My research journey is driven by a fundamental question:  how can we bridge the gap between biological intelligence and artificial systems? The brain, with its remarkable efficiency and adaptability, offers a blueprint for the next generation of computing architectures.

I am particularly fascinated by the computational principles underlying neural information processing. Through my work in spiking neuron simulations and biophysical modeling, I aim to understand not just what neurons do but also how they do it and how these mechanisms can be translated into efficient neuromorphic and brain-inspired computational systems, with relevance to large-scale neural modeling and adaptive intelligence.

My interdisciplinary background in mathematics and ongoing studies in AI equip me to approach these challenges from both theoretical and applied perspectives.