Jure Leskovec @ Stanford (original) (raw)
I am Professor of Computer Science at Stanford University.
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
- Organizing Stanford Graph Learning Workshop 2024. The workshop will bring together leaders from academia and industry to showcase recent advances in Machine Learning and AI. November 2024.
- We released PyTorch Frame: A PyTorch-based framework for deep learning over multi-modal tabular data. March 2024.
- We released RelBench: Relational Deep Learning Benchmark . An open benchmark for machine learning over relational databases. December 2023.
- 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