About Me

I’m an AI/ML graduate student working at the intersection of applied machine learning and algorithmic accountability. My academic background spans machine learning, evolutionary computation, neural networks, and responsible AI engineering, and I’m currently completing a master’s degree with a focus that bridges technical AI research and the governance questions increasingly shaping how AI gets built and deployed.

My research interests include bio-inspired approaches to AI system design, exploring how principles from biological systems can inform more robust, adaptive machine learning architectures. Alongside that, I’m increasingly focused on a question I think is under-addressed in the field: as AI systems get deployed into high-stakes domains like hiring, lending, and insurance, who verifies that they behave the way they’re supposed to?

That question led me to found a small AI compliance venture focused on algorithmic auditing by helping organizations navigate emerging bias-audit and algorithmic transparency requirements (state-level AI employment laws, the EU AI Act, and the broader regulatory landscape taking shape around automated decision-making). I see this work as a natural extension of my technical research: understanding how AI systems actually work is a prerequisite for being able to meaningfully audit them.

I’m interested in roles and collaborations at the intersection of ML engineering, AI governance, and regulatory technology particularly where technical rigor and policy relevance meet.