Hi! I am Kexin. I am a PhD student at Stanford Computer Science, advised by Prof. Jure Leskovec. My research is supported by Stanford Bio-X fellowship.
I work on enabling machine learning to produce novel, deployable, and interpretable biomedical discoveries. Questions that I am excited about:
How to encode the massive, multi-modal, and multi-scale biological experimental data as inductive bias and use them to generate novel hypotheses and discoveries?
How to make these discoveries reliable, trustworthy, and overall aligned with what scientists truly value?
How to ground model predictions to first-principle mechanisms and actionable insights?
I answer these questions on diverse and difficult biological problems such as human genetics, genetic perturbations, therapeutics discovery, etc.
Previously, I worked with Prof. Marinka Zitnik, Dr. Cao Xiao, and Prof. Jimeng Sun. I have spent time researching at Genentech, Pfizer, IQVIA, Dana-Farber, Flatiron Health, and Rockefeller University. I did my undergrad at NYU in math & CS & studio art, and master at Harvard in health data science.
News:
2023.12 Spotlight at NeurIPS 2023 and Oral at MLCB 2023!
2023.06 Excited to work with Prof. Aviv Regev and Dr. Romain Lopez at Genentech this summer!
2022.07 Co-organize the inaugural LoG conference!
2022.07 Best paper honorable mentions award at IEEE VIS!
2022.07 Co-organize AI for Science Workshop at ICML!
2021.07 Co-organize AI for Science Workshop at NeurIPS!
Research:
[ Preprint ] Zero-shot Prediction of Therapeutic Use with Geometric Deep Learning and Clinician-Centered Design
[ Preprint ] Relational Deep Learning: Graph Representation Learning on Relational Databases
[ RECOMB ] Sequential Optimal Experimental Design of Perturbation Screens Guided by Multimodal Priors
[ NeurIPS ] Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
[ NeurIPS ] Enabling Tabular Deep Learning When d≫n with an Auxiliary Knowledge Graph
[ Nature Biotechnology ] GEARS: Predicting Transcriptional Outcomes of Novel Multi-gene Perturbations
[ Nature ] Scientific Discovery in the Age of Artificial Intelligence
[ Nature Chemical Biology ] Artificial Intelligence Foundation for Therapeutic Science
[ Nature Biomedical Engineering ] Graph Representation Learning in Biomedicine and Healthcare
[ NeurIPS ] Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
[ IEEE VIS ] Towards Usable Explanations: Extending the Nested Model of Visualization Design for User-Centric XAI
[ NeurIPS ] Graph Meta Learning via Local Subgraphs
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