September 2026
Started at OpenProtein.AI, formally as an AI Researcher, but functionally have been helping on whatever I am needed on most: model evaluation, grant writing, documentation, sales, product.
Paul Kim
I am an AI Researcher at OpenProtein.AI. Since 2018, I’ve worked in various roles at the intersection of machine learning and biology, including small molecule drug discovery, cryo-EM, and de novo protein design. I’m interested in using machine learning to advance our understanding of and ability to manipulate biology, especially for therapeutics.
I did my PhD in Molecular Engineering at the Institute for Protein Design supervised by David Baker. Prior, I did my undergrad in statistics at UC Berkeley.
Beyond my work, I’m broadly interested in history, linguistics, human individual and group psychology, and business. See my reading, YouTube, and podcast recommendations here. I also love food: see my Google Maps lists here.
Timeline
August 2026
Graduated from the Baker Lab.
Spring 2026
We published our preprint on a deep learning based approach for de novo design of DNA binding proteins.
Fall 2022
Began my PhD in Molecular Engineering at UW. Joined the Baker lab and contributed to projects involving computational protein design.
June 2021
Joined the Simons Machine Learning Center, where I worked with Dr. Tristan Bepler on Ptolemy, a computer vision tool for cryo-electron microscopy automation. See our papers here and here.
May 2021
Left the MS program, due to lack of alignment with my goals of pursuing a PhD. The MS is a great program but is much more focused on industry preparedness.
August 2020
Enrolled in the Computational Biology MS program at Carnegie Mellon. Worked briefly with Dr. Hosein Mohimani at CMU, where I supported projects for adenylation domain specificity prediction.
June 2020
Did a brief internship at Silicon Therapeutics (since renamed Roivant Discovery, spun out as Psivant, recently rebranded as PsiThera). Learned about physics-driven structure-based drug discovery.
March 2018
Joined the Machine Learning Research Group at Bayer, initially as an intern, under the guidance of Dr. Djork-Arné Clevert. Worked on early protein sequence representation learning methods (you’d call them “foundation models” today) using LSTMs and GRUs, as well as protein-ligand binding prediction using these representation learning methods. Our paper here.
December 2017
Graduated from UC Berkeley with a BA in statistics.
May 2016
Watched AlphaGo beat Lee Sedol and got interested in deep learning. Changed majors from chemical biology to statistics.
August 2013
Began my undergrad at UC Berkeley in chemical biology.