Biography

I am a CS Ph.D. student at Purdue University advised by Professor Bruno Ribeiro in the PurdueMINDS Lab. Before joining Purdue, I was working with Professor Paul Rosenbloom and Dr. Volkan Ustun at the Sigma cognitive architecture lab in the USC Institute for Creative Technologies. I received the Ross Fellowship at Purdue University and Computer Science Outstanding Student Award from USC Viterbi School of Engineering when graduating with a B.S. degree in CS.

I am interested in graph representation learning and graph foundation models (GFM), causal inference and causal discovery, Out-of-Distribution (OOD) generalization, and artificial general intelligence. My past research has also involved cognitive architectures, multi-agent reinforcement learning, and probabilistic graphical models. I believe research breakthroughs in all these areas will be critical to the development of a next-generation integrated architecture of artificial general intelligence.

Download my Curriculum Vitae .

Contact me: zhou791 at purdue dot edu

Interests
  • Graph Representation Learning
  • Knowledge Graphs
  • Causal Inference & Causal Discovery
  • Meta Learning
  • Out-of-Distribution Generalization
Education
  • Ph.D. in Computer Science (in progress), 2027 (expected)

    Purdue University

  • MS in Computer Science, 2022

    University of Southern California

  • BS in Computer Science, 2021

    University of Southern California

  • BS in Mathematics, 2021

    University of Southern California

Publications

(2025). Differentiable Constraint-Based Causal Discovery. NeurIPS 2025.

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(2025). Zero-shot Generalization of GNNs Over Distinct Attribute Domains. ICML 2025.

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(2024). A Foundation Model for Zero-shot Logical Query Reasoning. NeurIPS 2024.

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(2024). Long-Range Synthetic Knowledge Graph Benchmarks for Double-Equivariant Models. ICLR 2024 BGPT.

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(2023). Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types. NeurIPS 2023 New Frontiers in Graph Learning (GLFrontiers) (Oral).

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