I am a computational astrophysics and ML researcher working at the intersection of time domain astronomy, machine learning, and statistics. Specifically, I am building state of the art machine learning classification and anomaly detection systems for current and future astrophysical surveys like the Legacy Survey of Space and Time (LSST).I graduated with James Scholar Honors from the University of Illinois Urbana-Champaign with a Bachelors in Computer Science and Astronomy and a minor in Statistics. At Illinois, I was awarded the 2024 Stanley Wyatt Memorial Award, given "to the graduating Astronomy major or minor with the most outstanding GPA and track record of undergraduate research." During my time there, I also received 2021 LSSTC Science Catalyst Grant and the 2024 Preble Scholarship for my research in multi-messenger astronomy and machine learning. My senior thesis research involved modelling kilonova discovery rates and developing new machine learning methods to find rare transients.Previously, I have interned in both research and software engineering roles at Country Financial, the National Centre for Supercomputing Applications (NCSA), and IIT Bombay. Fundamentally, I am interested in applying computational methods to solve scientific problems. In particular, I am interested in machine learning, scientific computing, and back-end software development.Feel free to reach out if you want to chat!