About
I am a Computational Scientist at Argonne National Laboratory, Chicago working at the intersection of artificial intelligence and scientific research. My work focuses on developing innovative computational methods for scientific analyses, particularly in the fields of cosmology and astrophysics.
Current Position
At the Computational Science division at Argonne, I develop and apply machine learning techniques to address complex scientific challenges. My work involves:
- Developing uncertainty quantification methods for scientific models
- Designing emulators for computationally expensive simulations
- Implementing multi-modal foundation models on heterogeneous scientific datasets
- Developing agent-led workflows for scientific problem solving
- Implementing deep learning pipelines for cluster cosmology and strong lensing analysis
- Creating realistic synthetic extragalactic catalogs from exascale simulations
Research Interests
- Machine Learning for Science: Developing AI methods that incorporate physical constraints and domain knowledge
- Cosmological Structure Formation: Understanding the formation and evolution of the cosmic web
- Uncertainty Quantification: Creating probabilistic models that provide reliable uncertainty estimates
- Dark Matter Physics: Investigating the properties and distribution of dark matter in the universe
Background
- NowComputational Scientist, Computational Science division, Argonne National Laboratory
- PostdocPostdoctoral Fellow, High Energy Physics division, Argonne · KICP Associate Fellow, University of Chicago
- PhDPhysics & Astronomy, University of Kansas, with Sergei Shandarin
- M.Sc.Integrated Masters in Physics, BITS Pilani · thesis at the Indian Institute of Astrophysics