International Centre for Neuromorphic Systems
Postdoctoral Research Fellow/Research Fellow in Neuromorphic Hardware
My research revolves around the development and application of Spiking Neural Networks (SNNs) within the realm of high-performance computing systems. Inspired by the remarkable computational efficiency of the human brain, I focus on harnessing the unique capabilities of SNNs to solve complex problems and advance various fields of study.
One primary area of my research involves the design and implementation of self-learning SNNs. I am dedicated to creating innovative training algorithms and neural architectures that mimic the brain's ability to learn from experience and adapt to changing environments.
In the realm of Artificial Intelligence (AI), my work is particularly centred on exploring the potential of Spiking Neural Networks. I specialize in leveraging the inherent parallelism and event-driven nature of SNNs to enhance cybersecurity. This includes developing intrusion detection systems and addressing side-channel attacks, ultimately fortifying the security and resilience of digital systems.
Additionally, my research extends to the realm of neuromorphic vision sensors, which draw inspiration from biological vision systems. I am actively involved in developing embedded systems that leverage neuromorphic vision sensors for real-time object recognition, tracking, and other applications.
Beyond this, my research encompasses digital signal processing and image processing. I continuously explore innovative techniques and algorithms to improve the processing and analysis of digital signals and images, with potential applications spanning from medical imaging to autonomous robotics.
- Bachelors in electronic engineering
- Masters in electronic engineering
- PhD in computer science
A full listing of my publications can be found on my Google Scholar page here(opens in a new window)