Jure Leskovec

About

Jure Leskovec is a Professor of Computer Science at Stanford University. His general research area is applied machine learning for large interconnected systems, with a focus on modeling complex, richly-labeled relational structures, graphs, and networks across systems at all scales. These scales range from interactions of proteins within a cell to interactions between humans in society. His research applications include commonsense reasoning, recommender systems, computational social science, and computational biology, with a particular emphasis on drug discovery.

Research topics

• Computer Science
• Artificial Intelligence
• Data Mining
• Machine Learning
• Biology
• Sociology
• Engineering
• Mathematics
• Geography
• Cell biology

Selected publications

• On the Opportunities and Risks of Foundation Models

  arXiv (Cornell University) · 2021 · 2169 citations

  AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(…

  PublisherOA PDFDOI

• Mobility network models of COVID-19 explain inequities and inform reopening

  Nature · 2020 · 1624 citations

  Senior authorCorresponding
  PublisherOA PDFDOI

• Scientific discovery in the age of artificial intelligence

  Nature · 2023 · 1538 citations

  PublisherDOI

• Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly

  Science · 2022 · 834 citations

  community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

  PublisherOA PDFDOI

• Organization of the human intestine at single-cell resolution

  Nature · 2023 · 301 citations

  . The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the int…

  PublisherOA PDFDOI

Recent grants

• Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology

  NSF · $1.4M · 2020–2026

• Collaborative Research: Framework: Software: CINES: A Scalable Cyberinfrastructure for Sustained Innovation in Network Engineering and Science

  NSF · $540k · 2018–2024

• CAREER: Mining structure and dynamics of groups of nodes in real-world networks

  NSF · $541k · 2012–2017

Frequent coauthors

• Marinka Žitnik

  64 shared

• Jon Kleinberg

  Cornell University

  55 shared

• Rex Ying

  Yale University

  40 shared

• Jiaxuan You

  38 shared

• Rok Sosič

  37 shared

• Maria Brbić

  31 shared

• Tim Althoff

  29 shared

• David Hallac

  28 shared

Education

• Postdoc, Computer Science Department

  Cornell University

  2009

• PhD, Machine Learning Department

  Carnegie Mellon University

  2008