I am a postdoctoral research associate at Fermilab. My research interest is understanding the Universe through big astronomy survey data and simulations. I am interested in applying novel Machine Learning algorithms for cosmology parameter inference. I also focus on using nuisance invariant ML algorithms for finding strong gravitational lenses from big data.I got my Ph.D. in Astronomy from University of Illinois Urbana Champaign where I studied galaxies from the Early Universe. One of my very interesting analyses on water detection in early galaxy is featured in media [https://beta.nsf.gov/news/scientists-find-water-billions-light-years-away].I worked as a Data Science Intern at 0ptimus Analytics, where I was involved in modeling and predicting 2020 USA election results using Machine Learning algorithms. Our methodology is published in Harvard Data Science Review [https://hdsr.mitpress.mit.edu/pub/gach7e59/release/1/].Competencies: Research , Machine Learning, Python, TensorFlow
Listed skills include Teaching, Python, Latex, Data Analysis, and 5 others.