Austin R. Brown
PhD: Applied Statistics & Research Methods, University of Northern Colorado
Research Interests: Dr. Austin Brown's research interests are primarily focused on process improvement, including both solving applied problems as well as developing novel control charting techniques (with specific interest in nonparametric methods) and statistics in sports. He has published and presented work at national and international conferences in both areas. Process improvement includes a variety of statistical methods which can be used to evaluate, inform, and control a process. While process improvement is traditionally thought of in manufacturing settings, it can be applied in nearly any area that has measurable inputs and outputs, including education, medicine, and economics. One tool which can be used in process improvement applications is the control chart, which is a graphical and statistical tool used to identify whether a characteristic of a process has substantially changed from the desired value. As the variety of areas of application for process improvement grows, so to does the need for control charts designed for the specific aspects of the processes being monitored. Statistics in sports is also a broad field with lots of areas of application including sport management, performance prediction, injury management, betting strategies, fantasy sports strategies, among many, many other areas.
Situational Awareness in Acute Patient Deterioration: Identifying Student Time to Task. https://europepmc.org/article/med/33481495
The alternative distribution of the non parametric extended median test CUSUM chart for multiple stream processes
A nonparametric CUSUM control chart for multiple stream processes based on a modified extended median test
Outlook in life of older adults and their health and community condition
The effect of a repeat septic shock simulation on the knowledge and skill performance of undergraduate nursing students
Motivation and Postsecondary Enrollment Among High School Students Whose Parents Did Not Go to College
PhD: Educational Psychology: Cognition and Development, University of Georgia
Research Interests: Structural equation modeling, multilevel modeling, psychometrics, learning sciences
Vaughn, A., Johnson, M., & Taasoobshirazi, G. (2020). Impostor phenomenon and motivation: Women in higher education. Studies in Higher Education, 45(4), 780-795.
Taasoobshirazi, G., Puckett, C., & Marchand, G., (2019). Stereotype threat and gender differences in biology. International Journal of Mathematics and Science Education, 17 (7), 1267-1282.
Sunny, C.E., Taasoobshirazi, G., Clark, L., & Marchand, G. (2017). Stereotype threat and gender differences in chemistry. Instructional Science, 45(2)-157-175.
Carr, M., & Taasoobshirazi, G. (2017). Is Strategy Variability Advantageous?: It Depends on Grade and Type of Strategy. Learning and Individual Differences, 54, 102-108.
Taasoobshirazi, G., & Wang, S. (2016). The Performance of the SRMR, RMSEA, CFI, and TLI: An Examination of Sample Size, Path Size, and Degrees of Freedom. Journal of Applied Quantitative Methods, 11(3).
Taasoobshirazi, G., Heddy, B., Bailey, M., & Farley, J. (2016). A multivariate model of conceptual change. Instructional Science, 44(2), 125-145.
PhD: Epidemiology, Emory University
Research Interests: While I am a part-time assistant professor, I am also a full-time epidemiologist at the Centers for Disease Control and Prevention (CDC). At CDC, I lead a time of analysts primarily conducting research in antibiotic resistance, antibiotic stewardship, and healthcare associated infections. My studies typically use large electronic health care and administrative data to look at important questions using methods in epidemiology and incorporating newer methods related to data science.
John A Jernigan, Kelly M Hatfield, Hannah Wolford, Richard E Nelson, Babatunde Olubajo, Sujan C Reddy, Natalie McCarthy, Prabasaj Paul, L Clifford McDonald, Alex Kallen, Anthony Fiore, Michael Craig, James Baggs. Multidrug-resistant bacterial infections in US hospitalized patients, 2012–2017. N Engl J Med, 382(14), 1309-1319. https://www.nejm.org/doi/full/10.1056/NEJMoa1914433
James Baggs, Scott K Fridkin, Lori A Pollack, Arjun Srinivasan, John A Jernigan. Estimating national trends in inpatient antibiotic use among US hospitals from 2006 to 2012, JAMA Internal Medicine, 176(11), 1639-1648. https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2553294
James Baggs, Julianne Gee, Edwin Lewis, Gabrielle Fowler, Patti Benson, Tracy Lieu, Allison Naleway, Nicola P Klein, Roger Baxter, Edward Belongia, Jason Glanz, Simon J Hambidge, Steven J Jacobsen, Lisa Jackson, Jim Nordin, Eric Weintraub. The Vaccine Safety Datalink: a model for monitoring immunization safety, Pediatrics, 127(S1), S45-S53. https://doi.org/10.1542/peds.2010-1722H
James Baggs, John A Jernigan, Alison Laufer Halpin, Lauren Epstein, Kelly M Hatfield, L Clifford McDonald. Risk of subsequent sepsis within 90 days after a hospital stay by type of antibiotic exposure, Clinical Infectious Diseases, 66(7), 1004-1012. https://doi.org/10.1093/cid/cix947
Eric S Weintraub, James Baggs, Jonathan Duffy, Claudia Vellozzi, Edward A Belongia, Stephanie Irving, Nicola P Klein, Jason M Glanz, Steven J Jacobsen, Allison Naleway, Lisa A Jackson, Frank DeStefano. Risk of intussusception after monovalent rotavirus vaccination, N Engl J Med, 370(6),513-519. https://www.nejm.org/doi/full/10.1056/nejmoa1311738
Athena P Kourtis, Kelly Hatfield, James Baggs, Yi Mu, Isaac See, Erin Epson, Joelle Nadle, Marion A Kainer, Ghinwa Dumyati, Susan Petit, Susan M Ray, Emerging Infections Program MRSA, David Ham, Catherine Capers, Heather Ewing, Nicole Coffin, L Clifford McDonald, John Jernigan, Denise Cardo. Vital Signs: Epidemiology and Recent Trends in Methicillin-Resistant and in Methicillin-Susceptible Staphylococcus aureus Bloodstream Infections — United States, MMWR Morb Mortal Wkly Rep, 68(9), 214-219. https://www.cdc.gov/mmwr/volumes/68/wr/mm6809e1.htm?s_cid=mm6809e1_w
Jennifer Lewis PriestleyGoogle Scholar
PhD: Decision Sciences, Georgia State University
Research Interests: My research interests are aligned with the evolution of data science as a nascent academic discipline, the ethics of data science, and the wide range of applications of emerging methods in data science.
