Writing
Research
Physical Review Letters · 2019
Theoretical study of active matter at fluid interfaces, analyzing instability formation and flow constraints in a viscous setting.
Nonlinearity · 2018
How diffusion constrains the effectiveness of optimal incompressible flows, with consequences for achievable mixing rates and filament scales.
Journal of Nonlinear Science · 2018
Reduced shell-model formulation for studying optimal mixing, capturing multiscale transport behavior while remaining analytically tractable.
Journal of Applied Physics · 2016
Theoretical treatment of the pressure threshold for droplet vaporization under ultrasound, aimed at clarifying phase-change contrast-agent behavior.
Ph.D. Thesis, University of Michigan · 2018
Doctoral thesis investigating optimal mixing strategies through control-theoretic analysis of the advection-diffusion equation.
Full citation record on Google Scholar.
About
I'm a data scientist and machine learning engineer with a background in physics, applied mathematics, and scientific computing.
My work sits at the intersection of AI, computation, and mathematical modeling. Across industry and research settings, I've worked on machine learning, optimization, forecasting, simulation, and technical software — with Python as the main tool for building models and experiments.
That training shapes the way I approach engineering problems: start from the mechanism, make the assumptions explicit, and build models that are useful rather than decorative.
Topics that appear often
MIT
B.S. in Physics
Cambridge, MA
University of Michigan
M.S. in Applied Mathematics
Ann Arbor, MI
University of Michigan
Ph.D. in Physics
Ann Arbor, MI · 2018