Master of Applied Statistics Program Co-chairs:
- Naomi Altman
- Armine Bagyan
- Steve Bai
- Indrani Basak
- Prasanta Basak
- Srabashi Basu
- Rashmi Bomiriya
- Priyangi Bulathsinhala
- Mosuk Chow
- Linda Clark
- Tracey Hammel
- Kwame Kankam
- Eugene J. Lengerich
- Bruce Lord
- Jason Morton
- Xiaoyue Niu
- Eric Nord
- Iain Pardoe
- Monia Ranalli
- Megan Romer
- James L Rosenberger
- Scott Roths
- Eduardo Santiago
- Durland Shumway
- Laura J. Simon
- Aleksandra B. Slavković
- Andrew J. Wiesner
- Manel M. Wijesinha
Naomi Altman's interest in statistics stems from her broad interests in the application of the mathematical sciences to problems in other disciplines — in particular, medical and biological sciences, Earth and environmental sciences, and social sciences. Her statistical interests include bioinformatics, high dimensional data, nonparametric smoothing, model selection, and analysis of functional and longitudinal data. Dr. Altman's current research is in bioinformatics and dimension reduction.
Dr. Altman is a member of the Clinical and Translational Sciences Institute; the core faculty in the Biostatistics, Epidemiology, and Research Design group, which provides consulting services for biomedical research at Penn State; the Huck Institutes of Life Sciences, specializing in bioinformatics and genomics; and the core faculty in the Computation, Bioinformatics, and Statistics (CBIOS) Training Program.
Dr. Altman was one of the founding authors of the New Researcher's Guide of the Institute for Mathematical Statistics and is co-author of the Points of Significance column on statistical methodology for Nature Methods Journal. She is also part of the statistical review board for Nature Publishing as part of their reproducible research initiative.
Dr. Armine Bagyan earned her Ph.D. in Statistics from the Department of Statistics at Penn State. Her research interests include topics related to limit theory for sequences with dependence and dimension reduction. She has been teaching statistics and probability courses at Penn State, online and in residence.
Dr. Steve Bai has been a mathematical reviewer for the U.S. Food and Drug Administration (FDA) since 2005 after earning a doctorate in statistics from Penn State. Throughout his career in Center for Drug Evaluation and Research (CDER) and the FDA, he has reviewed many regulatory submissions seeking indications in cardio/renal, neurology, and psychiatry. Based on his review work, his areas of research interest include adaptive design, non-inferiority trials, meta-analysis, multiplicity adjustment, and multi-regional trials.
Dr. Indrani Basak’s main research interests are in analytic hierarchy process (AHP), robust statistical methods, and censored data problems. AHP is a decision-making tool for problems with multiple criteria and multiple alternatives, especially useful in the business field. Dr. Basak studied methods of assigning statistical values to alternative priorities. AHP is related to paired comparison methods of considering alternatives two at a time.
Dr. Basak has also developed new applications within the established field of robust statistical analysis. These are ways of determining if the statistical methods being used are still valid when the assumptions they are based on are relaxed. Some of her interests in those analysis concern survival analysis and reliability of systems which are useful in the medical and engineering sciences.
Dr. Basak’s recent area of work is in statistical inferential and prediction methods in the setup of progressive censoring, a special censoring method which is relatively new. Censoring occurs when exact survival times are known only for a portion of the individuals or items under study. The complete survival times may not have been observed by the experimenter either intentionally (to reduce the cost and time of the experiments) or unintentionally (subjects may have withdrawn from the study or could not be tracked down).
Dr. Prasanta Basak's research interests have been in the field of finite mixture models, record statistics and paired comparison methods. In finite mixture models, the distribution of a process is assumed to be a weighted combination of two or more distributions. The main challenges in this field are to estimate the parameters of the distributions and the number of component distributions. For record statistics, which are closely related to order statistics, we can make conclusions based on a set of records. In pairwise comparison experiments, we compare two items or treatments based on certain criteria. Recently, Dr. Basak’s research interest has extended to prediction problems and progressive censoring methods. In particular, he is interested in problems such as predicting a future record value given past record values, prediction of failure times in progressively censored samples and parameter estimation for three-parameter lognormal distribution under progressive censoring.
Dr. Srabashi Basu has been pursuing research in applied and theoretical statistics for about two decades. She has worked with epidemiologists and medical researchers both in the United States and in India. She has provided service to a major airline and had spearheaded the development of a forecasting suite for passenger arrival and resource allocation. In her current position she is working for one of the largest contract research organizations (CRO) in the drug development domain. Dr. Basu is also very interested in the upcoming field of analytics and undertakes various consulting problems in different domains, such as media, marketing, risk analytics, and revenue management.
