced Heterogeneous Graph Contrastive Learning got accepted to SDM 2022.
  • 2021.12.15 I will join Amazon as an Applied Scientist Intern in the Product Graph Team this coming summer.


  • Graph Machine Learning
  • GNNs for Multimodality Data
  • Knowledge Graphs
  • Commonsense Reasoning


  • PhD in Computer Science, 2019-now

    Emory University

  • BEng in Software Engineering, 2015-2019

    Tongji University



Research Intern


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


Jun 2017 – Aug 2017 Shanghai, China
  • Selected as an excellent candidate for SAP vocational training projects and learned ABAP development

Funds and Awards

Fellowship of 2021 CRA-WP Grad Cohort for Women

Fellowship for attending 2021 Grad Cohort Workshops for Women in New Orleans

Emory LGS Conference Funding

Part of the travel grant for attending MICCAI 2019

Scholarship for Mitacs Globalink Research Internship

Awarded $7800 CAD fundings to top-ranked international undergraduates to participate in a 12-week research internship under the supervision of Canadian university faculty members

National Scholarships (Three times)

Awarded to nationwide excellent undergraduates (Top 0.2%)

Recent Posts

Pytorch Geometric Environment

traps of pytorch, cuda, gcc version conflicts

Tmux and Screen 常用指令

frequent using command, multiple session ssh


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

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


  • 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:
    • 2022 Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD)
    • 2022 SIAM International Conference on Data Mining (SDM)
    • 2022 TheWebConf (WWW)
    • 2022 Conference on Artificial Intelligence (AAAI)
    • 2020/2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
  • Journal Reviewer:
    • IEEE Transactions on Knowledge and Data Engineering (TKDE)
    • IEEE Transactions on Big Data (Big Data)
  • Volunteer:
    • 2020/2021 International Conference on Machine Learning (ICML)
  • 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