PhD Researcher in AI and Computer Science. I develop battery-less, ultra-low-power TinyML for secure visual perception on microcontrollers. My work bridges embedded AI, energy-harvesting systems, and hardware-aware deployment to deliver deployable on-device ML under extreme power and memory constraints.
I design and deploy battery-less, ultra-low-power TinyML systems for secure visual perception directly on microcontrollers. My research focuses on translating cutting-edge machine learning into practical, deployable solutions for constrained devices, combining embedded AI, energy-harvesting systems, and hardware-aware deployment.
Currently pursuing a PhD in Artificial Intelligence and Computer Science at Aston University, I specialize in secure on-device inference, hardware-aware machine learning, and system-level optimization for microcontroller-based IoT devices.
I am actively exploring collaborations on research and applied projects in embedded intelligence, secure TinyML, and energy-efficient AI deployment, contributing to innovations at the intersection of ML, systems, and hardware.
This research develops an ultra-low-power, battery-free newborn monitoring system for under-resourced hospitals. Using on-device artificial intelligence and energy harvesting, it continuously detects signs of distress while protecting patient privacy. The technology aims to support overstretched nurses, enable earlier intervention, and reduce preventable newborn deaths worldwide.