Siddharth Mishra-Sharma

Bio: Siddharth Mishra-Sharma is an IAIFI Fellow at the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), affiliated with the Center for Theoretical Physics at MIT and the Department of Physics at Harvard. Prior to this, he was a postdoc at NYU’s Center for Cosmology and Particle Physics from 2018-2021 and obtained his Ph.D. in Physics from Princeton University in 2018. Siddharth is interested in developing methods that leverage deep learning specifically and differentiable programming more generally to accelerate searches for new physics using astrophysical and cosmological data at all observable scales.

Talk Title: Illuminating the dark Universe with probabilistic machine learning

Abstract: The next several years will witness an influx of astrophysical data that will enable us to accurately map out the distribution of matter in the Universe, image billions of stars and galaxies to unprecedented precision, and create the highest-resolution maps of the Milky Way to-date. These observations may contain signatures of new physics, including hints about the nature of dark matter, offering significant discovery potential. At the same time, the complexity of the data and the presence of unknowable systematics pose significant challenges to robustly characterizing these signatures through conventional methods.

I will describe how overcoming these challenges will require a qualitative shift in our approach to statistical inference in astrophysics, bringing together several recent advances in generative modeling, differentiable programming, and simulation-based inference. I will showcase applications of these methods to diverse astrophysical systems, emphasizing how these will drive progress on key questions in astro- and particle-physics over the next decade.