Advancing understanding of complex systems through reverse-engineering approaches and interpretable model development
Developing robust methodologies for understanding behavior from observational data
Bridging theoretical frameworks with empirical observations across scientific domains
Advancing the field of Data Science in Industry and Academia using innovative techniques like Machine Learning.
Img Credit: Link
I am interested in using multimessenger astronomy to explain processes happening inside astrophysical objects like AGN. At WIPAC, I am using multi-wavelength data of AGN sources and performing a well detailed analysis to determine its connection with IceCube detected neutrinos.
AGN is an active supermassive black hole at the center of a galaxy which has highly relativistic particles coming out of its center in form of a jet. Using multimessenger astronomy described above and other techniques I want to learn everything about these amazing objects
Img Credit: Link
Developed and launched a simulation package (SNuGGY) using computational methods to solve complex problems, emphasizing reproducible and interpretable results.
Used simulation and data analytics to understand the mechanistic origins of neutrino detection patterns, developing interpretable models from observational data.
Simulating data for upcoming X-ray telescopes like AMEGO to test multimessenger implications
Studying correlation between VLBI Radio data and IceCube neturino signals from AGN.
Estimating the Extragalactic Background Light using GeV+TeV observations from AGN (Using Fermi-LAT and Cherenkov Telescopes)
Using the EBL to measure AGN redshifts
Modeling EBL diffuse background emission (collaborative work)
Modeling the Diffuse neutrino and Gamma-ray background from supernovae (collaborative work)
Fast Response Analysis with IceCube data (collaborative work)
Using the EBL to measure Hubble constant (collaborative work)