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Kumar Muthuraman - The University of Texas at Austin, McCombs School of Business. Austin, TX, US

Kumar Muthuraman Kumar Muthuraman

Professor, Department of Information, Risk and Operations Management and Director of the Center for Research and Analytics | The University of Texas at Austin, McCombs School of Business


Creating computational models with immediate, real-world applicability


Areas of Expertise (6)

Asset Pricing Derivatives Operations Research Energy Financial Risk Management Healthcare Scheduling and Profitability


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.



Kumar Muthuraman Publication



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Education (3)

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 (4)

The Academy of Distinguished Teachers Announces New Additions

Daily Texan  online


Professor Kumar Muthuraman was inducted into the Academy of Distinguished Teachers.

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$16 Billion Veterans Affairs Bill Still Leaves Patients Waiting

Texas Enterprise | Big Ideas in Business  online


House and Senate leaders rushed to pass a bill last week that allocates $16.3 billion to overhaul the Department of Veterans Affairs’ troubled healthcare system... “We need to focus on increasing efficiency at least as much as adding resources,” insists Kumar Muthuraman, associate professor at the McCombs School of Business, who researches and develops healthcare scheduling models. “It’s a no-brainer: If you add new staff without ensuring that existing resources are optimally utilized, you could have doctors staring at the ceilings some days while patients continue to wait weeks or months for appointments.”

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What Can You Do With the 7th-Fastest Computer in the World?

Texas Enterprise | Big Ideas In Business  online


IROM Associate Professor Kumar Muthuraman and doctoral student Ester Wang have also been using TACC’s resources for healthcare research. They want to improve how patients are scheduled for appointments so that clinics are more profitable and patients spend less time in the waiting room. To do this, Muthuraman and Wang use a patient’s no-show probability along with a clinic’s intake history to predict which kinds of patients will most likely need to be seen during a given timeframe.

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Will You See the Doctor Now?

Texas Enterprise | Big Ideas in Business  online


Clinics that experience high patient no-show rates risk physician idleness and profit loss. Overbooking can mitigate this, but traditional scheduling techniques often result in increased patient wait time and overtime expenses for staff. Two landmark studies use patient no-show probability to create a scheduling model that minimizes wait time while maximizing profit.

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Articles (4)

Replenishment Policies for Multi-Product Stochastic Inventory Systems with Correlated Demand and Joint-Replenishment Costs Production and Operations Management


This study analyzes optimal replenishment policies that minimize expected discounted cost of multi-product stochastic inventory systems.

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Boundary Evolution Equations for American Options Mathematical Finance


We consider the problem of finding optimal exercise policies for American options, both under constant and stochastic volatility settings.

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Modeling and Forecasting Mortality Rates Insurance: Mathematics and Economics


We show that by modeling the time series of mortality rate changes rather than mortality rate levels we can better model human mortality.

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A Stochastic Overbooking Model for Outpatient Clinical Scheduling with No-Shows IIE Transactions


In this paper a stochastic overbooking model is formulated and an appointment scheduling policy is developed for outpatient clinics.

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