I am a senior applied machine-learning and data-science leader with a PhD in Biological Sciences, specializing in molecular biology. I work across scientific questions, analytical systems, and the decisions those systems are meant to improve.
Scientific depth
My research centers on machine-learning-guided functional discovery and engineering in undercharacterized microbial systems. Oxygen-tolerant nitrogen fixation is a flagship application, connecting comparative genomics, condition-specific multi-omics, protein representation, candidate prioritization, and validation planning.
Operational judgment
Industry work has involved causal inference, forecasting, targeting, resource allocation, and analytical implementation. That experience shapes how I think about scientific ML: models have to fit the decision, expose their assumptions, survive operating constraints, and support measurement after adoption.
Translation
Much of my work is translation: keeping biological context visible in model design, explaining uncertainty to decision-makers, and tying analytical output to experiments or operating choices.