Selected work
Outcome-focused work across industrial assets, rail systems, and infrastructure.
2025 - Present
Physics-informed signal processing and ML pipelines for machinery health, combining sensing, feature extraction, and anomaly detection into structured diagnostic reports. Consolidated fragmented, multi-signal analysis into a single reproducible workflow that turns raw sensor streams into decisions an engineer can act on.
2024 - Present
Analytical pipelines linking continuous measurement signals to degradation behavior, applied to rotating machinery and rail wheel wear. Surfaced driver- and operation-related factors influencing wear that raw electrical signals alone missed, feeding maintenance and control decisions.
2023 - Present
Python and Azure analytics workflows over high-frequency operational engineering data, delivering diagnostics, KPI tracking, and dashboards that engineers rely on for maintenance decisions across multiple train units.
2025 - Present
A human-in-the-loop agent (Claude + Model Context Protocol) that orchestrates tool-use and retrieval over operational sensor data, while deterministic Python handles the inference and a human reviews every flagged case. Consolidated multi-signal condition-monitoring analysis into a single agent-driven workflow.
2022 - 2023
IoT-based monitoring and analytics workflows integrating sensor systems, cloud pipelines, and reporting tools for industrial process improvement and engineering decision support.
2018 - 2019
Drone-based inspection workflows and deep-learning visual defect detection methods for infrastructure condition assessment and structural health monitoring.
Available for consulting and project-based work in predictive maintenance, signal analytics, operational AI, and model validation.
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