Arka Roy is an Assistant Professor in the Management Science and Statistics department at UTSA. Previous to UTSA, Dr. Roy was a faculty member in the Department of Applied Statistics and Operations Research at Bowling Green State University. In addition to his doctoral work at Purdue, he conducted research as a Visiting Pre-Doctoral Fellow in the Department of Industrial Engineering and Management Sciences at Northwestern University.
His research focuses on developing decision-making models in the presence of uncertainties. Application areas include cancer radiotherapy and marketing. Previous and current projects in the context of radiotherapy include developing robust measures of correlation in radiotherapy plan evaluation; modeling robust radiotherapy treatments; establishing large-scale radiotherapy plan analytics and quality control; and optimal scheduling of medical diagnostics. Ongoing work in marketing include estimating customer worth under competing risks and optimal resource allocation for marketing campaigns.
Dr. Roy has won various teaching and research awards including the 2018 Best Paper Award in the IISE Transactions on Healthcare Systems Engineering journal managed by the Institute of Industrial Engineers. In addition to his teaching and research endeavors, Dr. Roy is an active member of the Institute for Operations Research and the Management Sciences (INFORMS) and the American Association of Physicists in Medicine (AAPM).
Experience & Education
2018 - now: Assistant Professor, Management Science & Statistics, University of Texas at San Antonio.
2015 - 2018: Assistant Professor, Applied Statistics & Operations Research, Bowling Green State University.
2013 - 2015: Visiting Pre-Doctoral Fellow, Industrial Engineering & Management Sciences, Northwestern University.
2010 - 2015: Ph.D. Industrial Engineering, School of Industrial Engineering, Purdue University.
2006 - 2009: Bachelor of Science in Industrial Engineering, School of Industrial Engineering, Purdue University.
Robust Optimization, Time-Dependent Uncertainties, Data Analytics, Radiation Therapy
Estimating Customer Churn under Competing Risks
Pallav Routh, Arkajyoti Roy, and Jeff Meyers
Customer churn management focuses on identifying potential churners and implementing incentives that can cure churn. The success of a churn management program depends on accurately identifying potential churners and understanding what conditions contribute to churn. However, in the presence of uncertainties in the process of churn, such as competing risks and unpredictable customer behaviour, the accuracy of the prediction models can be limited. To overcome this, we employ a competing risk methodology within a random survival forest framework that accurately computes the risks of churn and identifies relationships between the risks and customer behaviour. In contrast to existing methods, the proposed model does not rely on a specific functional form to model the relationships between risk and behaviour, and does not have underlying distributional assumptions, both of which are limitations faced in practice. The performance of the method is evaluated using data from a membership-based firm in the hospitality industry, where customers face two competing churning events. The proposed model improves prediction accuracy by up to 20%, compared to conventional models. The findings from this work can allow marketers to identify and understand churners, and develop strategies on how to design and implement incentives.
The Identity of Engineering Expertise: Implicitly Biased and Sustaining the Gender Gap
Cristina Poleacovschi, Kasey Faust, Arkajyoti Roy and Scott Feinstein
Experts bring the necessary comprehensive and authoritative knowledge to address issues as they arise throughout a project. This expertise is critical for construction and engineering organizations due to each project’s dynamic and unique characteristics. Practitioners often perceive expertise as objective across demographics; however, this study demonstrates that it is in fact subjective with gender implicit biases concerning expertise ratings. Enabling this study is survey data spanning 279 employees from a single construction and engineering company. The results revealed that men were likely to receive higher expertise ratings as compared to women. Further, this study found that men were likely to rate women’s expertise lower as compared to men’s expertise, while women’s expertise ratings show marginal difference based on gender. This research identified gender implicit biases within one large construction and engineering company, which may be typical within the industry more widely. Finally, the research contributes to the role congruity theory by showing the alignment and misalignment between expertise roles and gender roles.
A Risk-Adjusted Control Chart to Evaluate Intensity Modulated Radiation Therapy Plan Quality
DVH Analytics: A DVH Database for Clinicians and Researchers
Robust Optimization with Time-Dependent Uncertainty in Radiation Therapy
On Correlations in IMRT Planning Aims
MS 3043. Business Statistics with Computer Applications IIANOVA, Test for Independence, Regression, Forecasting, Quality Control & Decision Analysis
DA 6813. Data Analytics ApplicationsBusiness applications of machine learning topics, including regression-based, support vector, decision trees, & ensemble methods. Use of case-studies.
BA 2120. Predictive AnalyticsConfidence intervals, hypothesis testing, analysis of variance, two-way tables, linear regression, & forecasting
STAT 6010/6010P. Statistics for Managerial DecisionsExploratory data analysis, confidence intervals, hypothesis testing, analysis of variance, two-way tables, linear regression, & forecasting.
STAT 4060/5060. Sampling DesignStatistical estimators for simple random, stratified, systematic, & cluster sampling, sample size computations, & design of surveys.
OR 3800. Introduction to Management ScienceLinear & integer programming, decision analysis under uncertainty & simulation