About

My research journey is driven by a fundamental question: how can principles of biological intelligence be translated into artificial systems that can learn, adapt, and interact with the physical world? 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 artificial intelligence equip me to approach these challenges from both theoretical and applied perspectives.

Research Vision

My research interests focus on computational neuroscience and neuromorphic computing, with an emphasis on brain-inspired models for efficient intelligence. I am interested in understanding how biological principles such as spike-based communication, synaptic plasticity, and neural dynamics can be translated into spiking neural networks and low-power learning systems. I am particularly interested in the intersection of spiking neural networks, neural dynamics, plasticity, and hardware-aware learning, with embodied and robotic intelligence as a longer-term application. That is the question I want to spend my PhD chasing, and my career answering.

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