Some fun selected scientific projects I've done over the years, both for courses and for research, can be found in the pdf links below.
Astrophysics: As a SURF undergraduate intern at Caltech's LIGO Lab I investigated the computational costs of applying Bayesian parameter-estimation methods to gravitational-wave searches when distributed across a parallel CPU cluster using a particular flavor of Markov-chain Monte Carlo. For an undergraduate astronomy-lab final class project I conducted a series of photometric observations of the variable star DY Herculis, and used maximum-likelihood estimation with Markov-chain Monte Carlo (MCMC) and the Lomb-Scargle method to confirm previously reported decreases in period. As an undergraduate researcher in Stanford's KIPAC I was involved in a paper on LIGO cosmology which was published in The Astrophysical Journal.
Machine learning & computer vision: I did some investigation into (non-machine learning) hyperspectral sparse unmixing techniques for a graduate course in convex optimization. For an advanced computer vision graduate course I worked on a final project to improve methods of spacecraft pose estimation. My PhD research is currently focused on deep-learning methods for satellite imagery in contexts of particular humanitarian and conflict concern.
Physics & math teaching: I have produced a number of nicely-LaTeX'ed equation sheets over the years for courses in my BS, MS and PhD. The one for differential geometry (based on Tevian Dray's presentation in Differential Forms and the Geometry of General Relativity) is especially nice and can be found here. When I was a teaching assistant for the Summer Science Program in 2020-21 I gave a fun TA lecture on general relativity, which also gives a brief gloss on differential geometry using Sean Carroll's notation.