Dr. Basu has original research publications in a number of refereed journals in statistics, biostatistics, and medicine. She has several book chapters to her credit and is an international contributor to a business statistics text book.
Dr. Basu also likes to teach and was with the Indian Statistical Institute for nearly ten years. She has been teaching regularly for Penn State's World Campus since the spring of 2009. In India she works as a corporate trainer for various players in analytics domain and for Indian Institutes of Management.
Dr. Rashmi Bomiriya is a data scientist currently based in Sri Lanka, who works for a United States-based global market leader in alerts, signals, and big data derived from large-scale analysis of satellite imagery and other geospatial data sources. She earned her doctorate in statistics in August 2014 from Penn State. Her main research interests include social network analysis, exponential random graph models, and Bayesian Inference. She was engaged with the Statistical Consulting Center at Penn State for a few years as a research assistant as well as an adjunct faculty. She has been teaching regularly for Penn State World Campus since 2014.
Dr. Bulathsinhala earned her Ph.D. in Statistics in 2016 from Southern Methodist University in Dallas, Texas. She has research interests in applications of spatial statistical methods and functional Magnetic Resonance Imaging (fMRI) data. For her Ph.D., she investigated optimal spatial methods for the analysis of functional connectivity in fMRI data. She sought to reduce the large number of voxel-based tests that arise in current methods.
Dr. Mosuk Chow is the MAS Program Director and her areas of research interest include biostatistics, statistical decision theory, Bayesian inference, and sampling methods. An important question in statistical decision theory is to characterize the set of all optimal procedures. An admissible procedure is optimal in the weak sense that it cannot be outperformed by another procedure completely in all circumstances. It is thus desirable to find necessary conditions for admissible procedures. Her work in decision theory involves finding such necessary conditions, investigating the admissibility properties of various estimators for problems arising from biology, genetics, and fishery.
Since for most cases a necessary condition for admissibility is that the procedure corresponds to a generalized Bayes rule, Dr. Chow's research also covers Bayesian inference. With recent advances in Bayesian computation methods, she has used Markov chain Monte Carlo methods in some of her work. Currently she is interested in Bayesian inference for aggregated distributions under various sampling schemes and Bayesian approach to problems related to biostatistics.
Linda Clark received her doctorate in higher education from Penn State. Her area of specialization is program assessment as it pertains to academic and nonacademic initiatives in higher education.
Dr. Clark also has extensive higher education experience from working in student support, administrative, and faculty positions. Her career includes substantial teaching in statistics, and she has taught extensively in the online program. The main focus of her teaching is around graduate introductory courses, regression, analysis of variance, and consulting.
Dr. Clark has considerable experience in intercollegiate athletics. She served as vice chair of the Intercollegiate Athletics Committee of the Faculty Senate at Penn State, and she was one of two faculty members selected to be initial members of the Athletic Integrity Council at the University. She has collaborated on Title IX research and founded the Faculty Partner Program at Penn State, creating and cultivating relationships between athletic and academic environments.
Now at Central Connecticut State University, Dr. Clark coordinates the Ed.D. program in higher education. This program prepares current higher-education professionals for career advancement and increases the knowledge, scholarly aptitude, and skills for more effective and efficient administrators.
Dr. Tracey Hammel's main interest is in teaching statistics and has been teaching online since 2008. Dr. Hammel has considerable teaching experience at both the graduate and undergraduate levels. She earned her doctorate from Penn State's Department of Statistics in 2010 and her primary area of research is in mixture models. These models arise when the population under study is believed to be constructed of several distinct groups, but group membership is unknown.
Kwame Kankam earned his doctorate in statistics from Penn State's Department of Statistics in December 2014. His research interests include design of experiments and high dimensional data analysis, and he has applied penalized likelihood methods to the analysis of robust parameter design experiments. He has experience teaching both online and in-residence courses.
Dr. Eugene Lengerich is professor in the Department of Public Health Sciences at Penn State's College of Medicine, and director of the Community Sciences and Health Outcomes (CSHO) Core at the Penn State Hershey Cancer Institute. He brings 24 years of experience in epidemiology with a focus on participatory research, public health informatics and disease surveillance, and health disparities. His research has been funded by the National Cancer Institute, Centers for Disease Control and Prevention, Health Resources and Services Administration, Lance Armstrong Foundation, American Cancer Society, Pittsburgh Affiliate of Susan G. Komen for the Cure®, and the Pennsylvania Department of Health. He has led the analysis of registry data to quantify geospatial patterns in disease incidence and morbidity, developed a model digital cancer atlas for Pennsylvania, and used these analyses to design intervention strategies. This research has also led to the development of visualization software and methods.
