The AI-native platform for next-generation protein characterization.
Protein therapeutic candidates require slow, manual, error-prone characterization before clinical advancement, creating a bottleneck that can’t keep pace with AI-driven drug discovery.
An AI-native platform that uses frontier AI models with deep memory to automate next-generation protein characterization for drug development.
Enterprise pharmaceutical and biotech drug development teams characterizing protein therapeutics.
Chemistry postdoc and Damon Runyon Cancer Research Fellow in Prof. Carolyn Bertozzi's lab (2022 Nobel Prize in Chemistry) at Stanford University. Next-generation scientific leader in chemical biology and glycobiology with 1600+ citations & h-index 22. Received my B.S. double major in chemistry & mathematics at UC San Diego (2016) and Ph.D. in materials chemistry & analytical chemistry at the University of Wisconsin-Madison (2023).
Previously received my B.S. in Biology from UNC-Chapel Hill. I then joined Dr. Steven Carr’s Proteomics Platform Lab at the Broad Institute of MIT and Harvard, where I developed new methods in mass spectrometry. I later became a Ph.D. student and NSF-GRFP fellow in the Department of Biology at Stanford University, where I was co-advised by Dr. Carolyn Bertozzi (2022 Nobel Laureate in Chemistry) and Dr. Or Gozani.
I am a 2x YC Founder. Previously, I built AI for go-to-market teams as the 2nd hire and founding engineer at Nooks. Then, I forward deployed myself in research labs at the University of Washington, UCSF, and Stanford, where I was mentored by world-renowned scientists in analytical chemistry and cancer research. In < 1 year, I published 2 papers and won 4 national awards. Now, I'm building ultrafast AI models for drug discovery teams to characterize proteins and pharmaceutical candidates.


