A Math Degree
Opens Every Door

I came into Michigan thinking I wanted to be a physics professor, but a stochastic processes course in my junior year completely changed my trajectory. The beauty of how probability theory and calculus intertwined to model markets was unlike anything I'd seen. I pivoted hard into financial mathematics and landed a summer internship at a Chicago trading firm after my junior year. Two Sigma hired me straight out of undergrad and I've been building systematic equity models ever since. My pure math background — especially real analysis and measure theory — gives me a depth that most of my colleagues with engineering backgrounds don't have.

I started my PhD in pure mathematics focusing on algebraic topology, but the explosion of deep learning during my second year pulled me sideways. I realized that my proof-based mathematical training gave me an enormous advantage in understanding why neural networks work, not just that they work. I transferred my thesis focus to the theoretical foundations of deep learning — generalization bounds, loss landscape geometry, and the implicit bias of gradient descent. DeepMind recruited me before I finished and I now work on foundational ML research. My advice: take your pure math seriously and then pick up programming. The combination is unstoppable.

I chose math at UChicago because I wanted rigor and a clear career path. Actuarial science delivered exactly that. The exam process is demanding but incredibly fair — if you put in the work, you move up. I passed Exams P and FM as a sophomore, got my first internship at Milliman that summer, and never looked back. I'm now an FSA specializing in health insurance product pricing. The math I use daily — survival models, credibility theory, loss distributions — maps almost exactly to what I studied in probability theory and statistics courses. It's one of the most direct translations from classroom to career that exists.