Research Interests

🪐 Research

I am a cosmologist working at the intersection of fundamental physics, data analysis, and artificial intelligence, with the goal of extracting the full physical information encoded in the large-scale structure (LSS) of the Universe.
My research seeks to test the nature of dark energy, dark matter, and gravity by developing novel, interpretable, and computationally efficient inference methods for cosmological surveys.

The next generation of galaxy surveys, including DESI, the Vera Rubin Observatory LSST, Euclid, and the Nancy Grace Roman Telescope, will map the 3D distribution of cosmic structure with unprecedented precision. Fully harnessing this information requires moving beyond traditional two-point statistics, such as the power spectrum, which capture only a fraction of the cosmological signal. My work focuses on designing new statistical estimators and AI-driven inference frameworks that can efficiently extract the non-Gaussian information generated by nonlinear structure formation.

Wavelet Scattering and Non-Gaussian Cosmology

I pioneered the application of the Wavelet Scattering Transform (WST) to cosmological data, introducing a new family of summary statistics that bridge the gap between conventional clustering estimators and convolutional neural networks.
Using state-of-the-art simulations such as AbacusSummit, I led the first WST-based likelihood analysis of real galaxy data from the BOSS CMASS DR12 sample, achieving constraints up to six times tighter than the two-point correlation function. More recently, I co-led a project applying the WST and wavelet phase harmonics to cross-correlations of CMB lensing and weak lensing maps, demonstrating significant gains over standard cross-power spectra.

AI-Driven Inference and Simulation-Based Cosmology

As a member of the NSF–Simons AI Institute for the Sky (SkAI), I am extending these ideas to AI-based field-level inference, leveraging machine-learning techniques such as normalizing flows and diffusion models to emulate complex cosmological fields and accelerate parameter estimation. This work aims to unify physically interpretable and data-driven approaches, enabling optimal, simulation-calibrated inference from DESI and LSST.

Testing Fundamental Physics

Earlier in my career, I developed analytical and hybrid simulation techniques to study modified gravity theories and their screening mechanisms, constructing fast and accurate models of structure formation in non-standard cosmologies. These methods continue to inform my current efforts to test dark-energy models, neutrino masses, and primordial signatures through large-scale-structure data.

Through these projects, I strive to build a cohesive program that connects physical modeling, high-performance computing, and AI-based inference to uncover the fundamental laws governing cosmic evolution.