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Jure Leskovec
1,522 posts
@jure

Jure Leskovec

@jure
Professor of #computerscience @Stanford; Co-founder at Kumo.AI #machinelearning #graphs.
Stanford, CA
cs.stanford.edu/~jure/
Joined August 2007
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  • @jure
    Jure Leskovec
    @jure
    Aug 19
    Excited to see this out! Relational learning needs strong benchmarks, standardized evaluation, and easy ways to bring methods to real-world relational data.
    @prior_labs
    Prior Labs
    @prior_labs
    Aug 18
    We’re happy to announce our first release in relational learning at Prior Labs, continuing our commitment to open science. We open-source three pieces of software that we expect to accelerate research in the field towards meaningful, real-world impact. First and foremost, we
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  • @jure
    Jure Leskovec
    @jure
    Aug 13
    Fun conversation with @ItaiYanai on @nightsciencepod about what happens when AI starts doing science: agents that find patterns, generate hypotheses, and try to falsify them with more data. We also got into taste, judgment, and where human scientists still matter most.
    @ItaiYanai
    Itai Yanai
    @ItaiYanai
    Aug 11
    How would you let the LLM dream? [...] In a sense, to replay things, organize them, think about them so that tomorrow when we wake up, our thoughts are more organized than just input-output-input-output. –Jure Leskovec, Stanford Professor, @jure
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  • @jure
    Jure Leskovec
    @jure
    Jul 28
    Exciting milestone for @KexinHuang5 and the @phylo_bio team. Putting AI scientist systems directly in the hands of discovery scientists is where the next big gains in biomedical AI will come from.
    @KexinHuang5
    Kexin Huang
    Phylo
    @KexinHuang5
    Jul 28
    Today, we're delighted to announce Phylo's partnership with Ono Pharma to deploy Biomni Lab to its discovery scientists. At Phylo, we believe agentic AI will fundamentally change how new medicines are discovered. Ono is a global leader in innovative drug discovery with a rich
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  • @jure
    Jure Leskovec
    @jure
    Jul 13
    Excited to share that our Universal Cell Embedding (UCE) paper is published in @Nature ! Single-cell RNA sequencing data gives us an unprecedented look into the diversity of cell biology, but analysis has often been limited to the specific dataset or atlas that was collected.
    Content cover image
    Universal cell embedding provides a foundation model for cell biology
    From nature.com
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  • @jure
    Jure Leskovec
    @jure
    Jul 6
    Modern multimodal models aren't a single decode loop anymore; they're composite. M* is one runtime that serves them all, and it matches or beats every specialized system: up to 2.7× on omni TTS, 12.5× on world-model rollouts. Learn more here: ai.stanford.edu/blog/mstar/
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