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 Chuxu Zhang

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


About Me

I am an Assistant Professor of Computer Science at Brandeis University. My general research interests center around artificial intelligence, machine learning, and data science. Recently, I have focused on developing effective, efficient, safe, and generative machine learning models and algorithms on relational data (e.g., graph) and multi-modality data (e.g., text and image). Besides, I apply machine learning to solve societal challenges in healthcare, social media, science, and others. Before joining Brandeis, I did my PhD study at University of Notre Dame (2017-2020), advised by Nitesh Chawla.


News

  • Looking for multiple PhD students (Spring 2025, Fall 2025).
  • The workshop on Resource-efficient Learning at KDD'24: [Call For Paper], [Paper Submission Site].
  • AC for NeurIPS'24, KDD'24
  • Talk: Graph Machine Learning: Effectiveness, Efficiency, and Safety [slide]
  • 03/2024 - Received Frontiers of Science Award from ICBS 2024.
  • 02/2024 - Received NSF CAREER Award on graph machine learning! Thanks NSF, my excellent mentors and students:)
  • Talk: Taming Networks in the Wild: A Holistic Learning Framework [slide]
  • AC/SPC for AAAI'24, IJCAI'24, WSDM'24, CIKM'24, SDM'24, Track Chair for COLING'24
  • 08/2023 - The workshop on Resource-efficient Learning at KDD'23: [Call For Paper], [Paper Submission Site].
  • 07/2023 - Received an NSF grant on dietary recommendations.
  • Talk: Towards Societal Impact of AI [slide]
  • Selected as AI 2000 Most Influential Scholar Award Honorable Mention by AMiner.
  • SPC for AAAI'23, WSDM'23, SDM'23, KDD'23, IJCAI'23, CIKM'23
  • Selected as New Faculty Highlight at AAAI'23.
  • Talk: Resource-efficient Graph Representation Learning [slide]
  • 08/2022 - Received an NSF grant on drug trafficking network detection and intervention.
  • 05/2022 - KDD'22 Tutorial: Towards Graph Minimally-supervised Learning
  • Talk: Few-shot Learning on Graphs [slide] [survey]

Publications

Here are some recent publications. Please see my Google Scholar page for a complete list.

  • ICML'24: From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble
  • ICLR'24: Mitigating Severe Robustness Degradation on Graphs
  • KDD'24: Graph Cross Supervised Learning via Generalized Knowledge
  • KDD'24: Diet-ODIN: A Novel Framework for Opioid Misuse Detection with LLM-based Interpretable Dietary Patterns
  • ICML'23: When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
  • ICLR'23: Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization
  • ICLR'23: Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
  • ICLR'23: Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency
  • NeurIPS'22: Label-invariant Augmentation for Semi- Supervised Graph Classification
  • NeurIPS'22: Co-Modality Imbalanced Graph Contrastive Learning
  • KDD'22: Task-Adaptive Few-shot Node Classification
  • KDD'22: Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction

Students and Mentees

Current

Former

  • Chunhui Zhang (Brandeis MS, Next: PhD student at Dartmouth)
  • Zheyuan Liu (Brandeis BS, Next: PhD student at Notre Dame)
  • Erchi Zhang (Brandeis BS, Next: MS student at NYU)
  • Jiele Wu (BIT BE, Next: PhD student at NUS)
  • Qiannan Zhang (KAUST PhD, Next: PostDoc at Cornell)
  • Qiang Yang (KAUST PhD, Next: PostDoc at UF)
  • Yiyue Qian (Notre Dame PhD, Next: Research Scientis at Amazon)
  • Qianlong Wen (Notre Dame, Next: PhD student at Notre Dame)
  • Yijun Tian (Notre Dame, Next: Research Scientis at Amazon)
  • Zhichun Guo (Notre Dame, Next: Assistant Professor at Emory)
  • Mingxuan Ju (Notre Dame PhD, Next: Research Scientis at Snap)
  • Zhongyu Ouyang (Notre Dame, Next: PhD student at Notre Dame)
  • Jianan Zhao (BUPT BE, Next: PhD student at Mila)

Teaching

  • Artificial Intelligence
  • Deep Learning
  • Graph Mining and Learning
Last update in 05/2024