Hello, hello! 👋

I’m a Ph.D. Candidate at the MIT Kavli Institute for Astrophysics and Space Research and the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI). I seek to understand the nature of Dark Matter and its role in Galaxy Formation and Evolution, especially at the smallest scales. My research involves developing machine learning techniques to analyze cosmological simulations and data from astronomical surveys.

Currently, I’m working with Prof. Lina Necib at MIT. From August 2022 to January 2023, I was a Pre-Doctoral Researcher at the Center for Computational Astrophysics (CCA) at the Simons Foundation, collaborating with Prof. Rachel Somerville in the Galaxy Formation group.

Projects I’m working on:

1. Inferring the dark matter density profiles of dwarf galaxies using graph neural networks and simulation-based inference [arxiv].

2. Generating dark matter halo merger trees using recurrent flow-based generative models [arxiv].

3. Creating synthetic Gaia DR3 surveys from Milky Way-like galaxies in the FIRE simulation [arxiv].

4. Using kinematics of accreted stars to characterize the galaxy accretion history of the Milky Way.

5. Simulation-based inference to understand structure of stellar streams with Prof. Nora Shipp.

Feel free to explore my work and learn more about the exciting ways that machine learning can be used to understand the Universe!

My CV (including list of publications) can be found here.