I began my research journey as an MS student, working on the electromagnetic form factors of scalar and pseudoscalar mesons. The calculation of hadronic electromagnetic form factors enhances our understanding of their internal structures. During my PhD, I investigated the structure of the nucleon within the light cone framework, which reveals a more complex nucleon structure. I analyzed the impact of intrinsic charm and bottom distribution functions on the production of photons and Z bosons, as well as on the cross sections for charm and bottom production. Subsequently, I focused on the nuclear modifications of Parton Distribution Functions by studying isolated prompt photon production at the Large Hadron Collider (LHC).

With the commencement of my Post-Doctoral Research Fellowship, I studied doubly heavy baryons, placing particular emphasis on their strong and radiative decays. The existence of heavy quarks in baryons has been predicted by the quark model, and many single heavy baryons have been experimentally observed in recent years. However, experimental investigations of doubly heavy baryons are still ongoing. The analysis of doubly heavy baryons is intriguing because it contributes to our understanding of the structure of all baryons and the hadronic spectroscopy of various systems. Their decay processes can also provide insights into the dynamics associated with transitions involving two heavy quarks.
Recently, I have ventured into the promising field of machine learning, aiming to make significant advancements in this domain. Machine learning provides innovative tools that enable physicists to extract valuable information from large datasets, and these powerful techniques will be particularly beneficial for analyzing data from the LHC.

HTTPS://orcid.org/0000-0001-7082-6279

https://inspirehep.net/authors/1279410