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 Prof. Carl Yang in Emory Graph Mining (EGM) Lab. My research interests lie in interpretable machine learning, graph data mining, multi-modal learning, knowledge graphs, as well their applications in neuroscience and healthcare.

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.


  • [2022.8.1] Short paper on artificial node features for applying GNNs on non-attributed graphs got accepted to CIKM 2022.
  • [2022.7.27] Invited as a PC member for AAAI 2023 regular track and the special track on AI for social impact.
  • [2022.7.21] Invited as a reviewer for TextGraphs workshop on COLING 2022.
  • [2022.6.23] We will be organizing the 1st International Workshop on Federated Learning with Graph Data FedGraph2022, at CIKM 2022, in Atlanta, Georgia this October. Various types of submissions are welcomed!
  • [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 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 (Seattle). Drop me a message if you wish to grab a coffee together!
  • [2022.4.15] We will be organizing the 1st International Workshop on Neural Network Models for Brain Connectome Analysis BrainNN2022, at IEEE BigData 2022, in Osaka, Japan this December. Various types of submissions are welcomed!


  • 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



Applied Scientist Intern


May 2022 – Present Seattle, Washington, United States
  • Work in the Product Knowledge Graph Team

Research Intern


Dec 2018 – Jul 2019 Beijing, China
  • Worked in the Intelligent Medical Group
  • 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 Microsoft summer camp Hackathon

SAP Student Training and Rotation Program


Jun 2017 – Aug 2017 Shanghai, China
  • Project: 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:
    • Workshop on Med-NeurIPS Workshop (NeurIPS 2021)
    • Workshop on Interpretable Machine Learning in Healthcare (ICML 2021/2022)
    • Workshop on Computer Vision for Automated Medical Diagnosis (ICCV 2021)
  • Conference Reviewer:
    • 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 2022)
    • International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020/2022)
  • Journal Reviewer:
    • IEEE Transactions on Knowledge and Data Engineering (TKDE)
    • IEEE Transactions on Big Data (Big Data)
  • Organizer:
    • Workshop on Neural Networks for Brain Connectome Analysis (BrainNN): Theories, Methods, and Applications (IEEE BigData 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)