Zhang, L., Priestley, J., DeMaio, J., Ni, S., Tian, X. (2020). "Measuring Customer Similarity and Identifying Cross Selling Products by Community Detection". Journal of Big Data. 21(1). DOI: 10.1089/big.2020.0044.
Fatehi, K., Priestley, J. (2020) “The expanded view of individualism and collectivism: One, two, or four dimensions?”. International Journal of Cross Cultural Management. 20(1). DOI: 10.1177/1470595820913077.
Culligan, P., Lewis, C., Priestley, J., and Mushonga, N. (2019). "Long-Term Outcomes of Robotic-Assisted Laparoscopic Sacrocolpopexy Using Lightweight Y-Mesh." Female Pelvic Med Reconstructive Surgery DOI:10.1097/SPV.0000000000000788.
Zhang, L., Ray, H., Priestley, J. and Tan, S. (2019). "A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data" Journal of Applied Statistics. DOI:10.1080/02664763.2019.1643829
Huber, S, Priestley, J., Gadidov, B. and Culligan, P. (2019). "Understanding Your Online Ratings: A Methodological analysis using urogynecologists in the United States", Female Pelvic Medicine and Reconstructive Surgery. 25(2). DOI: 10.1097/SPV.0000000000000676
Fatehi, K and Priestley, J. (2018). "Beauty Is in the Eye of Beholder: Intracultural and Transcultural Heterogeneity of Individuals", Cross Cultural Research 52(5). DOI: 10.1177/1069397117732749
Priestley, J. and McGrath, R. (2018). "The Evolution of Data Science: A New Mode of Knowledge Production", International Journal of Knowledge Management, 15(2). DOI: 10.4018/IJKM.2019040106
Fatehi, K., Priestley, J., Taasoobshirazi, G. (2018). "International marketing and intra-cultural heterogeneity", Asia Pacific Journal of Marketing and Logistics, Vol. 30 Issue: 3, pp.669-688. DOI:10.1108/APJML-04-2017-0067
McGrath R.J., Priestley J.L., Zhou Y., Culligan P.J. (2018). "The Validity of Online Patient Ratings of Physicians: Analysis of Physician Peer Reviews and Patient Ratings", Interact J Med Res 2018;7(1):e8. DOI: 10.2196/ijmr.9350. PMID: 29631992.
Zhang, L., Priestley, J., & Ni, X. (2018). "Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models” International Journal of Database Management Systems, 10 (1), p. 1-14. DOI: 10.5121/ijdms.2018.10101
Zhang, L., Priestley, J., & Ni, X. (2018). “Comparison of Bankruptcy Prediction Models with Public Records and Firmographics”. Computer Science & Information Technology: Proceedings of the International Conference on Data Mining & Knowledge Management Process, Melbourne, Australia, February 17-18, 2018, Vol 8(3), pp. 97-109. DOI: 10.5121/csit.2018.80309
Fatehi, K., Priestley, J., Taasoobshirazi, G. (2017). "International marketing and intra-cultural heterogeneity", Asia Pacific Journal of Marketing and Logistics. DOI:10.1108/APJML-04-2017-0067
Culligan, P., Haughey, S., Lewis, C., Priestley, J., Salamon, C. (2018). “Sexual Satisfaction Changes Reported by Men After Their Partners' Robotic-Assisted Laparoscopic Sacrocolpopexies”. Female Pelvic Medicine & Reconstructive Surgery: March 1, 2018. DOI: 10.1097/SPV.0000000000000574
Xie, Y. Le, L., Hao, J., Priestley, J. “Deep Kernel: Learning Kernel Function from Data Using Deep Neural Network” (2016). BDCAT '16 Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies.
