Physics-Informed ML and Process Control
Model design and implementation that encodes process physics — reducing data requirements, improving generalization, and producing results that make sense to domain experts and ML practitioners alike.
Machine Vision and Perception Systems
From classical vision pipelines to modern deep learning architectures, applied to inspection, guidance, metrology, and robotic perception in manufacturing environments.
Sensor Fusion and Anomaly Detection
Multimodal sensing pipelines integrating acoustic emission, thermography, force/torque, and vision for process monitoring and quality control.
Sim-to-Real and Real-to-Real Pipeline Development
Digital twin and simulation environments that generate training data for real-world deployment, including Isaac Sim integration for robotic manufacturing applications. Real-to-real implementations that use hardware in the loop to inform places where the physics is unknown but a working system can be built.
Systems Integration and Architecture Review
Independent assessment of existing implementations, gap analysis, and strategic guidance for organizations building or scaling manufacturing intelligence systems.
Defense and Aerospace Applications
Sensor-based monitoring, anomaly detection, and process control for high-consequence manufacturing environments requiring rigorous validation.
