Daniel Tuzes
I work at the intersection of physics, applied mathematics, and finance — building models that turn noisy, complex systems into something precise enough to act on.
Background
My training is in physics: a Ph.D. investigating pattern formation in many-particle systems, combining hands-on experiments with Monte Carlo simulations of the underlying stochastic PDEs, with earlier degrees along the way — all in Hungary and Germany. That work led to papers in Nature Communications and Physical Review B (200+ citations since), and a few awards and scholarships alongside it. I also co-authored a textbook on real and complex analysis, and spent years mentoring students and organizing high-school science competitions.
These days that same instinct — build a model, find exactly where it breaks, then tighten it until it holds — has moved into quantitative finance. I work as a front-office credit quant, building the pricing and hedging models a trading desk runs on, and I'm completing a second Master's, in Financial Engineering, at Baruch College (CUNY). Python and C++ are still how the ideas get built, but they're tools in service of the modeling, not the point of it.
Elsewhere
- GitHub — github.com/danieltuzes
- Three projects live on this same site: Structile, a spatial editor and diff tool for structured data, fpml-convert, which converts FpML interest-rate XML to JSON and back, losslessly enough to round-trip, and explico, a build-time engine for prerequisite-aware, notation-rigorous course content. Fun fact: fpml-convert also plugs into Structile as a companion interpreter, and it runs in the browser with nothing to install.
- explico's first real course, credit-and-derivatives, is live — but it's a playground, not a lecture: a genuine first test of the framework end to end, with lesson content that's AI-written and not yet human-checked.
A handful of old problem-solving write-ups, from when this page was more of a scratchpad, are still here if you're curious — not kept up to date, but not thrown away either.