CAREER: A Data-driven Robust Approach for Large Scale Dynamic Optimization

The research objective of this Faculty Early Career Development (CAREER) Program award is to develop data-driven tractable and robust approach for large-scale dynamic optimization under uncertainty. This is a fundamental problem especially in today's world of BigData where we want to efficiently use vast amount of available data for decision-making. Due to its wide applicability, any progress in this field will have significant impact in practice. This project will address two fundamental problems in dynamic optimization. The first is related to modeling the underlying uncertainties in the optimization problem from data. Traditionally, probability theory has been used to model these uncertainties but it is often computationally intractable especially in high dimensions suffering from the curse of dimensionality. This research will develop new data-driven paradigms to model uncertainty that simultaneously ensure tractability and good performance. The second problem concerns developing efficient and robust algorithms for resulting multi-period optimization problems. The goal is to pursue the development and analysis of robust, practical and easy to implement algorithms through simple functional policy approximations with potentially strong theoretical performance guarantees.

If successful, the results of this research will develop foundational theory for tractable dynamic optimization and provide fundamental and practical new tools for a broad spectrum of real world problems. These results will be used to develop practical solutions for optimization problems arising in electricity markets and smart grid applications, which is an important area where dynamic optimization is very applicable. The graduate and undergraduate students will benefit from involvement in research and integration of the results into classroom instruction. The research will be broadly disseminated through outreach programs to local high schools with a goal of increasing the participation of students especially from underrepresented minorities in STEM education and research.