Graduate research and applied engineering investigations
Developing machine learning pipelines to automatically detect, classify, and localize welding defects from imaging data. Research targets the replacement of manual inspection bottlenecks in manufacturing environments with real-time, AI-driven quality assurance systems applicable to power generation fabrication and heavy equipment manufacturing.
Training deep learning models on weld imagery datasets to identify porosity, cracking, incomplete fusion, undercut, and spattering defects. Pipeline integrates computer vision preprocessing, feature extraction, and model inference with structured defect reporting outputs suitable for integration into existing quality management workflows.
ML-based weld inspection directly addresses manufacturing quality challenges in power generation, heavy equipment, and industrial fabrication. Automated defect detection reduces human error, enables real-time non-conformance tracking, generates traceable quality records, and scales to high-volume production environments where manual inspection creates throughput bottlenecks.
Applied engineering research in isotope extraction within high-temperature molten salt environments. Contributed to nuclear testing infrastructure through vessel design, failure analysis, and quality documentation — building foundational skills in manufacturing process evaluation under extreme operating conditions.