Bio
I'm a founding engineer building AI-first systems designed for production scale and reliability. My work spans distributed infrastructure, applied ML pipelines, and neuromorphic computing research, from training spiking neural networks to deploying them on custom hardware.
Shaped by real-world deployments driving $70K+ in client revenue, I focus on pragmatic execution, bridging the gap between research ideas and shipped products. My approach emphasizes minimal, battle-tested code over theoretical elegance.
Before joining CVision as a founding engineer, I researched deep learning for optical signal processing at the Optical Networks and Technologies Lab, where I worked on anomaly detection using GANs and transformers for sequence-level interpretability.
My current work at CVision involves building and deploying distributed systems for client-facing AI applications, owning the complete CI/CD and DevOps stack, and leading R&D initiatives across graph neural networks, knowledge graphs, and efficient neural architecture design.
On the research side, I've built a complete spiking neural network deployment stack, enabling efficient spike-based computation with significant memory optimizations and validation on event-based vision benchmarks.