Research Journey

My research journey has been shaped by a persistent question:
how can abstract computational theories be translated into systems that solve real-world, large-scale problems?

This question did not emerge suddenly during my PhD. It is the natural culmination of a series of projects across physics, computation, biology, and machine learning, each contributing a layer of perspective that now defines my work in quantum optimisation.

Early Foundations: Computation Through Physical Systems

My first exposure to computation as a physical process came during my time at IISER Pune, where I explored stochastic systems and statistical physics.

Working on space-dependent diffusion in Brownian motion, I simulated molecular dynamics systems to understand how stochastic integration frameworks like Ito and Stratonovich influence macroscopic behavior. This was more than a physics exercise, it introduced me to the idea that the choice of mathematical formalism directly impacts computational outcomes.

In parallel, my work on stochastic processes in financial systems involved simulating Ornstein–Uhlenbeck processes and validating theoretical models in option pricing. These projects collectively trained me to think rigorously about dynamical systems, numerical simulation, and the translation of theory into computational pipelines.

Game Theory and the Shift Toward Strategic Computation

This foundation naturally extended into game theory, where I began exploring how agents interact under constraints.

At IIT Bombay, I worked on quantum game theory, demonstrating how entanglement-enabled strategies could outperform classical Nash equilibria in team decision problems. This was my first concrete encounter with quantum advantage in structured optimisation settings.

I later expanded this perspective through mean-field game theory, where I generalized quantum mechanical formulations to large-scale agent-based systems. These experiences were pivotal—they reframed computation not just as simulation, but as optimisation under competing objectives, a theme that now sits at the core of my PhD.

Biology, Systems Thinking, and Real-World Constraints

A major turning point in my journey was my involvement in iGEM (International Genetically Engineered Machine): Our team developed a negative carbon footprint fuel generation method using synthetic bacterial co-cultures to convert CO₂ into biofuel.
We won a Gold Medal with nominations for Best Manufacturing and Education. Te following year, I mentored a team to Gold Medal again.

Project links:

This experience fundamentally changed how I approached research. Biology introduced: high-dimensional systems, real data constraints and the necessity of scalable computational methods. It was here that I began to see a clear gap: classical methods often struggle to scale efficiently for complex biological optimisation problems.

Formalising Machine Learning and Causality

To address these challenges, I moved deeper into machine learning and causal inference. During my Master’s thesis at TCS Research, I worked on quantum-enhanced reinforcement learning integrating causal inference into deep RL systems

This work sharpened two key insights:

Learning systems must be robust to distributional shifts
Incorporating causal structure improves both interpretability and performance

In parallel, my work on carbon premium in financial markets applied causal inference at scale using econometric techniques.

These projects solidified my ability to handle real-world datasets, design statistically rigorous models and connect theory with empirical validation

The Transition to Quantum Optimisation

By the end of my undergraduate and Master’s work, a clear convergence had emerged:

optimisation problems in biology are inherently combinatorial and NP-hard
classical approaches face scalability bottlenecks
quantum computing offers a natural framework via QUBO formulations

This led me directly into my PhD at University College Dublin.

Leadership and Community Building

Alongside research, I have taken on leadership roles that shape the research environment:

PhD School Representative (UCD School of Computer Science)
Founded the Dry Run Club to improve research communication
Mentored iGEM teams to international success

These roles reflect an important aspect of my approach:
research is not just individual output—it is a collaborative ecosystem.