I'm an applied mathematician working across dynamical systems, numerics, and AI compute.
My academic training was in nonlinear dynamics — PhD in physics and MS in applied math at the University of Michigan. My thesis applied PDE-constrained optimal control to fluid mixing, using spectral and finite-difference solvers built from scratch. In addition, I worked on other fluid dynamics problems in active matter and acoustic droplet vaporization.
Out of graduate school I spent two years as a data scientist and machine learning engineer at a fintech startup, building a suite of time-series forecasting models — ARIMA-like models, Markov-chain models, and time-delay embedding methods from nonlinear dynamics.
Since 2021 I've been at Quadric, a custom AI inference processor startup, as the in-house numerics lead. I've implemented floating-point hardware units in Verilog, built company-wide numerical testing methodology, developed fixed-point validation tooling and ONNX-to-silicon error tracing, led the first Ax=b solvers on the chip, and developed numerically stable algorithms for neural network ops.
