Bioengineering

Aditi Krishnapriyan's research interests include machine learning methods for the natural sciences, generative modeling, understanding what ML models learn from scientific data, molecular and materials modeling, multi-scale dynamics, statistical mechanics, numerical methods, and optimization.

Her research group develops machine learning methods motivated by the distinct challenges and opportunities of the natural sciences. A central theme is understanding what machine learning models learn from scientific data, rather than assuming physical structure must be hand-engineered into them, and...

The long-term goals of our research are to understand how protein conformational ensembles are reshaped by perturbations, such as mutation and ligand binding, and to quantify how these perturbations impact protein function and organismal fitness. To accomplish these goals, we create new computational and biophysical approaches to study how proteins move between different conformational states. As a graduate student, with Tom Alber at UC Berkeley, James established room temperature X-ray data collection techniques and electron density sampling strategies to define protein conformational...

Yun Song is a professor of Computer Science and Statistics. He received BS degrees in mathematics and physics from MIT, and a PhD in physics from Stanford University. His current research lies at the intersection of AI/ML, statistics, and biology. He is generally interested in developing robust and efficient computational tools and statistical methods to facilitate the research of the broad biomedical community, while also getting deeply involved in data analysis and interpretation.

He is currently serving as the Faculty Director of the Center for Computational Biology, and a co-Director of...

Jennifer Listgarten is a Professor in UC Berkeley's EECS Department , Center for Computational Biology, Bioengineering, and a member of the steering committee for the Berkeley AI Research (BAIR) Lab. From 2007 to 2017, she was at Microsoft Research, through Cambridge, MA, Los Angeles and Redmond, WA. Before that, she did her PhD in the machine learning group at the University of Toronto.

Her expertise and interests are broadly in the areas of AI/machine learning, applied statistics, and computational biology. Her group focuses on both methods development, and also in working closely with...