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 Chuxu Zhang

  Assistant Professor
  Email: chuxuzhang[at]brandeis[dot]edu

I am an Assistant Professor of Computer Science at Brandeis University. My research interests center around machine learning, data mining, and their applications. Recently, I focus on developing effective, efficient, and trustworthy machine learning models and algorithms, particularly on network/graph and multi-modal data. I also develop artificial intelligence techniques for public health, social/information network analysis, recommender systems, and interdisciplinary problems. Before joining Brandeis, I did my PhD study at Notre Dame, advised by Nitesh Chawla.


  • Looking for PhD students and research interns.
  • SPC for AAAI'24, WSDM'24, SDM'24, Track Chair for COLING'24
  • The First Workshop on Resource-efficient Learning at KDD'23: [Call For Paper], [Paper Submission Site].
  • 07/2023 - Received an NSF grant on dietary recommendations.
  • 02/2023 - AI 2000 Most Influential Scholar Award Honorable Mention in Data Mining by AMiner.
  • Talk: Towards Societal Impact of AI [slide]
  • SPC for KDD'23, IJCAI'23, CIKM'23
  • 11/2022 - New Faculty Highlight at AAAI'23.
  • Talk: Resource-efficient Graph Representation Learning [slide]
  • 08/2022 - Received an NSF grant on drug trafficking detection.
  • SPC for AAAI'23, WSDM'23, SDM'23
  • 05/2022 - KDD'22 Tutorial: Towards Graph Minimally-supervised Learning
  • Talk: Few-shot Learning on Graphs [slide] [survey]
  • SPC for KDD'22, AAAI'22, CIKM'22


  • Followings are some selected recent publications. Please see my Google Scholar page or the Publication page for a complete list.
  • ICML'23: When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
  • ICLR'23: Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
  • ICLR'23: Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization
  • WWW'23: Fair Graph Representation Learning via Diverse Mixture of Experts
  • AAAI'23: Heterogeneous Graph Masked Autoencoders
  • EMNLP'22: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering
  • KDD'22: Task-Adaptive Few-shot Node Classification
  • KDD'22: Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction
  • IJCAI'22: Few-Shot Learning on Graphs
  • SDM'22: Heterogeneous Temporal Graph Neural Network
  • NeurIPS'21: Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
  • CIKM'21: RxNet: Rx-refill Graph Neural Network for Overprescribing Detection
  • WWW'21: Few-Shot Graph Learning for Molecular Property Prediction


  • Introduction to Artificial Intelligence (Fall 2023)
  • Deep Learning (Fall 2022, Spring 2022, Spring 2021)
  • Network Mining and Learning (Spring 2023, Fall 2021)
  • Advanced Topics in Graph Mining (Fall 2020)

Last update in 08/2023