Dr. Jure Leskovec

Professor

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Biography

Jure Leskovec is a Professor in the Department of Computer Science at Stanford University, affiliated with the Stanford AI Lab and Machine Learning Group. He has significantly contributed to the field of Graph Neural Networks and co-authored PyG, a widely-used graph neural network library. His research has focused on using social network analysis and machine learning to combat the COVID-19 pandemic, and he has collaborated with major companies such as Facebook, Pinterest, and Uber. Leskovec has received several prestigious awards for his research, including the Microsoft Research Faculty Fellowship in 2011, the Okawa Research Award in 2012, the Alfred P. Sloan Fellowship in 2012, the Lagrange Prize in 2015, and the ICDM Research Contributions Award in 2019. He has also won multiple paper awards and recognitions for his contributions to data mining and computational biomedicine, particularly in drug discovery. Leskovec completed his Bachelor's degree in Computer Science at the University of Ljubljana in Slovenia and obtained his PhD in Machine Learning from Carnegie Mellon University, followed by postdoctoral training at Cornell University.

Research Interests

Courses

CS 524 CS 224W CS 246 CS 499 CS 499P CS 390A CS 390B CS 390C DDRL 191 BIOE 392 BMDS 299 BIOE 391 CS 399 CS 399P CS 199 CS 199P CME 291 CS 390D CS 192 CS 191 CS 195 CS 191W

Requirements for Stanford University

Doctorate Program
Requirements
GPA Requirement
Required:3.5
TOEFL
Listening
Required:26
Reading
Required:26
Writing
Required:26
Speaking
Required:26
Total
Required:100
GRE General
Verbal
Required:160
Quantitative
Required:165
Analytical Writing
Required:4.5
Overall
Required:4.5
Prerequisites
Bachelor degree from an accredited institution Strong background in mathematics and programming
Application Checklist
  • Statement of Purpose
  • Three letters of recommendation
  • Official transcripts
  • Resume/CV
Specialization Notes

The Computer Science department emphasizes research potential. GRE General is currently optional but recommended for some tracks.