Jafar Shamsi

We build brain-inspired computing systems that run on ultra-low power, addressing the energy-intensive nature of modern AI. Our research spans the entire neuromorphic computing stack to bridge the gap between neuroscience and engineering:

• Models: Developing brain-inspired models and algorithms that mimic how biological neural networks learn and perform tasks.
• Hardware: Designing custom, energy-efficient microchips to run these models seamlessly.
• Applications: Deploying these integrated systems into the real world, including biomedical wearables, assistive technologies, and edge AI devices.

For more details, please visit the Neo Computing Lab website.

Keywords: Neuromorphic Computing, Spiking Neural Networks (SNNs), Hardware-Software Co-design, Continual Learning, Digital Neuromorphic Hardware, FPGA-based Prototyping, Event-Driven Computation, Energy-Efficient AI, Biomedical Signal Processing (EMG/EEG), Wearable Assistive Technologies, Edge AI, Low-Power Real-Time Processing.