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Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
A eureka moment is a two-step process: discovering something new and surprising, and establishing that it is correct. Drawing on her work at Axiom, Carina will discuss the transition from solving mathematical benchmarks to contributing to research, and from proving individual theorems to formalizing mathematical theories in Lean.
A eureka moment is a two-step process: discovering something new and surprising, and establishing that it is correct. Drawing on her work at Axiom, Carina will discuss the transition from solving mathematical benchmarks to contributing to research, and from proving individual theorems to formalizing mathematical theories in Lean.
Stanford Data Science is pleased to host our Spring Distinguished Lecture on June 5th, 2024. Our speaker is Aviv Regev, Head and Executive Vice President, Genentech Research and Early Development.
Aviv Regev's talk is titled: Design for Inference: From Random Experiments to Lab in the Loop
The Distinguished Lecture will start promptly at 4:30pm, followed by a networking reception at 5:45pm, and an opportunity to engage with others in the Stanford Data Science community.
Abstract
All stages of drug discovery and development are incredibly challenging, such that development of new medicines not only is long, complex, and costly, but also suffers from an exceptionally high failure rate, leaving much unmet medical need. These challenges are largely due to the complex, non-linear nature of the underlying scientific problems, from deciphering how cells malfunction in disease, predicting correct targets for therapeutic intervention, generating and designing molecules or other therapeutics to target them, and predicting which patients should be treated and in which dose and regimen. In each of these problems, we are posed with enormous spaces of possibilities, far exceeding those that can be measured in a lab or a patient population, such as the number of possible combinations of gene variants, or drug-like small molecules or therapeutic antibodies. The dramatic advances across different areas of machine learning, from representation learning to generative AI, now open an extraordinary opportunity to tackle each of these challenges to transform drug discovery. A true impact will require a shift across drug R&D, to become part of a “Lab in a Loop,” where experimental or clinical data is collected in order to train a model, the model is used to predict the next set of experiments, and the process is iterated, at scale, both to yield key predictions in any specific project and improve the model for all projects. In this talk, I will describe how we built such a Lab in a Loop of experiments and machine learning in Genentech across our target discovery, drug discovery, and drug development efforts to serve patients with autoimmune disease, neurodegeneration, infectious disease, and cancer.
Aviv Regev is head of Genentech Research and Early Development. Formerly, Regev was Chair of the Faculty and Core Member at the Broad Institute of MIT and Harvard, Professor of Biology at MIT, and a Howard Hughes Medical Institute Investigator. She is founding co-chair of the Human Cell Atlas and a leader in deciphering molecular circuits that govern cells, tissues, and organs in health and their malfunction in disease. She has pioneered foundational experimental and computational methods in single-cell genomics, enabling greater understanding of cell and tissue functions. Regev is a member of the National Academy of Sciences, National Academy of Medicine, American Academy of Arts and Sciences, and a Fellow of the International Society of Computational Biology. Her many honors include the ISCB Overton and Innovator Prizes, Paul Marks Prize, Lurie Prize in Biomedical Sciences, Keio Medical Science Prize, HFSP Nakasone Award, and L'Oréal-UNESCO for Women in Science Award.