I am a founding engineer at Kumo.ai and work on neural architectures for the Kumo.ai platform, as well as explainable AI for Kumo.ai (and PyG). I graduated from the Department of Computer Science at Stanford University with a PhD degree, advised by Jure Leskovec. My PhD focus was on graph representation learning and graph neural networks.
I leverage GNNs in a variety of real-world applications, including recommender systems, anomaly detection, knowledge graphs, scientific simulations, molecule prediction etc. I have been a long-term user and contributor to PyG when developing all the research projects, and aim to create a GNN-based platform that can help users to achieve their goals with graph learning in an easy-to-use manner. Thus I am excited to lead many efforts in PyG for the research community, as well as development in Kumo.ai for industry users.
Working and creating Kumo.ai have been an exciting journey, where we gradually shaped the product from ideas through multiple iterations of brainstorming, prototyping and experimentation. I enjoy working in an agile environment where all of us are quick in responding to customer requests and experimenting with novel ideas in quick iteration to find the most effective and generalized solutions.
I recently joined Yale University as an assistant professor, but I am continuing as a technical advisor and contributor to Kumo.ai. I am excited to bring my new research in trustworthy GNNs, graph pre-training and AutoML to PyG and Kumo.ai. In my free time, I like hiking, piano, and PC building. Reach me at Twitter.