I am an Artificial Intelligence Researcher, currently working as an Associate Professor of Computer Science and Engineering at Adamas University, Kolkata. My academic and research journey spans over two decades across India and Europe, with expertise in artificial intelligence, soft computing, and data-driven optimization algorithms (NSO, PSO, GWO). I have spent many years abroad and have held several research positions at the Norwegian University of Science and Technology (NTNU), Norway, contributing to pioneering projects in cyber-physical systems, Computer Vision & Digital Twin Systems, and industrial AI.

My core interests lie in nature-inspired algorithms, surrogate-assisted optimization, and intelligent manufacturing systems. I have published extensively in high-impact journals and conferences, and I actively supervise PhD scholars in cutting-edge domains such as prompt engineering and reinforcement learning.

I have also served as a NAAC coordinator and played key academic roles in curriculum development, international collaborations, and quality assurance. As a recognized reviewer and editorial contributor, I continue to support the scholarly community.

My research has been backed by international grants, and I have led proposals in digital transformation for industries ranging from automotive to food processing. I am committed to bridging academia and industry through innovation, digital intelligence, and sustainable technology.

My recent research sits at the intersection of deep learning architecture and high-performance computing, heavily focused on advancing AI architectures and foundation models. My primary undertaking is pre-training a lightweight Edge-enabled model for offline applications, optimizing training on lowcost GPU/CPU. Concurrently, I rigorously benchmark alternative recursive structures such as HybridRecursiveNet and Liquid Neural Networks (LNNs), analyzing loss convergence, weight sharing, and VRAM iteration speeds across massive parameter scales. It further directs my findings toward rigorous ablation analyses.