PhD, Computational Biology Prof., Weizmann Inst.
Biography
Eran Segal heads a multi-disciplinary team of computational biologists and experimental scientists in Computational and Systems biology. His research focuses on Microbiome, Nutrition, Genetics, and their effect on health and disease. His aim is to develop personalized medicine based on big data from human cohorts.
Prof. Segal published over 200 publications, and received several awards and honors for his work. During the COVID-19 pandemic, Prof. Segal developed models for analyzing the dynamics of the pandemic and served as an advisor to the government of Israel.
Before joining the Weizmann Institute, Prof. Segal held an independent research position at Rockefeller University, New York.
Education: Prof. Segal was awarded a B.Sc. in Computer Science summa cum laude in 1998, from Tel-Aviv University, and a Ph.D. in Computer Science and Genetics in 2004, from Stanford University
Talk
AI and Data Science Showcase:
Weizmann Inst. of Science
Pheno.AI manages The Human Phenotype Project with a mission to make accessible the world’s largest collection of deep-phenotype multi-omics datasets to improve human health. We do so by collecting and enabling others to collect, organize, analyze and make accessible these datasets
Personalized medicine based on deep human phenotyping
The technological and procedural building blocks, pitfalls and lessons learnt of building a deep phenotyped, multi-omics cohort
Session Abstract – PMWC 2023 Silicon Valley
Showcase Track S1 - January 25 9.00 A.M.-1.15 P.M.,Showcase Track S1 - January 26 11.30 A.M.-1.15 P.M.,Showcase Track S1 - January 27 11.00 A.M.-1.15 P.M.
The PMWC 2023 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.
