About

I came to data science and machine learning from a foundation in neurobiology, a background that taught me to think in terms of systems with interacting components and feedback loops. That perspective helps me focus on defining the right questions, measuring the right things, and building models whose assumptions are both explicit and testable. I’m comfortable with uncertainty and design models and pipelines so that their limitations are visible and improvable.

I’ve worked on problems in healthcare, transportation, and infrastructure. I care deeply about the entire lifecycle of an ML system — from data quality and feature engineering to model choice, serving, monitoring, and documentation. I prefer owning the full loop from problem definition to deployed system rather than only building the model in isolation.

I’m drawn to domains where data systems touch real people and real infrastructure, including healthcare, public transit, energy, and supply chains. I focus on making machine-learning systems that are interpretable, robust, and fairly evaluated across different populations. I think critically about trade-offs between accuracy and simplicity, automation and human oversight, and short-term speed versus long-term maintainability. I believe in writing things down — whether design documents, evaluation criteria, or limitations — so that decisions and reasoning remain clear to others.

I want to work on problems that have public or societal impact. That doesn’t necessarily mean only in nonprofits or government — it means choosing roles and projects where outcomes improve something meaningful beyond a single metric or shareholder return, such as patient outcomes, equitable access to services, or more reliable infrastructure. I value clarity of purpose, ethics, rigor, and teams that take those seriously.

Looking ahead, I see myself continuing to build and lead applied ML work — either as an individual contributor owning complex systems or as someone who helps shape how teams build, evaluate, and deploy them. I’m not chasing the latest algorithm for its own sake; I’m focused on systems that work in production and that teams can trust.