Jure Leskovec.
I am Associate Professor of Computer Science at Stanford University, and investigator at Chan Zuckerberg Biohub.
My general research area is applied machine learning for large interconnected systems focusing on modeling complex, richly-labeled relational structures, graphs, and networks for systems at all scales, from interactions of proteins in a cell to interactions between humans in a society. Applications include commonsense reasoning, recommender systems, computational social science, and computational biology with an emphasis on drug discovery.
What's new
- We released PyG: The ultimate library for Graph Neural Networks. September 2021.
- Videos and slides from Stanford Graph Learning Workshop. Held at Stanford, September 2021.
- We released the Open Graph Benchmark---Large Scale Challenge and held KDD Cup 2021. Check the workshop slides and videos. August 2021.
- Tutorial on Meta-learning for Bridging Labeled and Unlabeled Data in Biomedicine. Held at ISMB 2021.
- Videos of my CS224W: Machine Learning with Graphs, which focuses on representation learning and graph neural networks. CS224W Syllabus.
- Organizing Deep Learning for Simulation workshop at ICLR 2021.
- COVID-19 Mobility network Modeling appeared in Nature. Read also the commentary by Kevin Ma and Marc Lipsitch. Try out the model.
- We released the Open Graph Benchmark. May 2020.
- Tutorial on Deep Learning for Network Biology. Held at ISMB 2018 (July 6, Chicago, IL).
- Tutorial on Representation Learning on Networks. Held at WWW 2018 (April 24, Lyon, France).
- With Anand Rajaraman and Jeff Ullman we are working in a new edition of Mining of Massive Datasets book. PDFs are at MMDS.org