Mark Gerstein, PhD

The Gerstein lab has been engaged in biomedical data science for the past ~25 years – before the field had a defined name. We initially focused on macromolecular structure and physical simulation due to the availability of data and a well-developed calculational formalism. While we continue to work in these areas, the excitement surrounding the human genome has led us to increasingly focus on genomics. Overall, the lab serves as a connector, bridging the vast data generation in the biomedical sciences with analytic approaches from statistics and computer science, particularly AI-driven methods. Much of our work takes place within large consortia, such as ENCODE and 1000 Genomes.

cloud computing ;  computational modeling ;  deep learning ;  network analysis ;  neural networks ;  signal processing ;  wearable devices ;  language models  

artificial intelligence ;  computational biology ;  computational neuroscience ;  computational psychiatry ;  evolution ;  machine learning ;  network neuroscience  

Mark Gerstein is the Williams Professor of Biomedical Informatics at Yale University, affiliated with Molecular Biophysics and Biochemistry and Statistics and Data Science. His research involves applying AI and machine-learning approaches to large-scale biomedical data. He is specifically interested in disease genome annotation, gene networks, macromolecular simulation, and biosensor analysis.