معرفی
Mark Gerstein is the Albert L Williams Professor of Biomedical Informatics and Professor of Molecular Biophysics & Biochemistry, Computer Science, and Statistics & Data Science at Yale University School of Medicine. He serves as a primary faculty member in the Biomedical Informatics & Data Science department and holds joint appointments across multiple departments, reflecting his interdisciplinary research approach.
Gerstein earned his A.B. in physics from Harvard in 1989, followed by a PhD in theoretical chemistry and biophysics from Cambridge University in 1993. He completed postdoctoral research in bioinformatics at Stanford University from 1993 to 1996 before joining Yale in 1997 as an assistant professor. In 2003, he became co-director of the Yale Computational Biology and Bioinformatics Program.
His research spans biomedical data science with particular emphasis on machine learning applications, human genome annotation, disease genomics, and genomic privacy. The Gerstein Lab has been engaged in biomedical data science for approximately 25 years, initially focusing on macromolecular structure and simulation before shifting toward genomic research. The lab serves as a connector between biomedical data generation and analytic approaches from statistics and computer science, particularly AI-driven methods. Much of this work occurs within large consortia including ENCODE, 1000 Genomes, and PsychENCODE.
Gerstein's recent publications reveal a strong focus on integrating diverse biomedical data modalities with AI approaches, particularly in neurogenomics and cancer genomics. His work increasingly examines the intersection of wearable technology data with genomic information, as well as developing frameworks for secure genomic data sharing. The research demonstrates a consistent trajectory toward more complex data integration and privacy-preserving analytical approaches.
With an H-index exceeding 200 and over 700 publications, Gerstein has made significant contributions to prominent scientific venues including Science, Nature, and Cell. His work bridges computational approaches with biomedical applications, particularly in understanding brain disorders and cancer through genomic analysis.
The Gerstein Lab maintains active collaborations across Yale and with numerous external institutions through various consortia. Current research directions include fusing diverse biomedical data modalities (image data, biosensor data, and textual data from publications and electronic health records) using the genome as an organizing platform. Recent progress includes linking genetic variants to biosensor outputs, developing ensemble machine-learning approaches for cryo-EM image processing, and creating large-language models for automatic bioinformatics code generation.