About

Developing memory-efficient neural architectures and zero-shot adaptation techniques for resource-constrained environments. Leading research on systems that balance high accuracy with extreme frugality.

My research focuses on memory-efficient neural architectures, fast adaptation techniques, and semantic understanding mechanisms that allow AI systems to operate effectively under real-world constraints — emphasizing functional intelligence over brute-force scaling.

During this exploratory phase, I immersed myself in multiple domains — Network Security, Blockchain, and Machine Learning — searching for an area where mathematical depth met real-world impact.

Machine Learning stood apart. Studying backpropagation and gradient descent introduced me to a new way of thinking about software: systems that do not just execute predefined logic, but improve through experience. The shift from programmed behavior to learned behavior marked a clear change in how I understood intelligence in computation.

I came to see ML not as just another technical field, but as a foundational layer for modern technology — one capable of reshaping how systems perceive, adapt, and operate. That realization set the direction for my doctoral journey and my focus on building adaptable, real-world intelligent systems.

After a few years in a stable software development role, I made the deliberate decision to step away from industry. I had come to a clear realization: I didn’t want to only implement existing tools — I wanted to understand and improve the fundamental systems behind them.

Without a fixed destination, I treated this period as a focused phase of technical exploration — a time to step back, reassess, and search for problems that were foundational rather than incremental.

During this period, my role expanded from application development into infrastructure and deployment as the organization modernized its stack. Alongside full-stack work, I managed Azure cloud environments and implemented infrastructure-as-code for scalable, repeatable deployments, gaining hands-on experience with reliability and scale in production systems.

Focused on designing and maintaining responsive, user-facing applications and the backend systems that supported them. Worked across the full stack using modern web technologies and Java-based services, contributing to reliable data workflows and business-critical functionality. This phase strengthened my understanding of how large software systems are structured, integrated, and delivered in real production environments.

This phase expanded my foundation from electronics and embedded systems into broader computing concepts. Through coursework and projects, I strengthened my understanding of software systems, data structures, internet and core computer science principles. While I had not yet defined a long-term research direction, this period widened my technical perspective and prepared me to engage with more complex computational problems in the years that followed.

This phase marked my transition from studying equations and theoretical principles to understanding how real-world systems are built. Through embedded systems and signal processing, I learned how sensing, computation, and actuation come together to create functional machines. Working directly with sensors and hardware–software interfaces gave me hands-on experience in selecting the right components, designing system-level interactions, and writing code that responds to real-world signals.

Built a strong analytical foundation through intensive study of mathematics, physics, and chemistry — disciplines that shaped my early interest in problem-solving, systems thinking, and the underlying rules that govern how things work.