Events
FDS Statistics & Data Science Seminar
Machine learning for aging and spatial omics
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Speaker: Eric Sun (Stanford) PhD Student Stanford University Monday, January 27, 2025 11:30AM - 1:00PM Lunch at 11:30am in room 1307
Talk from 12:00-1:00pm in room 1327A and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=092a7568-7c71-47b0-b0da-b233012bcd25 |
Abstract: Aging is a highly complex process and the greatest risk factor for many chronic diseases including cardiovascular disease, dementia, stroke, diabetes, and cancer. Recent spatial and single-cell omics technologies have enabled the high-dimensional profiling of complex biology including that underlying aging. As such, new machine learning and computational methods are needed to unlock important insights from spatial and single-cell omics datasets. First, I present the development of high-resolution machine learning models (‘spatial aging clocks’) that can measure the aging of individual cells in the brain. Using these spatial aging clocks, I discovered that some cell types can dramatically influence the aging of nearby cells. Next, I present new computational and statistical methods for overcoming the gene coverage limitations of existing spatial omics technologies, which have enabled the discovery of gene pathways underlying the spatial effects of brain aging. Finally, I introduce several methods for improving the reliability and robustness of high-dimensional data visualizations.
Speaker bio: Eric Sun is a final year Ph.D. Candidate in the Biomedical Informatics program at Stanford University, where he is jointly advised by Dr. James Zou and Dr. Anne Brunet. Prior to Stanford, Eric received an A.B. in Chemistry and Physics (summa cum laude) and a S.M. in Applied Mathematics from Harvard University. His research has focused on developing machine learning and computational methods to analyze high-dimensional biological datasets (e.g. spatial and single-cell omics) with several applications towards dissecting brain aging at single-cell resolution. Select highlights from his past and current research include building high-resolution machine learning models (‘spatial aging clocks’) to measure the aging of individual cells and the development of new computational tools for improved imputation of spatial omics data and for more reliable and robust visualizations of high-dimensional data. Eric’s research has been supported through the Paul & Daisy Soros Fellowship for New Americans, the Knight-Hennessy Scholars Program, and the NSF Graduate Research Fellowship Program. His future research program will concentrate on building innovative machine learning and artificial intelligence tools to dissect the complex biology of aging and accelerate the discovery of interventions targeting aging and its associated diseases.
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