Stanford Engineering Data Science Applications

The Mission of Data Science Applications

Helping organizations, from the financial industry to healthcare and beyond, incorporate the power of graph neural networks to tackle their real-world problems and achieve their goals is the mission of Data Science Applications (DSA). The popularity of graph neural networks (GNNs) in the AI community is surging. The work of Stanford professor and co-founder at Kumo.AI, Jure Leskovec, is poised to bring solutions to organizations from its position at the intersection of GNNs, knowledge graphs, and generative AI.

Jure Leskovec

"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."

Jure Leskovec is affiliated with the Stanford AI Lab, Machine Learning Group and the Center for Research on Foundation Models. In the past, he served as a Chief Scientist at Pinterest and was an investigator at Chan Zuckerberg BioHub. Jure recently pioneered the field of Graph Neural Networks and co-authored PyG, the most widely-used graph neural network library. His research received several awards including Microsoft Research Faculty Fellowship in 2011, Okawa Research award in 2012, Alfred P. Sloan Fellowship in 2012, Lagrange Prize in 2015, and ICDM Research Contributions Award in 2019. His work has won 12 best paper awards and 5 10-year test of time awards at a premier venues in these research areas. Jure received his bachelor's degree in computer science from University of Ljubljana, Slovenia, PhD in machine learning from Carnegie Mellon University, and postdoctoral training at Cornell University.