Fatehi, K., Kedia, B., Priestley, J. (2014). “Mindscapes and individual heterogeneity within and between cultures”. Journal of Business Research. 68(2) DOI:10.1016/j.jbusres.2014.07.005
Ruddy, J., Reisenman, P., Priestley, J., Brewster, L, Duwayri, Y., Veeraswamy, R. (2014). “Stent-graft Therapy for False lumen Aneurysmal Degeneration in Established Type B aortic Dissection (FADED) Results in Differential volumetric Remodeling of the thoracic Versus Abdominal aortic Segments”. Annals of Vascular Surgery. 28(7) DOI:10.1016/j.avsg.2014.02.009
Culligan, P., Gurshumov, E., Lewis, C., Priestley, J., Komar, J., Salamon, C. (2014). “Predictive validity of a training protocol using a robotic surgery simulator”. Female Pelvic Medical Reconstructive Surgery. Jan-Feb; 20(1): 48-51. DOI:10.1097/SPV.0000000000000045
Culligan, P., Gurshumov, E., Lewis, C. Priestley, J., Komar, J., Shah, N., Salamon, C. (2013) “Subjective and objective results 1 year after robotic sacrocolpopexu using a lightweight Y-mesh”. International Urogynecology Journal. 25(6) DOI:10.1007/s00192-013-2265-x
Salamon, C., Lewis, C., Priestley, J., Gurshumov, E., Culligan, P. (2013). “Prospective study of an ultra-lightweight polypropylene Y mesh for robotic sacrocolpopexy”. International Urogynecology Journal. 24(8) DOI: 10.1007/s00192-012-2021-7
Culligan, P., Salamon, C., Priestley, J.L., Shariati, A. (2013). “Porcine Dermis Compared With Polypropylene Mesh for Laparoscopic Sacrocolpopexy: A Randomized Controlled Trial”. Journal of Obstetrics and Gynecology, 121 (1). DOI:10.1097/aog.0b013e31827558dc
Culligan, P., Priestley, J., Littman, P., Salamon, C., Shariati, A. (2010). “Evaluation of a transvaginal mesh delivery system for the correction of pelvic organ prolapse: subjective and objective findings at least 1 year after surgery”. American Journal of Obstetrics and Gynecology 03(5). DOI: 10.1016/j.ajog.2010.07.020.
Priestley, J. and Massey, J. (2011). “Counting the Impossible: Sampling and Modeling to Achieve a Large State Homeless Count”. Journal of Public Management and Social Policy, Spring, 85-106. Link to paper.
Culligan, P., Sherer, J, Dyer, K, Priestley, J., Guingon-White, G, Delvecchio, D and Vangeli, M (2010) “A randomized clinical trial comparing pelvic floor muscle training to a Pilates exercise program for improving pelvic muscle strength”. International Journal of Urogynocology. 21(4), 401-408. DOI:10.1007/s00192-009-1046-z.
Culligan, P., Priestley, J.L., Littman, P., Salamon, C., Shariati, A. (2010). “Evaluation of a transvaginal mesh delivery system for the correction of pelvic organ prolapse: subjective and objective findings at least 1 year after surgery”. American Journal of Obstetrics and Gynecology 03(5). DOI:10.1016/j.ajog.2010.07.020.
Culligan, P., Priestley, J., Blackwell L., Tate, S. (2010). “Can urethral retro-resistance pressures predict mid urethral sling outcomes?” The Journal of Reproductive Medicine, 55,103-107. pmid: 20506669
Johnson, P., Priestley, J., Porter, K. J., Petrillo, J. (2010). “Complementary and alternative medicine: Attitudes and use among health educators in the United States”. American Journal of Health Education, 41(3), 167-177. DOI: 10.1080/19325037.2010.10598858.
Johnson, P., Priestley, J., Johnson, R. (2008). “A Survey of Complementary and Alternative Medicine Knowledge among Health Educators in the United States”. American Journal of Health Education 39(2), 66-79. DOI: 10.1080/19325037.2008.10599018.
Priestley, J. and Samaddar S. (2007). “Multi-Organizational Networks: Three Antecedents of Knowledge Transfer”. International Journal of Knowledge Management. DOI: 10.4018/jkm.2007010106.
Books, Book Chapters
Priestley, Jennifer and McGrath, Robert (2021). Closing the Analytics Talent Gap: An Executive’s Guide to Working with Universities. CRC Press (in press).
Priestley, Jennifer (2020). “Ethics and Figs: Why Data Scientists Cannot Take Shortcuts”. 97 Things About Ethics Everyone in Data Science Should Know: Collective Wisdom from the Experts. Franks, Bill (Ed). O’Reilly Press.
Priestley, Jennifer (2020). “Should Data Have Rights?”. 97 Things About Ethics Everyone in Data Science Should Know: Collective Wisdom from the Experts. Franks, Bill (Ed). O’Reilly Press.
Priestley, Jennifer (2019). "Data Analytics: Effective Methods for Presenting Results". Visualization to Improve Analytics S. Samaddar and S. Nargundkar (Eds). CRC Press.
Gadidov, Bogdan and Priestley, Jennifer (2017). “Does Yelp Matter? Analyzing (And Guide to Using) Ratings for a Quick Serve Restaurant Chain”. Guide to Big Data Applications. S. Srinivasan (Ed). Springer Publishing.
Nargundkar, Satish and Priestley, Jennifer (2003). “Model Development Techniques and Evaluation Methods for Prediction and Classification of Consumer Risk in the Credit Industry”. Neural Networks for Business Forecasting. P. Zhang (Ed) IRM Press: Hershey, PA.
Priestley, Jennifer (2005). “Knowledge Transfer Within Interorganizational Networks” Encyclopedia of Communities of Practice in Information and Knowledge Management. E. Coakes and S. Clarke (Eds). IRM Press: Hershey, PA.
Priestley, Jennifer and Samaddar, Subhashish (2006). “Information Sharing in Innovation Networks” Encyclopedia of Information Science and Technology, 2nd Edition. M. Khosrow-Pour (Ed). IRM Press: Hershey, PA.
