We are in the dawn of a new era of structural biology where machine learning is actively being used in real applications to predict, visualize, and design proteins and other biological molecules. At the intersection of AI and biology, our group develops algorithms that can leverage new sources of data, especially cryo-EM imaging data, to push the frontiers of structural biology research with impactful applications in scientific discovery, therapeutic development, and bioengineering.
We are a multi- and interdisciplinary group in the Department of Computer Science at Princeton University interested in developing and applying state-of-the-art AI and computational tools for structural biology research. Our current research focuses on three core areas:
- Cryo-EM reconstruction
- Generative modeling of proteins
- In situ visual proteomics and cryo-ET
Computational problems in protein biology pose unique challenges requiring the development of novel methods that span many domains of AI research including geometry, vision, and language. We believe that methods motivated by structural biology applications can inspire algorithms of general interest in machine learning, and we seek to make cross-cutting connections across areas. Our research program also emphasizes collaboration with experimentalists to interpret data from the latest experimental tools in structural biology.