AI+Science is a core group of faculty in EECS focused on the intersection of AI+SCIENCE.
Our mission is two-fold:
1) to leverage scientific insight to develop new machine learning methods, and
2) to develop and leverage new machine learning methods to advance science.
Admissions: Please apply directly to the UC Berkeley EECS department. All AI+SCIENCE applicants should apply to CS-AI-SCIENCE as their primary choice. Please ensure to list your scientific areas of interest in your application, as well as faculty of potential interest. We sometimes take students from other programs, including the Center for Computational Biology, Biophysics, Mathematics, Statistics, and Chemical and Biomolecular Engineering.
Research interests
- Ioannidis: machine learning in genomics; variant effect prediction; sequence-to-phenotype prediction
- Krishnapriyan: physics-inspired machine learning methods; geometric deep learning; inverse problems; development of machine learning methods informed by physical sciences applications including molecular dynamics, fluid mechanics, climate science
- Listgarten: development of new machine learning methods for problems in molecular biology and chemistry, including data-driven design, and with current emphasis on problems in protein engineering
- Song: applications of machine learning to biology; variant effect prediction; proteins; immunology; single-cell genomics; statistical and population genetics