Priestley, Jennifer and Samaddar, Subhashish (2006) “The Role of Ambiguity in the Transfer of Knowledge Within Multi-Organizational Networks”. Knowledge and Technology Management in Virtual Organizations: Issues, Trends, Opportunities and Solutions. G. Putnik and M. Cunha (Eds). IRM Press: Hershey, PA.
Joe DeMaioGoogle Scholar
PhD: Mathematics, Emory University
Research Interests: My expertise lies in the fields of Graph Theory and Combinatorics. These areas are rich with opportunity for both theoretical and applied research. On the theoretical side, one theme in my research is the use of graphs to realize combinatorial identities. Sometimes these were new identities and at other times, the method of proof was extremely novel. On the application side, lives the theme of routing (and other optimization) problems in graphs and networks. These range from the recreational such as the closed knight’s tour on a chessboard to the serious when decreasing travel times to incidents for the Cobb County Fire Department. While I have published some of my 25+ journal and proceedings papers as the sole author, I strive to include students in my research (and hence on the publications as well). Hence, most of my scholarly output has included students.
Zhang, l., Priestley, J., DeMaio, J., Ni, S., Tian, X., Measuring Customer Similarity and Identifying Cross-Selling Products by Community Detection, Big Data, 2020
DeMaio, J., Alum, M., Using the Optgraph Procedure to Construct Closed Knight’s Tours on Standard and Variant Chessboards, SAS Global Forum Conference Proceedings, 2020
Rudd, J.M., Henshaw, A.M., Staples, L., Akkineni, S., Li, L., DeMaio, J., Genetic Algorithm Guidance of a Constraint Programming Solver for the Multiple Traveling Salesman Problem
DeMaio, J., Yockey, B., Using Proc Optgraph to implement the Prize Collecting Traveling Salesman Problem in SAS (Gotta catch as many as we can in a Pokémon raid for Alice), 2019 SAS Global Forum Conference Proceedings
DeMaio, J., Henshaw, A., Staples, L., Graph Visualization for PROC OPTGRAPH, Proceedings from Southeast SAS Users Group 2018
Venn, A., DeMaio, J., Worker Safety in Energy Production in America A Comparative Analysis, Southeast SAS Users Group
DeMaio, J., Old Age and Treachery vs. Youth and Skill: An Analysis of the Mean Age of World Series Teams, Southeast SAS® Users Group (SESUG) Conference
DeMaio, J., Jacobson, J., Fibonacci number of the tadpole graph, Electronic Journal of Graph Theory and Applications (EJGTA) 2 (2), 129-138
Hillen, A., DeMaio, J., Math for Real: Preparing for the 2014 Winter Olympics:“when will I ever use this?”, MatheMatics teaching in the Middle school 19 (6), 392-392
DeMaio, J., Bindia, M., Which Chessboards have a Closed Knight's Tour within the Rectangular Prism?, Electronic Journal of Combinatorics 18, P8
Kevin B. Gittner
PhD: Applied Statistics and Research Methods, University of Northern Colorado
Research Interests: I have a passion for survey methods and latent variable analyses. I often seek out unique ways to incorporate secondary methodological hypotheses within primary research objectives. I have served as the primary statistician and methodologist on various public health research teams and enjoy a collaborative environment.
Matheny, L. M., Gittner, K., Harding, J., & Clanton, T. O. (2021). Patient Reported Outcome Measures in the Foot and Ankle: Normative Values Do Not Reflect 100% Full Function. Knee Surgery, Sports Traumatology, Arthroscopy, 29, 1276-1283.
Matheny, L., Clanton, T., Gittner, K., & Harding, J. (2018). Normative values for commonly reported outcome measures in the foot and ankle. Foot & Ankle Orthopaedics, 3(2), 2473011418S00011.
Gittner, L. S., & Gittner, K. B. (2017). Psychometrics of the “self-efficacy consumption of fruit and vegetables scale” in African American women. Eating behaviors, 26, 133-136.
PhD: Mathematics Teaching and Learning, Georgia State University
Research Interest: Dr. Kimberly Gardner is interested in Statistics Education research. She currently conducts research on the interdisciplinary nature of secondary mathematics and science teachers’ pedagogical content knowledge for teaching statistics, and the application of statistics as a practical theory of inquiry in integrated science, technology, engineering and mathematics (STEM) content. Her investigations contribute to identifying research-based professional development models for teachers’ integrated STEM education training. In her work to improve undergraduate students’ STEM education and experiences, Dr. Gardner investigates the impact of interventions for teaching and learning focused on increasing teaching effectiveness and on fostering quality learning environments for all students.
Gardner, K. D., Worthy, R., Glassmeyer, D. M. (2020). An Integrated STEM Professional Development Initiative for Connecting Environmental Education Across Middle and Secondary Mathematics. In Schroth, T., & Daniels, J. (Eds.), Handbook of Research on Building STEM Skills Through Environmental Education. Hershey, PA: IGI Global. https://www.igi-global.com/book/building-stem-skills-through-environmental/237830
Glassmeyer, D. M., Smith, A., Gardner, K. D. (2020). Developing Teacher Content Knowledge by Integrating pH and Logarithms Concepts. School Science and Mathematics, vol. 120, pp.165-174. DOI: 10.1111/ssm.12394
Gardner, K., Glassmeyer, D., Worthy, R. (2019). Impacts of STEM Professional Development on Teachers' Knowledge, Self-Efficacy, and Practice. Frontiers in Education (4). DOI: 10.3389/feduc.2019.00026. https://www.frontiersin.org/article/10.3389/feduc.2019.00026
Clarke, D., Strømskag, H., Johnson, H. L., Bikner - Ahsbahs, A., Gardner, K. D. (2014). Mathematical Tasks and the Student. In Liljedahl,P, Nicol, D., & Allan, D. (Ed.), Proceedings of the 38th Conference of the International Group for the Psychology of Mathematics Education (38th ed., vol. 1, pp. 30).
