Areas of Expertise (6)
Kumar Muthuraman is a professor with expertise in modeling and forecasting. In addition, he serves as the director for the Center of Research and Analytics. As the recipient of a prestigious National Science Foundation grant, he's worked closely with hospitals and clinics to create innovative scheduling models that dramatically reduce patient wait times while improving clinic profitability. His work is currently being tested in more than 300 clinics as well as a large university hospital.
His other research areas include asset pricing, derivatives, and operations. Before joining the faculty at McCombs, Muthuraman was an assistant professor at Purdue University and a graduate research assistant at Stanford.
Stanford University: Ph.D., Scientific Computing and Computational Mathematics 2003
Stanford University: M.S., Scientific Computing and Computational Mathematics 2000
Central Electrochemical Research Institute: B.Tech., Chemical and Electro-Chemical Technology 1998
Media Appearances (1)
The Academy of Distinguished Teachers Announces New Additions
Daily Texan online
Professor Kumar Muthuraman was inducted into the Academy of Distinguished Teachers.
In this study, we develop a coordinated pre‐operative scheduling approach between Anesthesiology and Internal Medicine to optimize patients’ medical conditions prior to surgery.
The problem of regulating natural gas procurement has become a huge burden to regulators, especially due to the plethora of complicated financial contracts that are now being used by local distribution companies (LDCs) for risk management purposes. ...We demonstrate in this paper that when modeling errors are present, the policy benchmarks proposed earlier can backfire and are hence, as suspected, not well suited for regulation.
This study analyzes optimal replenishment policies that minimize expected discounted cost of multi-product stochastic inventory systems.
We consider the problem of finding optimal exercise policies for American options, both under constant and stochastic volatility settings.
We show that by modeling the time series of mortality rate changes rather than mortality rate levels we can better model human mortality.
In this paper a stochastic overbooking model is formulated and an appointment scheduling policy is developed for outpatient clinics.