Research Interest
"Two things fill the mind with ever new and increasing admiration and awe, the more often and steadily we reflect upon them: the starry heavens above me and the moral law within me." - Immanuel Kant
Our research group is dedicated to advancing the field of astronomy by harnessing the power of modern deep learning techniques. Our primary focus is on expanding the scope of inferences and solving inverse problems to investigate a wide range of topics across all cosmic scales. From exploring the dynamical evolution of planetary systems to constructing precise emulations of 3D stellar atmospheres and their emergent spectra, our work encompasses a diverse array of subjects. We also study the billion-year evolution of our own galaxy, the Milky Way, by studying the billion of stars that we can now observe. Furthermore, we aim to understand the fate of the universe, including investigating whether the distribution of galaxies exhibits any parity violation. These complex and non-linear problems were previously difficult to address, but the advent of deep learning approaches has provided us with the tools to tackle these big questions and make strides on these topics.
Our research is grounded in the extensive amount of survey data available, and we are actively involved in analyzing data from various surveys across different domains. These include spectroscopy surveys such as SDSS-V, DESI, and 4MOST, astrometric data from Gaia, photometric surveys like Euclid, Roman, and CSST, and time-series data from LSST, TESS, and PLATO.
In addition to statistical inference and inverse problems, our group is at the forefront of developing Large Language Models specifically tailored for astronomy. As the principal investigator of the AstroMLab collaboration (formerly part of UniverseTBD), I work closely with the Oak Ridge National Laboratory and the Astrophysics Data System database to build ambitious large language models. By extensively pretraining and fine-tuning these models on the vast corpus of astronomical literature, we aim to gain insights into the mechanisms behind creativity and scientific breakthroughs. Moreover, we are working on a series of projects that will lead to end-to-end autonomous research, known as AI x Scientist, in astronomy. Our goal is to leverage AI to accelerate research in astronomy, enabling more efficient and effective exploration of the universe.
Galaxy Evolution
Stellar Astrophysics
Cosmology /Reionization
Planetary Systems
Large Language Models
Simulation-Based Inference