Scholar
Jure Leskovec
Google Scholar ID: Q_kKkIUAAAAJ
Professor of Computer Science, Stanford University
Citations & Impact
Citations
152,681
H-index
144
i10-index
364
Publications
20
Co-authors
9
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Publications: See personal homepage for latest publications; Tools and Frameworks: PyTorch Frame, RelBench, PyG, Open Graph Benchmark; Workshops and Conferences: Stanford Graph Learning Workshop, ICLR 2021 Deep Learning for Simulation Workshop, ISMB 2021 Meta-learning Tutorial, CS224W: Machine Learning with Graphs, Nature paper on COVID-19 Mobility network Modeling, ISMB 2018 Deep Learning for Network Biology Tutorial, WWW 2018 Representation Learning on Networks Tutorial.
Research Experience
- Organizing Stanford Graph Learning Workshop 2024; Released PyTorch Frame: A PyTorch-based framework for deep learning over multi-modal tabular data; Released RelBench: Relational Deep Learning Benchmark; Released PyG: The ultimate library for Graph Neural Networks; Released the Open Graph Benchmark---Large Scale Challenge and held KDD Cup 2021; Tutorial on Meta-learning for Bridging Labeled and Unlabeled Data in Biomedicine at ISMB 2021; Videos and slides from CS224W: Machine Learning with Graphs; Organizing Deep Learning for Simulation workshop at ICLR 2021; COVID-19 Mobility network Modeling appeared in Nature; Released the Open Graph Benchmark; Tutorial on Deep Learning for Network Biology at ISMB 2018; Tutorial on Representation Learning on Networks at WWW 2018; Working on a new edition of Mining of Massive Datasets book with Anand Rajaraman and Jeff Ullman.
Background
- Professor of Computer Science at Stanford University. His 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.
Miscellany
- Social Media: @jure
