The reliance on autonomous technologies in our future systems, will only continue to increase. There is potential for significant benefits from increased autonomy in ‘driverless’ passenger cars, trucks, trains, and commercial aircraft. It is assumed that the most significant benefit is the economic efficiency that can be created because these systems can be operated with fewer human resources. Additionally, increased autonomy has the potential to improve safety for travelers, employees, and communities. While the expected increase in economic efficiency is clearly a benefit, more research is needed to understand the impacts to economics, workload, and safety, and the trade-offs among these factors. My research utilizes information based models to design, monitor, and maintain sociotechnical systems that policy makers can trust and continually improve.
Engineered systems are becoming increasingly complex to satisfy the growing needs of our society such as colonizing space or allowing for the survival of a company in the competitive global market. To achieve these goals, systems engineers make high consequence decisions that are concerned with balancing interrelated system characteristics given limited amount of resources and uncontrollable contextual conditions. The essence of systems engineering lies in making rational decisions that are consistent with the preferences of the system’s stakeholders. My research investigates decision making under uncertainty in system design and the formulation of a theoretical foundation for the discipline of systems engineering.
Measurement of Productivity and Efficiency
We design and operate transformative processes to satisfy various needs, such as building factories for producing certain goods or starting companies to deliver services. Fundamentally, all transformative processes convert a set of resources into a set of outputs given uncontrollable environmental and contextual conditions. My research focuses on the micro-economic production theory that describes the set of rules that govern the production performance of manmade transformative processes. I use robust multivariate statistical techniques, machine learning, and data analytics to analyze the productive efficiency of systems to inform rational managerial decisions.