Industrial Research Interests

I oversee fundamental research and core software development in projects centered around artificial neural networks, computer vision, augmented reality, and virtual reality. My work spans across diverse areas such as Computer Vision with CNN, Style Transfer, Generative AI, Augmented Reality using AR Core, and mobile application development via ChatGPT API etc...

Some of the completed work has been presented as outputs at conferences.

Academic Research Interests

My research interests are focused on understanding the properties and dynamics of hadrons using the ab-initio method of Lattice Quantum Chromodynamics and extending these studies to the weak interaction processes.

My research focus starts from the properties of hadrons, extends through nuclear physics, and creates a bridge between high-energy physics and nuclear physics. To this end, the Lattice gauge theory is the primary theoretical tool to explore the properties of hadrons and other QCD observables. Developing the Lattice software and extending the relevant theoretical methods to study the hadron properties constitutes the main framework of my research.

I am working on electromagnetic transitions of baryons containing charm quarks using Lattice Quantum Chromo Dynamics.

In these studies, we predict the mass, electric and magnetic form factors, and decay widths of particles using theoretical and numerical methods.

These predictions help experimental physicists to observe the particles that have not yet been observed.

I have recently been working on semi-leptonic transitions of charmed baryons. Studying the semileptonic decays of charmed particles is prominent in testing the standard model of particle physics. One can gain complementary information about Cabibbo–Kobayashi–Maskawa (CKM) matrix elements and CP violations; moreover, semileptonic charm baryon decays can be used to test the lepton flavor universality. Any deviation from the Standard Model predictions might indicate a hint for the physics beyond the standard model.

I am also interested in artificial neural networks, which is a recent method and direction in science to make predictions based on the known properties of nuclei.

To this end, we initiated a neural networks collaboration to study nuclear properties with the aim of the improvement of the predictions for nuclear properties compared to the experimental data as well as finding new, fast, and reliable methods to predict nuclear properties.

Academic Research Interest

Publications

Neural Networks & Machine Learning Publications:

Publications in Lattice QCD (link to inspirehep)

CMS Experiment (Experimental High Energy Physics) (link to inspirehep)