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SynBioBeta Speaker

Alex Rives

CZI Biohub

Head of Science

Alex Rives is head of science at the Chan Zuckerberg Initiative, leading its work at the intersection of artificial intelligence and biology. Alex was a founder and chief scientist at EvolutionaryScale, a public-benefit company developing AI to accelerate scientific discovery in biology, and delivering transformative AI systems and applications to benefit the global scientific community. Rives is a core institute member at the Broad Institute, and an assistant professor in the Department of Electrical Engineering and Computer Science at MIT. He will continue to be affiliated with Broad Institute and MIT.As a scientist, Rives has done pioneering work in artificial intelligence for biology. He started and led the Evolutionary Scale Modeling, or ESM, project at Meta’s fundamental AI research lab, which developed the first large-scale transformer language models for proteins. The ESM models are widely recognized as a breakthrough in artificial intelligence for biology, and opened the field of language modeling in biology. These models are used by scientists worldwide, enabling applications including designing therapeutic proteins such as antibodies, predicting the effects of mutations in the human genome, illuminating evolutionary history, discovering new proteins, and creating models of the cell. Rives earned a bachelor’s degree in philosophy and biology from Yale University and a doctorate in computer science from New York University.

Sessions Featuring

Alex

This Year

TBD

Session lineup still growing

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Fireside Chat

12:00 AM

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8:30 AM

Human Health

From Cells to Patients: Solving the Scale Mismatch in Virtual Biology

Drug discovery often measures biology at the cell level while interventions work at the tissue, organ, or whole-patient scale. This mismatch can make accurate cell-level predictions irrelevant in the clinic. This session dives into strategies to bridge that gap: multiscale modeling that nests single-cell dynamics within organ-level simulations, spatial transcriptomics that preserve context, and surrogate models that translate cell-level outputs into clinical biomarkers. Speakers will ask: how do we ensure virtual biology reflects not just what cells do in isolation, but how biology behaves in the real complexity of patients?

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