I turn research prototypes into dependable engineering workflows for real technical data.
Industrial AI / Agent Systems / Case-Based Reasoning
Problem: Standard document retrieval surfaces passages without preserving the reasoning process required for technical tasks.
Contribution: Co-engineered procedure retrieval, objective evaluation, and fallback logic around the LLM, reducing unnecessary LLM usage through selective context and caching.
Industrial AI / Time-Series Machine Learning / Model Validation
Problem: CAE simulation assessment requires evaluating complex multichannel time-series signals alongside tabular test parameters.
Contribution: Learned expert assessment patterns from multichannel simulation signals and tabular context using modular time-series models and bootstrapped confidence intervals.
Industrial AI / Graph Neural Networks / Scientific ML
Problem: High-dimensional crash simulation requires rapid approximate predictions for mesh deformation and time-dependent acceleration.
Contribution: Developed and evaluated GNN surrogate workflows for selected crash-simulation outputs using diagnostic evaluation tools.
Industrial AI / MLOps
Problem: Crash-test sensor channel validation requires a deployable inference path combining signal imagery and tabular features.
Contribution: Built containerized AWS Lambda signal processing and classification workflows for crash-test sensor time-series data with Pydantic event validation.
Retrieval, structured outputs, objective evaluation, observability, fallback behavior, and cost-aware orchestration.
Time-series signals, simulation data, anomaly detection, domain-aware evaluation, and uncertainty reporting.
CAE surrogates, geometry processing, diagnostic interfaces, reproducible experiments, and model integration.
My background in mechatronics and computational engineering led me from simulation and material modeling to building production AI systems.
Today I work across LLM workflows, industrial time-series data, simulation, and model evaluation, with a focus on systems that are reproducible, inspectable, and useful.