At DRDO-CAIR, I contributed to neuromorphic computing research spanning software and hardware. I built algorithm-level components in Python and PyTorch and analyzed throughput, latency, and memory trade-offs in real-time, resource-constrained settings.
For my Neuromorphic Computing Research Study, I built and ran seven experiments on spiking neural networks and neuromorphic hardware. A natively trained convolutional SNN reached 98.06% accuracy, the best result in the study.
I also built a crossbar-array circuit simulator and re-implemented the CNN/SNN inference pipeline in JavaScript as an interactive GitHub Pages demo, unit-testing each layer against a NumPy reference.
My other projects include a real-time football player detection and re-identification pipeline, a personalized diet recommendation system using rules and machine learning, and CNN-based X-ray image classification. I’m studying Computer Science and Engineering at PES University and was a national finalist in the Canara Bank SuRaksha Cyber Hackathon 2025.
