I design and deploy production-grade computer vision systems for fixed industrial environments, converting raw detections into reliable process decisions using spatial and temporal logic. My work spans data curation, model training, post-processing logic, and real-time inference under strict operational constraints.
My work focuses on bridging the gap between object detection accuracy and real-world reliability. In
industrial computer vision, high mAP alone is insufficient,systems must reason about position, sequence, and
time.
I specialize in fixed-camera vision systems where spatial validation, temporal smoothing, and rule-based state
tracking are used to transform frame-level detections into dependable operational signals. I take ownership of
the full pipeline, from dataset curation and training to deployment, monitoring, and known failure analysis.
• Fixed camera placement and constant field-of-view
• Offline-only inference (no cloud access)
• Latency-sensitive pipeline under continuous operation
• No model retraining allowed post-deployment