Research at the Intersection of Astrophysics and Machine Learning
The institute's main research activities are focused on developing rigorous Bayesian inference frameworks that employ deep learning to accelerate parameter estimation and increase the accuracy of the inferred posteriors.
Black Holes
Breakthrough discoveries about the most fascinating objects in the Universe, black holes, ultimately revealing how these objects act as the beating heart of all galaxies.
Lensing for Dark Matter
Discovering the particle properties of dark matter and probing cosmology with strong gravitational lensing and machine learning.
Cosmology
Revealing the origin of the Universe, the nature of its components and how they affect its evolution, and its eventual fate.
Inference
Using neural networks to create new statistical analyses for complex, high-dimensional astrophysical datasets.
Publications
Learning the Universe
Research Areas : Cosmological parameters, dark matter, dark energy, large sky surveys, simulation-based inference
Future Lens
Research Areas : Strong gravitational lensing, dark matter, Hubble constant, dark energy, computer vision, AGN
Gaia Data
Research Area : Anomaly detection, unsupervised learning, density estimation, point clouds, HR diagram, Milky Way stream
Turbulence
Research Area : Subgrid physics, closure, Navier–Stokes equations, deep learning, recurrent models, Dedalus
X-Ray Astronomy
Research Area : Active Galactic Nuclei, intra cluster medium, X-ray binaries, galaxy clusters, deep learning
