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 (2024-present)

Scientific Advisor, Atomscale and Guide Labs

Previous Positions

Principal Machine Learning Scientist & Group Leader, Prescient Design • Genentech (2021-2024)

• Led Foundation Models for Drug Discovery Team (Frey Lab)
• Inventor and co-lead, Lab-in-the-loop autonomous antibody design system

Postdoctoral Associate, MIT (2021)

Affiliate Scientist, Materials Project, Lawrence Berkeley National Laboratory

Education

Ph.D., Materials Science & Engineering, University of Pennsylvania (2016-2021)

• National Defense Science & Engineering Graduate Fellow
• S.J. Stein Dissertation Prize (2021)

Awards & Recognition

• ICLR Outstanding Paper Award (2024) - Protein Discovery with Discrete Walk-Jump Sampling
• National Defense Science & Engineering Graduate Fellowship (2016-2021)
• S.J. Stein Dissertation Prize, University of Pennsylvania (2021)
• Geoffrey Belton Memorial Fellowship (2019)
• PhD Career Exploration Fellowship, Merck Quantitative Biosciences (2020)
• Howard Hughes Medical Institute C3 Fellowship (2013)
• NSF Undergraduate Research Fellowship (2012)
• Phi Beta Kappa (2014)

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, structure prediction, therapeutic optimization, binding affinity prediction

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