Hey folks. I am a fourth-year PhD student in Computer Science and Engineering at University of Notre Dame, supervised by Prof. Nitesh Chawla. Before that, I worked as a data scientist at Aunalytics. I received my Master degree in Statistics at University of Illinois Urbana-Champaign, and my Bachelor degree in Mathematics at Sichuan University.

My research interests lie in graph machine learning, representation learning and data science. I am currently working on the problem of graph representation learning, and its applications in link prediction. I also like building data-driven applications that can help people make better decisions.

Cheers!

What’s New

• [2024.09] My work MPLP got accepted to Neurips’24!
• [2024.05] Excited to start my summer internship as a research scientist at Intuit.
• [2024.04] We propose a family of Trainless GNNs that can be fitted on graphs without gradient descent. See preprint at Arxiv.
• [2024.02] As a step towards graph foundation models, we propose a Universal Link Prediction model (UniLP) that can be applied to any unseen new graphs without training. See preprint at Arxiv. It is also discussed in a recent blog about graph foundation models.
• [2023.09] We study whether GNNs can count Common Neighbors for link prediction tasks, and propose a novel link prediction model, MPLP, which only leverages message passing to estimate structural features. See preprint at Arxiv.
• [2022.11] Our paper about link prediction is accepted to LOG’22. Paper
• [2022.11] Our paper about self-supervised learning on heterogeneous graph is accepted to AAAI’23. Paper
• [2021.08] Started my first year at Notre Dame!
• [2021.06] One paper was accepted at ECML PKDD 2021. Paper