Prior to joining the faculty at Penn State, he was the state chronic disease epidemiologist for North Carolina, during which time he directed the Behavioral Risk Factor Surveillance System (BRFSS) and evaluated the quality of the North Carolina Central Cancer Registry. Since 2004, he has been the principal investigator of the Northern Appalachia Cancer Network, which was recognized nationally in 2009 by the Association of Public and Land-grant Universities for engaged research.
Dr. Lengerich directs an online graduate program in public health preparedness, with an emphasis on disease and syndromic surveillance. Dr. Lengerich received epidemiologic training via the Epidemic Intelligence Service program and the Preventive Medicine Residency at the Centers for Disease Control and Prevention in Atlanta, Georgia. Dr. Lengerich enjoys teaching epidemiology and mentoring pre- and post-doctorate scholars.
Dr. Bruce Lord has more than thirty years of experience as a resource economist specializing in the impacts of natural resources upon rural economies.
He has made extensive use of survey research to study the economic impacts of the wood products industry and natural resource-based travel and tourism. His research interests are in survey design and analysis; natural resource measurements; and economic forecasting.
Jason Morton is an assistant professor of mathematics and statistics at Penn State. His research interests involve the fields of applied algebraic geometry and tensor networks in statistics, computer science, and quantum information. He has published on tensor networks and monoidal categories in machine learning, mathematical finance, quantum condensed matter physics, and computational complexity. He manages a team of researchers at Penn State, which includes research associates, graduate students, and undergraduates.
Dr. Morton was part of the DARPA Fundamental Laws of Biology and Topological Data Analysis programs and a principal investigator on the Deep Learning program; in each he developed novel mathematics as well as machine learning and statistical techniques for DoD applications. Dr. Morton has received awards including the DARPA Young Faculty Award, and has managed multiple research projects for the DoD.
Xiaoyue Niu serves as the associate director of the Statistical Consulting Center at Penn State. She works closely with researchers and mentors graduate students working on consulting projects. Dr. Niu received her Ph.D. in statistics from the University of Washington.
Prior to joining the Department of Statistics at Penn State, Niu worked at a global health institute as a research fellow. Dr. Niu's research interests include multivariate analysis, latent variable models, Bayesian statistics, and social network and relational data analysis, with applications in global health and social sciences.
Eric Nord earned his doctoral degree from Penn State in 2008 and is now an assistant professor of biology at Greenville College, Greenville, Illinois. As a ecologist with broad interests in the area of sustainability, Dr. Nord's research involves extensive use of R. He is particularly interested in questions of sustainability in food production, having worked in Honduras, China, South Africa, and Malawi.
Iain Pardoe resides in Nelson, British Columbia, Canada, where he is an independent statistical consultant and mathematics/statistics instructor. He obtained his Ph.D. in statistics at the University of Minnesota in August 2001. He is the author of Applied Regression Modeling, (Wiley, 2nd edition, 2012), a university-level statistics textbook.
Dr. Pardoe has been involved with a number of statistical consulting projects over the last 15 years, including a criminal justice sentencing study; manufacturing demand management forecasting; scheduling software validation; and eco-labeling marketing surveys. He has experience in a broad range of statistical areas, including statistical theory and mathematical statistics, measure theory and probability, applied statistical methods, linear models, computational statistical methods, linear and nonlinear regression, multivariate methods, experimental design, Bayesian decision theory and data analysis, modern nonparametrics, and regression graphics.
Dr. Monia Ranalli's research interests include finite mixture models, composite likelihood methods, application of statistics to spatial data, social sciences, and genomics. She earned her doctorate in statistics from Sapienza University of Rome in December 2014.
Ranalli entered Penn State's Department of Statistics as a visiting scholar under the supervision of Professor Bruce Lindsay in January 2014, and completed a postdoc in 2015. She has experience teaching both online and in residence, and began regularly instructing Penn State World Campus courses in 2016.
Dr. Megan Romer’s main interest is in teaching statistics. She has been teaching online since 2009 when she earned her doctorate from Penn State's Department of Statistics. She enjoys discussing statistical concepts and problems with students. Before returning to school to finish her doctorate, Dr. Romer worked in clinical trials as a senior research support associate. Dr. Romer’s primary area of research is in incomplete data.
Dr. Scott Roths earned his doctorate in August, 2011 from Penn State and has been teaching as a faculty lecturer since the fall of 2011. He also has considerable teaching experience at the undergraduate level as well.
