Arif Chu

Used SNEC radiation-hydrodynamics code to model early-time emission from core-collapse supernova progenitors, aiming to separate shock breakout from radioactive nickel heating.

Constrained interacting dark matter models using N-body simulations and prepared initial conditions for velocity-dependent dark matter constraints.

Fabricated and maintained lab equipment, conducted Rutherford backscattering experiments for PIONEER's detectors, and produced Ne-19 data for the He-6 CRES group.

Developed an API for a Python Flask web app to coordinate electromagnetic follow-ups for gravitational wave events, using PostgreSQL for database management.

Worked on likelihood-free inference for kilonovae light curves and binary black hole gravitational wave classification, and mentored two high school students.

Worked on transient classification for Vera C. Rubin data, achieving 98% classifier accuracy using PyTorch, OpenCV, and scikit-learn. Won the DiRAC 2022 Summer Research Prize.

Calculated power generated by photons in a haloscope microwave cavity and modeled thermal noise using Python and scipy, with chi-squared goodness of fit tests.

Pursuing a PhD in Physics, researching interacting dark matter models and Type II supernovae classification from observational lightcurves.

Earned two Bachelors of Science in Physics and Astronomy with a minor in Data Science, and was on the Dean's List from 2021 to 2023.

Obtained an International Baccalaureate Diploma with Higher Levels in Mathematics, Physics, and Chemistry, and Standard Levels in Global Politics, English A, and Spanish Ab Initio.