Most AI stays in the lab. I put it to work.
I'm a biomedical engineer who somehow ended up building production infrastructure for a living. By day I work on enterprise data systems and I'm co-founding Synthyra, we screen drug candidates against entire proteomes so pharma teams can catch bad interactions before they burn $2B finding out the hard way.
Before that I built CompSciLib, an ed-tech platform that grew to 300K+ users across 175 countries without spending a dollar on ads. Still not sure how that happened.
What I'm working on right now:
🧬 Shipping protein language models that run 14,000x faster than the next best thing
🏗️ Figuring out how to make Modal, Cloud Run, and Firestore play nice together
📄 A patent (first one — the paperwork is not as fun as the research)
What I nerd out about outside of work:
🏋️ Lifting heavy things and putting them back down
🏠 Spreadsheets about real estate I haven't bought yet
🥗 Forming new food opinions (strong and non-negotiable)
The gap between "possible" and "deployed" is where the impact is.