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Data Science Applications

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Hear Prof. Jure Leskovic

The 2024 DSA Workshop

“You are here to see the latest trends & understand how to be ready for the future.”
- Jure Leskovic

The 2024 DSA Workshop brought together leaders from academia and industry to showcase recent advances in Machine Learning and AI in Relational domains, Foundation Models, and Agents. 

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 Leskovek at the lecturn

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.

Meet the DSA Team

The Data Science Applications lab is a vibrant epicenter of research with a deep commitment to community building. The founding DSA team is deeply committed to building collaboration with industry partners to pioneer solutions that address real-world challenges.

Jure Leskovik, Director of Data Science Applications

People

  • Associate Professor of Computer Science and, by courtesy, of Education
  • Associate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment
  • Assistant Professor of Computer Science
  • Thomas M. Siebel Professor of Machine Learning, Professor of Linguistics, of Computer Science and Senior Fellow at the Stanford Institute for HAI
  • Cadence Design Systems Professor, Professor of Electrical Engineering and of Computer Science
  • Assistant Professor of Computer Science

DSA Invites Corporate Engagement

The Data Science Applications program welcomes engagement from industry to provide insight into real-world use cases and funding to drive solutions with a global impact. Partner with DSA through our Affiliate Program and accelerate innovation. Contribute to the definition of research projects, recruit Stanford students, send a visiting scholar: these are just some of the opportunities membership to our Affiliate Program will offer. Contact Joseph.Huang [a] Stanford.edu to learn more.

Events
Take a look inside a DSA event

A DSA event: be at the frontier of innovation

A key benefit of membership to the DSA Affiliate Program is access to exclusive events that inspire and connect. Hosted on the beautiful Stanford campus, they are designed to take members on a deep dive into innovation while encouraging casual networking.