Decision systems & predictive modeling for complex engineering and manufacturing problems

Overview

I build predictive models and decision systems for problems where outcomes depend on complex interactions between process, physical or biological systems, and uncertainty.

My work has been used in national defense programs, manufacturing environments, biological and medical settings, and operational business contexts — including systems for process modeling, product selection, anomaly detection, and decision support.

A typical engagement starts with messy, incomplete, or poorly structured data and a real operational question — not a clean modeling problem. I work with teams to structure the problem, build models that reflect how the system actually behaves, and connect results to how decisions are made in practice.

Types of work

• Targeted modeling and analysis — focused efforts to answer a specific question reliably, including process prediction, classification, sensitivity analysis, and uncertainty quantification.

• Decision tools and digital twins — tools that allow teams to explore scenarios, compare options, and make consistent decisions across product, process, or experimental settings.

• End-to-end systems — pipelines from raw data through modeling and into operational use, including preparation, development, validation, and deployment.

• Problem definition and framing — when the problem is not well specified, I work with teams to define the right questions, identify key variables, and structure the problem so it can be modeled and acted on.

Approach

The requirement is simple: the work has to hold up outside of a notebook.

Most modeling efforts fail because they stop at accuracy. Accuracy isn’t enough — the model has to be robust, explainable, and safe to base decisions on.

Engagement

I work directly with business, engineering, scientific, and technical leadership. Engagements range from focused efforts to longer-term collaborations where systems are built and refined.