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.
Discovering the particle properties of dark matter and probing cosmology with strong gravitational lensing and machine learning.
Revealing the origin of the Universe, the nature of its components and how they affect its evolution, and its eventual fate.
Using neural networks to create new statistical analyses for complex, high-dimensional astrophysical datasets.
Learning the Universe
Research Area : Cosmological parameters, dark matter, dark energy, large sky surveys, hydrodynamics simulations, simulation-based inference, emulators, uncertainty estimation and sampling
Future Lens
Research Area : Strong Gravitational Lensing, Dark matter, Mass Function, Hubble Constant, Dark Energy, Computer Vision, Time-Delays, Stellar Populations, 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