Build the system. Measure the result. Show the evidence.

I focus on making each system measurable: defining the evaluation first, understanding failure modes, and keeping model behavior tied to reproducible evidence.

In my current AI R&D intern work, I develop and evaluate EEG-based machine-learning pipelines for seizure classification and cross-patient generalization. Across research and engineering projects, I care about the same things: sound evaluation, clear system boundaries, and software that can be inspected beyond a single metric.