About
A chemical engineer who fell for data.
I’m a chemical engineer who fell for data. Over more than a decade at Merck, I’ve worked at the seam between the laboratory and the model — using data-rich experimentation and in-silico tools to make process development faster, cleaner, and more certain. My work has spanned small molecules and biologics, from the first kilogram-scale electrochemistry demonstrations to Neural ODE hybrid models and algorithmic optimization deployed across the pipeline.
What I care about most is turning a hard, messy development problem into something a team can reason about quantitatively — and then building the tools that let other scientists do the same without needing to be modelers themselves. I’ve spent as much energy on adoption and mentoring as on the algorithms, because a capability that only one person can run isn’t really a capability.
Along the way I earned an M.S. in Analytics from Georgia Tech to sharpen the machine-learning side, on top of my B.S. in Chemical Engineering from Delaware. I’ve been fortunate to work on medicines that mattered — including antivirals developed during the COVID-19 pandemic — and to share that work through publications, invited talks, and industry consortia.
Beyond the lab
Away from work, I’m a classical pianist with a particular love for Franz Liszt.
Education
- M.S. Analytics
Machine learning and computational data analytics.
- B.S. Chemical Engineering
Minors: Materials Science, Chemistry, Biomechanical Engineering, and Biochemical Engineering.