Dr. Roths is currently investigating ways to control the false discovery rate (FDR) and false discovery proportion (FDP) in multiple testing situations. The FDR and FDP are often the preferred error quantities to control because they do not become prohibitively restrictive, unlike the traditional family-wise error rate, when the number of tests is very large. Some notable examples of large-scale testing are genetic profile analysis and astronomical source detection. Among the most popular methods to control the FDR are the stepwise procedures, where test statistics or p-values are ordered by significance and compared to pre-specified cutoffs. These procedures are easy to use, but they focus only on the binary indicators for whether the test statistics exceed their cutoffs or not. He is investigating the effect of this discretization on the power and variance of these procedures and methods that do not use such indicators.
Dr. Eduardo Santiago has interest in industrial statistics applications, specifically in design of experiments (DOE). His area of expertise is in optimal designs, and the creation of algorithms to produce better customized designs. He has more than ten years of industry experience working with automotive, food and beverage, pharmaceutical, medical device, insurance, and chemical industries, where he has cooperated with customers on the instruction of statistics or consulting projects to improve the quality of products.
Dr. Santiago has written several papers in different areas including DOE and statistical process control. Has co-developed a new control chart to monitor adverse events such as nosocomial infections and urinary tract infections, which has been included in Minitab Statistical Software.
Dr. Durland Shumway's academic background is in the environmental sciences. He earned a master of science in ecology from Rutgers and a doctorate in forest science at Penn State in 1990. Dr. Shumway taught at Frostburg State University in Maryland as an associate professor of forest ecology where his research expertise was in tree architecture. In 2005, he returned to Penn State as a faculty consultant and research associate in the Department of Statistics. From 2007 to 2012 he served as the director of the department's Statistical Consulting Center. Today he teaches both online and resident courses in ANOVA, regression, and design of experiments, and is also currently working on developing case studies.
Dr. Laura Simon is primarily interested in teaching statistical concepts to undergraduate math majors and to nonstatisticians in the biostatistical and health sciences fields. As her courses illustrate, she is a proponent of active, hands-on, and web-based statistical education. Dr. Simon was nominated for the 2004 Eberly College of Science's C. I. Noll Award for Teaching Excellence. She was also nominated by two students and subsequently named to Who's Who Among America's Teachers in 2004 and 2005.
Dr. Simon co-authored nine units of the web-based introductory statistics text, Visualizing Statistics (Cybergnostics, Inc.). She is a reviewer of statistical education resources submitted to CAUSEweb.org and merlot.org. Other professional interests include biostatistical consulting, repeated measures modeling, clinical trials development, research data management, and web-page programming.
Before moving to the University Park campus in 1996, Dr. Simon worked at the Center for Biostatistics and Epidemiology at the Penn State College of Medicine at the Milton S. Hershey Medical Center. While there, she consulted with more than 40 medical researchers and provided overall leadership to the data management unit on numerous projects, including the National Institutes of Health-funded National Interstitial Cystitis Database Study.
Prior to working at Penn State, Dr. Simon was a statistician at Auke Bay Research Laboratories in Auke Bay, Alaska, and a visiting summer statistician at Rohm and Haas Research Laboratories in Spring House, Pennsylvania.
Dr. Aleksandra Slavković is Co-chair of the Applied Statistics program and her past and current research interests include usability evaluation methods, human performance in virtual environments, statistical data mining, application of statistics to social sciences, algebraic statistics, and statistical approaches to confidentiality and data disclosure. Her Ph.D. dissertation work focuses on statistical methodologies for disclosure limitation and data confidentiality, and presents new theoretical links between disclosure limitation, statistical theory, and computational algebraic geometry. It is a unique and interesting integration of diverse results from conditional specification of joint distribution, graphical models, disclosure limitation, and algebraic statistics.
Dr. Slavković served as a consultant to the National Academy of Sciences/National Research Council Committee to Review the Scientific Evidence on the Polygraph in 2001 and part of 2002. In 2003 she received an honorable mention for the best student paper from the Committee on Statisticians in Defense and National Security of the American Statistical Association.
Dr. Andrew Wiesner's primary research interests involve educational statistics. He has presented several faculty workshops on interpreting item statistics to improve exams and the fundamentals of test item writing. Currently his research entails how frequent testing of students can improve their overall performance and increase learning. This includes the use of several web tools that are available to deliver online assessments.
Dr. Wijesinha focuses her research on optimal designs in multiresponse regression models, where observations are made on several dependent variables. An optimal design is a collection of predictor values called design points that satisfies a predefined criterion, such as minimizing the maximum of predicted response variances. Well-known single response algorithms for constructing optimal designs can be adapted to cover the multiresponse case. This is done by incorporating the estimation of error dispersion matrix elements into the algorithms simultaneously with the generation of optimal design points.
Since her postdoctoral training in biostatistics, Dr. Wijesinha has expanded her research areas to include dose response experiments and microarray data analysis. In these areas of biostatistics, she finds many opportunities to apply her optimal design expertise.