Gardner, K. D. (2013). Applying the phenomenographic approach to students’ conceptions of tasks. Proceedings of the International Commission on Mathematics Instruction Study 22: Task Design in Mathematics Education (1st ed., vol. 22, pp. 195-204). Oxford, England.
Gardner, K. D. (2013). A data generating review that bops, twists and pulls at misconceptions. Teaching Statistics/Blackwell Publishing, 35(1), 8-13, https://onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9639.2012.00522.x
Gardner, K., Edenfield, K., Sanchez, W., Lischka, A., Rimpola, R. & Gammill, R. (2011). State Conference Presenters’ Conceptions of Reform in Mathematics. Proceedings of the 33nd annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp.1286 – 1294). Reno, NV.
Gardner, K. (2010). Investigating Secondary Students' Experiences of Statistics. In Brosnan, P., Erchick, D. B., & Flevares, L. (Eds.). Proceedings of the 32nd annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education: Optimizing Student Understanding in Mathematics Columbus, OH (678 – 684): The Ohio State University.
Gardner, K. (2010). A Qualitative Framework for Evaluating Learning Outcomes. In Copeland, S. (Editor). Proceedings of the 40th annual meeting of the International Society of Exploring Teaching and Learning (ISETL). https://www.isetl.org/wp-content/uploads/2018/11/ISETL10Proceedings.pdf
Thomas, C., Williams, D., & Gardner, K. (2008). Performance-based mathematics instruction: An investigation of urban school mathematics teachers’ knowledge. Proceedings of the 10th International Conference on Education. Education Research Unit of the Athens Institute for Education and Research. Athens, Greece.
Thomas, C., Williams, D., & Gardner, K. (2007). An examination of teacher-designed mathematical tasks for urban learners. In Lamberg, T & Wiest, L. (Eds.), (vol. 29). Proceedings of the 29th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. Lake Tahoe, Nevada: North American Chapter of the International Group for the Psychology of Mathematics Education.
Thomas, C., Williams, D., Gardner, K. (2007). Designing performance-based mathematics tasks for urban learners. Proceedings of the 5th Annual Hawaii International Conference on Education. Honolulu, HI.
PhD: Statistics, The George Washington University
Research Interests: Nonparametric Regression, Bayesian Analysis, Survival Analysis, Competing Risk Model, Item Response Theory and Longitudinal Data Analysis.
Mohammed Chowdhury: 157 Question-Answer format problems in R. DOI: 10.13140/2.1.1175.8401. Jan, 2015.
Hafiz Khan, Mohammed Chowdhury, Aamrin Rafiq, Agam Shah, Mohammad S Zahangir,
and Muni Rubens: COVID-19 Epidemic Models: A Study from Georgia State in the USA; American Journal of Biomedical Science and Research.
Chowdhury, Mohammed: Book Review: Nonparametric Models for Longitudinal Data-with implementation in R-by Colin Wu and Xin Tian. Biometrics, 2020.
Chowdhury, Mohammed (2017): Nonparametric estimation of time-variant parametric models with application to cross-sectional data - J Japan Statist. Soc., 47(2):197-220.
Chowdhury, Mohammed; Wu, Colin; Modarres, Reza (2018): Nonparametric estimation of conditional distribution functions with longitudinal data and time-varying parametric models - Metrika, 81(1):61-83
Online Supplement, click here.
Chowdhury, Mohammed; Wu, Colin; Modarres, Reza (2017): Local Box-Cox Transformation on Time Varying Parametric models for Smoothing Estimation of Conditional CDF with Longitudinal Data. Journal of Statistical Computation and Simulation.
Chowdhury, Mohammed; VanBrackle, Lewis; Patwary (2017): Two-step smoothing estimation of the time-variant parameter with application to temperature data. Journal of Iranian Statistical Society, 16(2):33-50.
Laucis, Nicholas; Chowdhury, Mohammed; Dasgupta, Abhijit; Bhattacharyya, Timothy (2016): Trend Toward High-Volume Hospitals and the Influence on Complications in Knee and Hip Arthroplasty. J Bone Joint Surg Am, 98(9):707-712.
Chowdhury, Mohammed; Umbach, Dale (2012): Some Bayesian Analyses of Fecundability. Pakistan Journal of Statistics 28(3).
Islam, S.M.S; Karim, M.A; Chowdhury, Mohammed; Bhuiyan, M & Morshed, M: Level, Trends and Differentials of Fecundability among Bangladeshi Women:-2005-3(2):281-288, Pakistan Journal of Social Sciences.
Islam, S.M.S; Karim, M.A; Chowdhury, Mohammed; Hossain, M.S: Fitting and Estimation of Type-I Geometric Distribution by Using C-Language:-2005-4(1):112-126, Asian Journal of Information Technology.
Karim, M.A; Chowdhury, Mohammed; Hossain, M.S & Nahar, Z: Urban-Rural Differentials of Contraceptive Use in Bangladesh. 2004-28(1):77-87, The Chittagong University Journal of Science.
