Research

The modern theory that describes the properties of elementary particles and their interactions is the Standard Model. However, there are reasons to believe that it is not the whole story; there is an underlying theory. For the last few decades, physicists have been trying to infer the underlying theory, generally called the physics beyond the Standard Model or simply new physics. The Large Hadron Collider at CERN has been searching for new physics models like supersymmetry, extra-dimension, etc. 

In the last few years, I have primarily worked on the properties of some hypothetical particles called Leptoquarks that appear in different new physics models. Leptoquarks are the carriers of a new type of interaction. They are mysterious particles with both baryon and lepton numbers. As a result, they can interact with quarks (and gluons) and leptons (like the electron, the muon, neutrinos, etc.). If Leptoquarks exist, they can solve various puzzles (anomalies, like the muon anomalous magnetic moment or the ones seen in some decays of the B-meson) that have been hanging around for some time.

Apart from Leptoquarks, I have also worked with other types of hypothetical particles called vector-like quarks appearing in the Randall–Sundrum Model (a type of extra-dimension model). 

Modern particle detectors like those at the Large Hadron Collider record enormous amounts of data, most of which looks like ordinary Standard Model processes. Finding a rare signal (say, the decay of a hypothetical heavy particle) hidden inside this data is often like finding a needle in a haystack. Over the last several years, a significant part of my research has focused on using Machine Learning to sharpen this search.

I work on developing and applying deep learning techniques to identify and classify complex particle decay signatures that are otherwise very hard to separate from background processes using traditional cut-based methods. This includes tagging the decay products of heavy vectorlike quarks, identifying boosted dark photons, and improving discovery prospects for various new-physics signals at the LHC. We have also worked on more foundational questions, such as designing better loss functions to directly optimise for discovery significance, building interpretable models using techniques like Integrated Gradients, and developing HEP-JEPA, a foundation model for collider physics based on joint embedding predictive architectures.

Alongside particle physics, I am also interested in the foundations and applications of Quantum Information Theory. A central theme in my work here is quantum entanglement and non-Markovianity — how quantum systems lose (and sometimes regain) their "quantumness" as they interact with their environment. I have worked on detecting genuine multipartite entanglement in multi-qubit systems, characterising non-Markovian dynamics in models like the spin-boson model, and studying how information flows in setups like the quantum switch.

I am also interested in quantum algorithms. For example, I have worked on early fault-tolerant quantum algorithms for simulating open quantum systems, developed in collaboration with Dr. Shantanav Chakraborty.