Hejie Cui

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

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

Funds and Awards

NeurIPS AI4Science Workshop Travel Award

AI4Science Travel Award for attending NeurIPS 2022 in New Orleans

CIKM Student Travel Grant Award

NSF Travel Grants for attending CIKM 2022 in Atlanta

Laney Graduate Student Council (LGSC) Travel / Research Grant

Laney Graduate Student Council (LGSC) Travel / Research Grant for attending academic conference

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

Globalink Research Award

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 Scholarship (Three times)

Nationwide highest honor for undergraduates in China

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

Talks

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

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

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)