Introduction

I'm an assistant professor in the Department of Computer Science at Yale University. My research focus includes algorithms for graph neural networks, geometric embeddings and explainable models. I am the author of many widely used GNN algorithms such as GraphSAGE, PinSAGE and GNNExplainer. In addition, I have worked on a variety of applications of graph learning in physical simulations, social networks, knowledge graphs and biology. I developed the first billion-scale graph embedding services at Pinterest, and the graph-based anomaly detection algorithm at Amazon.

Education

I obtained my Ph.D degree in computer science at Stanford University, advised by Jure Leskovec. My thesis focuses on expressive, scalable and explainable GNNs (graph neural networks), which is available on Github. Prior to that, I graduated from Duke University in 2016 with the highest distinction. I majored in computer science and mathematics.

Notice

I moved my website to this new address, where I will make more regular updates available. Content in this current address might not be up-to-date.

• I'm the winner of KDD 2022 Dissertationn Award, and will present the thesis at the KDD 2022 Conference.
• I'm excited to be a founding engineer of Kumo.ai, where we work on building a cloud AI platform based on state-of-the-art GNNs and the PyG library.
• I'm one of the 10 winners of the 2019 Baidu Scholarship in Artificial Intelligence.

My research spans 3 broad areas: deep learning for graphs, geometric representation learning, and real-world applications with relational reasoning and modeling. My interests span from theoretical questions in graph learning, generalization, manifold geometry, to practical use cases in science and technology, collaborating with science institutes and IT companies.

My list of publications can be found on Google Scholar

I'm excited to serve the research community in various aspects. I co-lead the open-source project, PyTorch Geometric, which aims to make developing graph neural networks easy and accessible for researchers, engineers and general audience with a variety of background. I served as committee members for machine learning conferences including AAAI, ICML, NeurIPS, ICLR, KDD, WebConf for over 7 years, and I am serving as area chair for LoG 2022. In addition, I organized a variety of workshops on topics including graph learning, graph neural networks and deep learning for simulation. I'll also teach the course of "Deep Learning for Graph-Structured Data" at Yale this coming academic year. More course materials will be available soon!