Karim, M.A; Hossain, M.S & Chowdhury, Mohammed: Timing of First Birth in Bangladesh and Some Socio-Demographic Factors: A Hazard Model Analysis. 2004-28(1):17-26, The Chittagong University Journal of Science.
Karim, M.A; Hossain, M.S & Chowdhury, Mohammed: Current Contraceptive Use among the Young and Low Parity Women: Levels, Trends and Differentials 2002-26(1&2): 63-73, The Chittagong University Journal of Science.
Pollard, Kylah; Chowdhury, Mohammed; Bauguess, Sarai (2017:) Prevalence and Incidence of Health Risk Factors Among Adolescent Girls. The Kennesaw Journal of Undergraduate Research, Volume 5, Issue 3, Article 1.
PhD: Biostatistics, University of Louisville
Research Interests: Dr. Ferguson uses her training in biostatistics to conduct research medical research. Her early research focused on developing methods for estimating nonparametric multistate models for truncated and censored data and applying new and existing methods to real medical data. Her research is now focused on preterm infant growth and clinical epidemiology.
Ingram, K. H. (Principal), Amason, J. S. (Supporting), Kliszczewicz, B. M. (Supporting), Ferguson, A. N. (Supporting), Grant, "Risk for Gestational Diabetes: A Condition of Abdominal Fatness or Sedentariness?", Sponsored by NIH, Federal, $406,255.00, Currently Under Review. (August 1, 2020 - Present).
Ferguson, A. N. (Principal), Olsen, I. E. (Supporting), Grabich, S., Grant, "Determining what values in growth curves best classify small and large-for-gestational age in preterm infants to predict morbidity and mortality", Sponsored by Gerber Foundation, Private, $334,233.00, Funded. (January 1, 2020 - April 2023).
Differential classification of infants in United States neonatal intensive care units for weight, length, and head circumference by United States and international growth curves (2020), A Nicole Ferguson1, Irene E Olsen 2, Reese H Clark 3, Bryan D Yockey 1, Jonathan Boardman 1, Kyle Biron 1, Cooper Jannuzzo 1, Daniel Waskiewicz 1, Amanda Mendoza 1, M Louise Lawson 1, Ann Hum Biol. 2020 Sep;47(6):564-571. doi: 10.1080/03014460.2020.1817555. Epub 2020 Sep 18.
BMI is a better body proportionality measure than the ponderal index and weight-for-length for preterm infants (2018), Ferguson A.N.a Grabich S.C.a · Olsen I.E.b · Cantrell R.a · Clark R.H.c · Ballew W.N.a · Chou J.a · Lawson M.L.a, Neonatology 2018;113:108–116. Doi: 10.1159/000480118.
MELDEQ: An alternative Model for End‐Stage Liver Disease score for patients with hepatocellular carcinoma (2015), Michael R Marvin1, Nicole Ferguson, Robert M Cannon, Christopher M Jones, Guy N Brock, Liver Transpl. 2015 May;21(5):612-22. doi: 10.1002/lt.24098. Epub 2015 Apr 15.
BMI curves for preterm infants (2015), Irene E Olsen1, M Louise Lawson 2, A Nicole Ferguson 3, Rebecca Cantrell 3, Shannon C Grabich 3, Babette S Zemel 4, Reese H Clark 5, Pediatrics. 2015 Mar;135(3):e572-81. doi: 10.1542/peds.2014-2777. Epub 2015 Feb 16.
msSurv: An R package for nonparametric estimation of multistate models (2012), Nicole Ferguson, Somnath Datta, Guy Brock, Journal of Statistical Software September 2012, Volume 50, Issue 14. DOI: 8637/jss.v050.i14
Ramazan S. Aygun
PhD: Computer Science and Engineering, State University of New York at Buffalo
Research Interests: By positioning data at the core of my research studies, data science, data mining, data modeling, data communications, data compression, data presentation, data retrieval, data indexing, data querying, and data fusion have been different aspects of my data science research. I have performed research on protein crystallization analysis, bioinformatics/biochemistry, data mining, machine learning, computer vision, image & video processing, information retrieval, spatio-temporal indexing & querying, multimedia synchronization, and multimedia databases. I have published or presented over 100 refereed international journal/conference/workshop papers and book chapters in various aspects of data science.
T. X. Tran and R. S. Aygun, “WisdomNet: trustable machine learning toward error-free classification,” Neural Comput. Appl., Jul. 2020, doi: 10.1007/s00521-020-05147-4.
T. X. Tran, M. L. Pusey, and R. S. Aygun, “Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images,” J. Fluoresc., vol. 30, pp. 637–656, 2020.
M. Shrestha, T. X. Tran, B. Bhattarai, M. L. Pusey, and R. S. Aygun, “Schema Matching and Data Integration with Consistent Naming on Protein Crystallization Screens,” IEEE/ACM Trans. Comput. Biol. Bioinform., 2019.
K. M. Paramkusem and R. S. Aygun, “Classifying Categories of SCADA Attacks in a Big Data Framework,” Ann. Data Sci., vol. 5, no. 3, pp. 359–386, 2018.
R. Aygun and W. Benesova, “Multimedia Retrieval that Works,” in 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), Apr. 2018, pp. 63–68, doi: 10.1109/MIPR.2018.00019.
