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Jure Leskovec on Muck Rack

Jure Leskovec

Stanford
As seen in: Nature, arXiv, PNAS, The Conversation, Flipboard, Science Magazine, BioRxiv, Cell Press, Foreign Affairs, Towards Data Science and
Professor of #computerscience @Stanford; Co-founder at Kumo.AI #machinelearning #graphs.
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Articles

Universal cell embedding provides a foundation model for cell biology

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By Yanay Rosen, Yusuf Roohani, Ayush Agrawal, Stephen R. Quake, Jure Leskovec
| Nature Verified
Abstract Developing a universal representation space for cells that encompasses the tremendous molecular diversity of cell types across species would be transformative for cell biology. Recent work using single-cell transcriptomic approaches to create molecular definitions of cell types in the form of cell atlases has provided the necessary data for such an endeavour1,2,3. Here we present the universal cell embedding (UCE) foundation model.
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Mining of massive datasets

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By Jure Leskovec, Anand Rajaraman, Jeffrey Ullman
| Dialnet - Universidad de La Rioja
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets.
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Why LLMs Aren’t a One-Size-Fits-All Solution for Enterprises

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By Jure Leskovec
| Towards Data Science
are racing to use LLMs, but often for tasks they aren’t well-suited to. In fact, according to recent research by MIT, 95% of GenAI pilots fail — they’re getting zero return. An area that has been overlooked in the GenAI storm is that of structured data, not only from an adoption standpoint, but also from a technological front. In reality, there is a goldmine of potential value that can be extracted from structured data, particularly in the form of predictions.
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