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My research focuses on the design and analysis of decision-making models under
uncertainty with limited information. It is grounded in
applications where stochasticity is often difficult to characterize using
traditional probabilistic techniques because of fast-changing environments
or of limited data. My overarching goal is to incorporate uncertainty in an
intuitive, tractable manner, which (i) is well-suited to the information
available, i.e., does not require managers to make restrictive assumptions
on the underlying probability distributions, (ii) leads to tractable
formulations that can be solved efficiently for the large data sets
encountered in practice, and (iii) yields theoretical insights into the
structure of the optimal solution, to foster a better understanding of the
computer-generated strategy. These features play an important role in
practitioners adopting quantitative approaches. To achieve this research’s
goals, I use tools from robust
optimization as well as data-driven
optimization.
My current application area of choice is health care
financing under uncertainty. The rest of these pages contain information
about previous application areas. I am looking forward to sharing my new
results online later this year.
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