About Me
CTO and Co-Founder at Coefficient Bio. Formerly, I was a Group Leader and Principal Scientist at Prescient Design • Genentech. At Prescient, I led a multidisciplinary project team of machine learning scientists and engineers, molecular biologists, computational biologists, postdoctoral and graduate student researchers to conduct collaborative research on biological foundation models and novel AI/ML approaches to biomolecule design, resulting in new capabilities deployed to drug discovery projects. I was on the Foundation Model and Large Molecule Drug Discovery Leadership Teams, where I set the research and product directions, roadmaps, and long-term AI strategy for Roche and Genentech. I also established and led our collaboration with NVIDIA.
I have published more than 20 scientific papers in journals such as Science Advances, Nature Machine Intelligence, and ACS Nano, and ML conferences including NeurIPS, ICML, and ICLR. I won an ICLR Outstanding Paper Award for innovative work on generative modeling and biology, and seven spotlight awards from the main conference track and workshops at NeurIPS, ICML, and ICLR.
I am on the scientific advisory boards of Atomscale and Guide Labs.
Current
CTO & Co-Founder, Coefficient Bio
Scientific Advisor, Atomscale and Guide Labs
Previous Positions
Principal Machine Learning Scientist & Group Leader, Prescient Design • Genentech
- Led Foundation Models for Drug Discovery Team (Frey Lab)
- Inventor and co-lead, Lab-in-the-loop autonomous antibody design system
Postdoctoral Associate, MIT
Affiliate Scientist, Materials Project, Lawrence Berkeley National Laboratory
Education
Ph.D., Materials Science & Engineering, University of Pennsylvania
- National Defense Science & Engineering Graduate Fellow
- S.J. Stein Dissertation Prize
Awards & Recognition
- ICLR Outstanding Paper Award (2024) - Protein Discovery with Discrete Walk-Jump Sampling
- National Defense Science & Engineering Graduate Fellowship
- S.J. Stein Dissertation Prize, University of Pennsylvania
- Geoffrey Belton Memorial Fellowship
- PhD Career Exploration Fellowship, Merck Quantitative Biosciences
- Howard Hughes Medical Institute C3 Fellowship
- NSF Undergraduate Research Fellowship
- Phi Beta Kappa
Research Impact
- 20+ peer-reviewed publications in Science Advances, Nature Machine Intelligence, ACS Nano, JACS, Chemistry of Materials
- Conference publications at ICLR, NeurIPS, ICML
- Citation metrics: h-index and citation counts available on Google Scholar
Expertise
AI for Drug Discovery: Foundation models, generative modeling, autonomous design systems, active learning, lab-in-the-loop optimization
Protein Engineering: Antibody design, multi-property optimization, de novo design
Machine Learning: Neural scaling laws, discrete diffusion models, energy-based models, graph neural networks, protein language models
Computational Methods: High-throughput screening, property prediction, molecular simulation, materials informatics