I'm a junior in the ACME program at Brigham Young University, with a physics emphasis. I'm minoring in computer science and logic, on track to graduate in April 2028. After that, I'm planning to pursue a PhD in applied math or computational science. My research sits at the intersection of physics, math, and computation.
I'm also interested in philosophy and good writing; you can see what I've been reading lately. I work mostly in python, but I know c++, java, and web stuff.
I'm working on additions to the OpInf package and associated research. I helped create a tutorial on preprocessing techniques like Lift and Learn. Now, we're exploring avenues that differentiable programming opens up in the operator inference domain. Is an operator inference derived ROM suitable for parameter recovery and other similar inverse problems? Can trajectory based post-tuning of operators and bases facilitate learning the underlying dynamics?
I'm designing an adaptive, automated protocol to estimate the relevant parameters of an ion trap, without resorting to the Lamb-Dicke approximation, to enable optimal calibration even in warm or fast regimes. I built a digital twin of a single ion trap in JAX, suitable for real time control. A gradient based pulse optimizer, together with sequential Monte-Carlo methods, find the sequence of square pulses with the maximum expected information gain.
I made this for my advanced software construction class. The server-client model with MySQL relational database allows simultaneous synchronized gameplay across clients.
I built a simulation for the popular card game Flip 7 to discover the optimal strategy. Collaboration with my brother.
A personal journal application that I wrote to document my life in a distraction free environment. Stores data locally encrypted on your disk.