Jure Leskovec
Associate Professor of Computer Science at Stanford University (ONLINE)
Biography
Stanford University (ONLINE)
Leskovec's research focuses on the analyzing and modeling of large social and information networks as the study of phenomena across the social, technological, and natural worlds. He focuses on statistical modeling of network structure, network evolution, and spread of information, influence and viruses over networks. Problems he investigates are motivated by large scale data, the Web and other on-line media. He also does work on text mining and applications of machine learning.
Academic Appointments
- Associate Professor, Computer Science
- Member, Bio-X
- Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
- Member, Wu Tsai Neurosciences Institute
Professional Education
- BSc, University of Ljubljana, Sloveni, Computer Science (2004)
- PhD, Carnegie Mellon University, Computer Science (2008)
Videos
Graph Node Embedding Algorithms (Stanford - Fall 2019)
Jure Leskovec, "The Web as a Laboratory for Studying Humanity", by Jure Leskovec 2011-10-24:
CS224W: Machine Learning with Graphs | 2021 | Lecture 1.1 - Why Graphs
Jure Leskovec "Deep Learning on Graphs"
CS224W: Machine Learning with Graphs | 2021 | Lecture 11.3 - Query2box: Reasoning over KGs
Jure Leskovec: "Large-scale Graph Representation Learning"
Jure Leskovec, Stanford Computer Science
CS224W: Machine Learning with Graphs | 2021 | Lecture 10.1-Heterogeneous & Knowledge Graph Embedding
Jure Leskovec - Web Science Meets Network Science Workshop
CS224W: Machine Learning with Graphs | 2021 | Lecture 4.1 - PageRank
Affiliation Network Models for Densely Overlapping Communities in Networks, Jure Leskovec
What's New and Next? | Jure Leskovec
Recent Advancements in Graph Neural Networks | Jure Leskovec
CS224W: Machine Learning with Graphs | 2021 | Lecture 3.2-Random Walk Approaches for Node Embeddings
Jure Leskovec | Advancements in Graph Neural Networks
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