N. Henderson and R. Aygun, “Human Action Classification Using Temporal Slicing for Deep Convolutional Neural Networks,” in 2017 IEEE International Symposium on Multimedia (ISM), Dec. 2017, pp. 83–90, doi: 10.1109/ISM.2017.22.
S. Dinc, F. Fahimi, and R. Aygun, “Mirage: an O (n) time analytical solution to 3D camera pose estimation with multi-camera support,” Robotica, pp. 1–19, 2017.
M. L. Pusey and R. S. Aygün, Data Analytics for Protein Crystallization. Springer International Publishing, 2017.
T. Tuna et al., “User characterization for online social networks,” Soc. Netw. Anal. Min., vol. 6, no. 1, p. 104, Dec. 2016, doi: 10.1007/s13278-016-0412-3.
M. S. Sigdel, M. Sigdel, S. Dinç, I. Dinc, M. L. Pusey, and R. S. Aygün, “FocusALL: Focal Stacking of Microscopic Images Using Modified Harris Corner Response Measure,” IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 13, no. 2, pp. 326–340, Mar. 2016, doi: 10.1109/TCBB.2015.2459685.
Sherrill W. Hayes
PhD: Sociology and Social Policy, Newcastle University (UK)
Research Interests: I use quantitative and qualitative research methods to study the impacts of policies and practices on children, families, and professionals. This has included studies of family mediators, parenting coordinators, refugee adolescent identity, intercultural parenting practices, and a wide variety of program evaluation research. More recently I have been interested in the social implications of data science and analytics, especially work-related stress and burnout among technology workers
Wood, B., Guimaraes, A.B., Holm, C.E., Hayes S. W., & Brooks, K.R. (2020). Academic Librarian Burnout: A Survey Using the Copenhagen Burnout Inventory (CBI). Journal of Library Administration, 60(5), 512-531 https://doi.org/10.1080/01930826.2020.1729622
Hayes, S. (2020). Cautionary Ethics Tales: Phrenology, Eugenics...and Data Science? In B. Franks (Ed.) 97 Things About Ethics Everyone in Data Science Should Know. (p. 9-12). Sebastopol, CA, O’Reilly Media. ISBN: 9781492072638, 149207263X
Hayes, S. W., & Endale, E. (2018). Sometimes my mind, it has to analyze two things: Identity development and adaptation for refugee and newcomer adolescents. Peace and Conflict: Journal of Peace Psychology, 24(3), 283-290. http://dx.doi.org/10.1037/pac0000315
Hayes, S. (2017). Changing radicalization to resilience by understanding marginalization. Peace Review: A Journal of Social Justice, 29(2), 153-159. doi: 10.1080/10402659.2017.1308190
Hayes, S., Grady, M., & Brantley, H. (2012). Emails, Statutes, & Personality Disorders: A survey of the processes, interventions, and perspectives of parenting coordinators. Family Court Review, 50(3), 429-440. https://doi.org/10.1111/j.1744-1617.2012.01458.x
Hayes, S. (2010). More of a street cop than a detective: An analysis of the roles and functions of parenting coordinators in North Carolina. Family Court Review, 48 (4), 698-709. https://doi.org/10.1111/j.1744-1617.2010.01343.x
Victor E. Kane
PhD: Statistics, Florida State University
Research Interests: I have a background in automotive manufacturing where I served in quality and senior management positions at Ford Motor Company. In these assignments, I used a data-based, statistical approach to problem solving and mentored many manufacturing engineers in Six Sigma Problem Solving. The goal of Six Sigma is to improve business operating performance in any targeted area. I have written three research papers in the area of Six Sigma Measurement Systems Analysis using various design of experiments methods. While teaching Six Sigma Problem Solving at KSU, I developed a deductive approach to teaching Six Sigma problem solving methodology. Recently, I published a paper on specifying the Lean Six Sigma assumptions required for all improvement projects. Currently, I am working on developing a framework for finding process improvement opportunities using Lean Six Sigma methodology.
Kane, V.E. (2020). Using Lean Six Sigma Implied Assumptions, Total Quality Management Journal, 32(6), 1561-1575. https://doi.org/10.1108/TQM-11-2019-0271
Kane, V. E. (2016). Low Resource Gage Screening, Quality Management Journal, 23(3), 6-18. https://search.proquest.com/docview/1806223220?fromopenview=true&pq-origsite=gscholar
Kane, V.E. (2014). Gage Triage Analysis for MSA Studies, Six Sigma Forum, 13(2), 5-17. https://search.proquest.com/docview/1505315231?pq-origsite=gscholar&fromopenview=true
Kane, V.E. and VanBrackle, L. (2011). One-Step Assessment of Measurement System Variation in MSA Studies, Six Sigma Forum, 10, 6-19 (Featured Cover Article). https://search.proquest.com/docview/873044023?pq-origsite=gscholar&fromopenview=true
Kane, V.E. (2007). Mistake Proofing in Assembly. Six Sigma Forum Magazine, 6, 17-25 (Featured Cover Article). https://search.proquest.com/openview/bad529e5791ddc9aae6a563e8c606c50/1?pq-origsite=gscholar&cbl=25781
Kane, V. E. (1986). “Process Capability Indices”. Journal of Quality Technology 18, 41-52. https://doi.org/10.1080/00224065.1986.11978984
PhD: Biostatistics, University of Alabama at Birmingham
Research Interests: My research focuses on two major areas, the first of which majorly studies statistical methodology for analyzing cancer genomics and metagenomics data. In this area, my research interests include: (1) develop and apply Bayesian statistical methods for cancer survival prediction with high dimensional genomics by incorporating systems biology or computational biology with a published R package BhGLM in this area; (2) Microbiome/Metagenomics Data Analysis: applying existing methods and developing Bayesian over-dispersed and zero-inflated models for microbiome association studies, with an R package NBZIMM published in this area. My second area of research consists of extensive collaborative research on various medical and public health related topics.
