Founding member, Kumo AI
Ph.D. in Computer Science, Stanford University
Palo Alto, California
I received my Ph.D. and M.S. degrees from Department of Computer Science, Stanford University, advised by Prof. Jure Leskovec.
I was supported by JPMC PhD Fellowship and Baidu Scholarship during my PhD.
At Kumo AI, I built a graph machine learning engine for cloud databases as a founding member.
My research aims at developing data-driven methods to study our interconnected world. I investigate scientific and industrial problems through the lens of graph/relational data, and develop AI/ML solutions for these problems.
My main research interests include:
- Core graph/relational learning methods: Learning from graphs [NeurIPS 2018b/2019b/2020a, ICML 2019, AAAI 2021]; Generating & optimizing graphs [ICML 2018, NeurIPS 2018a/2019a]
- Democratize graph learning: Software and systems that make graph learning accessible to researchers and practitioners [GraphGym, PyG, Kumo AI]
- Graph-inspired machine learning: Neural architecture design [ICML 2020], multi-task learning [ICLR 2022], deep learning with missing data [NeurIPS 2020b].
- Interdisciplinary applications: crop yield prediction [AAAI 2017], drug discovery [NeurIPS 2018a], recommender systems [WWW 2019], financial transactions [KDD 2022], relational database [Kumo AI]