Arka Roy

Assistant Professor

Management Science and Statistics

The University of Texas at San Antonio

Email: arkajyoti.roy@utsa.edu

Office: BB 4.05.02

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.

Research Interests

Robust Optimization, Time-Dependent Uncertainties, Data Analytics, Radiation Therapy

Selected Publications

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.
Forthcoming in JCEE

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

Arkajyoti Roy, Dan Cutright, Mahesh Gopalakrishnan, Arthur Yeh, and Bharat Mittal
Purpose: This study aimed to develop a quality control framework for intensity modulated radiation therapy plan evaluations that can account for variations in patient- and treatment-specific risk factors.Methods: Patient-specific risk factors, such as a patient’s anatomy and tumor dose requirements, affect organs-at-risk (OARs) dose-volume histograms (DVHs), which in turn affects plan quality and can potentially cause adverse effects. Treatment-specific risk factors, such as the use of chemotherapy and surgery, are clinically relevant when evaluating radiation therapy planning criteria. A risk-adjusted control chart was developed to identify unusual plan quality after accounting for patient- and treatment-specific risk factors. In this proof of concept, 6 OAR DVH points and average monitor units serve as proxies for plan quality. Eighteen risk factors are considered for modeling quality: planning target volume (PTV) and OAR cross-sectional areas; volumes, spreads, and surface areas; minimum and centroid distances between OARs and the PTV; 6 PTV DVH points; use of chemotherapy; and surgery. A total of 69 head and neck cases were used to demonstrate the application of risk-adjusted control charts, and the results were compared with the application of conventional control charts.Results: The risk-adjusted control chart remains robust to interpatient variations in the studied risk factors, unlike the conventional control chart. For the brainstem, the conventional chart signaled 4 patients with unusual (out-of-control) doses to 2% brainstem volume. However, the adjusted chart did not signal any plans after accounting for their risk factors. For the spinal cord doses to 2% brainstem volume, the conventional chart signaled 2 patients, and the adjusted chart signaled a separate patient after accounting for their risk factors. Similar adjustments were observed for the other DVH points when evaluating brainstem, spinal cord, ipsilateral parotid, and average monitor units. The adjustments can be directly attributed to the patient- and treatment-specific risk factors.Conclusions: A risk-adjusted control chart was developed to evaluate plan quality, which is robust to variations in patient- and treatment-specific parameters.

DVH Analytics: A DVH Database for Clinicians and Researchers

Dan Cutright, Mahesh Gopalakrishnan, Arkajyoti Roy, Aditya Panchal, and Bharat Mittal
In this study, we build a vendor‐agnostic software application capable of importing and analyzing non‐image‐based DICOM files for various radiation treatment modalities (i.e., DICOM RT Dose, RT Structure, and RT Plan files). Dose‐volume histogram (DVH) and planning data are imported into a SQL database, and methods are provided to manage, edit, view, and download data. Furthermore, the software provides various analytical tools for plan evaluations, plan comparisons, benchmarking, and plan outcome predictions. DVH Analytics is developed using Python, including libraries such as pydicom, dicompyler, psycopg2, SciPy, Statsmodels, and Bokeh for parsing DICOM files, computing DVHs, communicating with a PostgreSQL database, performing statistical analyses, and creating a web‐based user interface. This software is open‐source and compatible with Windows, Mac OS, and Linux. For proof‐of‐concept, a database with over 3,000 DVHs from a single physician's head & neck practice was built. From these data, differences in means, correlations, and temporal trends in dose to multiple organs‐at‐risk (OARs) were observed. Furthermore, an example of the predictive regression tool is reported, where a model was constructed to predict maximum dose to brainstem based on minimum distance from planning target volume (PTV) and treatment beam source‐to‐skin distance (SSD). With DVH Analytics, we have developed a free, open‐source software program to parse, organize, and analyze non‐image‐based DICOM data for use in a radiation oncology setting. Furthermore, this software can be used to generate statistical models for the purposes of quality control or outcome predictions and correlations.

Robust Optimization with Time-Dependent Uncertainty in Radiation Therapy

Omid Nohadani and Arkajyoti Roy
In the recent past, robust optimization methods have been developed and successfully applied to a variety of single-stage problems. More recently, some of these approaches have been extended to multi-stage settings with fixed uncertainties. However, in many real-world applications, uncertainties evolve over time, rendering the robust solutions suboptimal. This issue is particularly prevalent in medical decision making, where a patient’s condition can change during the course of the treatment. In the context of radiation therapy, changes in cell oxygenation directly affect the response to radiation. To address such uncertain changes, we provide a general robust optimization framework that incorporates time-dependent uncertainty sets in a tractable fashion. Temporal changes reside within a cone, whose projection at each step yields the current uncertainty set. We develop conic robust two-stage linear problems and provide their robust counterparts for uncertain constraint parameters, covering the range of radiation therapy problems. For a clinical prostate cancer case, the time-dependent robust approach improves the tumor control throughout the treatment, as opposed to current methods that lose efficacy at some stage. We show that this advantage does not bear additional risks compared to current clinical methods. For intermediate diagnostics, we provide the optimal observation timing that maximizes the value of information. While these findings are relevant to clinical settings, they are also general and can be applied to a broad range of applications; e.g., in maintenance scheduling.

On Correlations in IMRT Planning Aims

Arkajyoti Roy, Indra Das, and Omid Nohadani
The purpose was to study correlations amongst IMRT DVH evaluation points and how their relaxation impacts the overall plan. 100 head‐and‐neck cancer cases, using the Eclipse treatment planning system with the same protocol, are statistically analyzed for PTV, brainstem, and spinal cord. To measure variations amongst the plans, we use (i) interquartile range (IQR) of volume as a function of dose, (ii) interquartile range of dose as a function of volume, and (iii) dose falloff. To determine correlations for institutional and ICRU goals, conditional probabilities and medians are computed. We observe that most plans exceed the median PTV dose (average prescribed dose). Furthermore, satisfying reduced the probability of also satisfying , constituting a negative correlation of these goals. On the other hand, satisfying increased the probability of satisfying , suggesting a positive correlation. A positive correlation is also observed between the PTV and . Similarly, a positive correlation between the brainstem and is measured by an increase in the conditional median of , when is violated. Despite the imposed institutional and international recommendations, significant variations amongst DVH points can occur. Even though DVH aims are evaluated independently, sizable correlations amongst them are possible, indicating that some goals cannot be satisfied concurrently, calling for unbiased plan criteria.

Teaching

At UTSA

MS 3043. Business Statistics with Computer Applications II

ANOVA, Test for Independence, Regression, Forecasting, Quality Control & Decision Analysis

DA 6813. Data Analytics Applications

Business applications of machine learning topics, including regression-based, support vector, decision trees, & ensemble methods. Use of case-studies.

At BGSU

BA 2120. Predictive Analytics

Confidence intervals, hypothesis testing, analysis of variance, two-way tables, linear regression, & forecasting

STAT 6010/6010P. Statistics for Managerial Decisions

Exploratory data analysis, confidence intervals, hypothesis testing, analysis of variance, two-way tables, linear regression, & forecasting.

STAT 4060/5060. Sampling Design

Statistical estimators for simple random, stratified, systematic, & cluster sampling, sample size computations, & design of surveys.

OR 3800. Introduction to Management Science

Linear & integer programming, decision analysis under uncertainty & simulation