About Me

Hi — I’m a Senior AI Engineer at Renesas Electronics America, where I build deep neural networks for embedded systems. My work sits at the intersection of computer vision, signal processing, and hardware-aware optimization. In practice, that means translating mathematical models into systems that are not only accurate, but also efficient, lightweight, and fast enough to run on constrained devices.

Before joining Renesas, I was an AI Consultant at Boston Consulting Group and a researcher at the University of Chicago’s Thirty Million Words Center, where I worked under John List and Dana Suskind. Across industry and academia, the common thread in my work has been using rigorous quantitative thinking to solve real-world problems.

I’ve always been drawn to mathematics and algorithms. This blog is a space for me to think out loud about those topics — especially the foundations of machine learning and AI. I prefer to understand ideas from first principles: small toy examples, clean derivations, and proofs that make the core insight obvious. Whenever possible, I like to strip away the framework and implement algorithms by hand. To me, the best way to learn AI is still with pen and paper.

Outside of work, you’ll usually find me playing basketball or football (soccer), often in rec leagues around Chicago.