Zhang, X; Yi, N. NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis. Oct 2020. BMC Bioinformatics. DOI: 10.1186/s12859-020-03803-z
Zheng, H; Song Q; Zhang, C; Sun, W; Mao, M; Zhang, X; Zhu, X; Ma, G; Mao, D. The effect of text-based math task on dynamic stability control during stair descent. Oct 2020.https://doi.org/10.1016/j.jbiomech.2020.110088
Zhang, X; Li, B.; Han, H.; Song, S.; Xu, H.; Yi, Z.; Yi, N. Pathway-structured predictive modeling for multi-level drug response in multiple myeloma. Dec 2018. Bioinformatics, 34(21), 3609-3615.
Zhang, X; Li, B.; Han, H.; Song, S.; Xu, H.; Hong, Y.; Zhuang, W. Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression. Dec 2018. BMC cancer, 18(1), 551.
Zhang, X; Pei, Y. F.; Zhang, L.; Guo, B.; Pendegraft, A.; Zhuang, W.; Yi, N. Negative Binomial Mixed Models for Analyzing Longitudinal Microbiome Data. July 2018. Frontiers in microbiology, 9, 1683.
Yi, N.; Tang, Z.; Zhang, X; Guo, B. BhGLM: Bayesian hierarchical GLMs and survival models, with applications to Genomics and Epidemiology. Sep 2018. Bioinformatics.
Tang, Z; Shen, Y; Li, Y; Zhang, X; Yi, N. Group Spike-and-Slab Lasso Generalized Linear Models for Disease Prediction and Associated Genes Detection by Incorporating Pathway Information. Bioinformatics. Oct 2017; DOI 10.1093/bioinformatics/btx684.
Zhang, X; Li, Y; Akinyemiju, T; Ojesina, A; Xu, B; Yi, N. Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach. Genetics Early online Nov 2016; DOI: 10.1534/genetics.116.189191
Tian, S; Zhang, X; Jiang, R; Pillai, R; Owonikoko, T; Steuer, C; Saba, N; Pakkala, S; Patel, P; Belani, C; Khuri, F; Curran, W; Ramalingam, S; Behera, M; Higgins, K. Survival Outcomes with Thoracic Radiotherapy in Extensive-Stage Small Cell Lung Cancer: A Propensity-Score Matched Analysis of the National Cancer Data Base. May 2019. Clinical Lung Cancer. DOI:10.1016/j.cllc.2019.06.014
Cassidy, R; Zhang, X; Switchenko, J; Patel, P; Shelton, J; Tian, S; Nanda, R; Steuer, C; Pillai,R; Owonikoko, T; Ramalingam, S; Fernandez, F; Force, S; Gillespie, T; Curran, W; Higgins,K. Health care disparities among octogenarians and nonagenarians with stage III lung cancer: Elderly Patients With Stage III Lung Cancer. Cancer. Jan 2018; DOI:10.1002/cncr.31077.
PhD: Business Administration (Marketing), Penn State University
Research Interests: My research generally focuses on understanding and predicting consumer behavior through quantitative model with particular interests in market segmentation, brand positioning, customer relationship management and digital marketing. Most recently, my research focuses on how Artificial Intelligence and Big Data can help business improve their practices.
Du, K., Huddart, S., Xue, L., & Zhang, Y. (2020). Using a hidden Markov model to measure earnings quality. Journal of Accounting and Economics, 69(2-3), 101281. https://doi.org/10.1016/j.jacceco.2019.101281
PhD: Biomedical Data Science and Informatics, Clemson University-Medical University of South Carolina (joint)
Research Interests: My research interests lie in the area of healthcare data science, focusing on its application in medical imaging, clinical text, and clinically-oriented speech. I spent my early career developing a variety of clinical decision support tools to assist with combat medics on battlefield and radiologists in cancer clinics. My broader research agenda includes scalable healthcare analytics aggregating extracted information from all possible data sources.
Woo M, Devane AM, Lowe SC, Lowther EL, Gimbel RW. Deep learning for semi-automated unidirectional measurement of lung tumor size in CT. Cancer Imaging. 2021 Jun 23;21(1):43. https://pubmed.ncbi.nlm.nih.gov/34162439/
Woo M, Heo M, Devane AM, Lowe SC, Gimbel RW. Retrospective comparison of approaches to evaluating inter-observer variability in CT tumour measurements in an academic health centre. BMJ Open. 2020 Nov 14;10(11):e040096. https://pubmed.ncbi.nlm.nih.gov/33191265/
Woo M, Lowe SC, Devane AM, Gimbel RW. Intervention to Reduce Interobserver Variability in Computed Tomographic Measurement of Cancer Lesions Among Experienced Radiologists. Current Problems in Diagnostic Radiology. 2021 May-Jun;50(3):321-327. https://pubmed.ncbi.nlm.nih.gov/32014355/