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.
Herman "Gene" Ray
PhD: Biostatistics and Data Science, University of Louisville School of Public Health and Information Sciences
Research Interests: Dr. Ray’s research interest includes methodology development for imbalanced data, clinical trial design, and the application areas of healthcare and education. He is also interested in incorporation of Real-World Data into clinical trials.
Staples*, L. L., Morgan*, T., Yockey*, B. D., Rudd*, J. M., Nicole, H., Fontana, S. J., Ray, H. E., DeMaio, J. (2021). Characterizing managing physicians by claims sequences in episodes of care. Journal of Biomedical Informatics. 117.
Paranjape, N., Staples*, L. L., Stradwick*, C. Y., Ray, H. E., Saldanha, I.J. (2021). Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19. PLoS ONE. 16(3): e0248891.
Ndembera, R., Hao, J., Fallin, R., Ray, H. E., Shah, L., Rushton, G. T. (2021). Demographic factors that influence performance on the Praxis Earth and Space Science: Content Knowledge Test, Journal of Geoscience Education, 69:4, 401-410, DOI: 10.1080/10899995.2020.1813866. (https://www.tandfonline.com/doi/abs/10.1080/10899995.2020.1813866)
Rushton, G. T., Rosengrant, D., Dewar*, A., Shah, L., Ray, H. E., Sheppard, K., Watanabe, L., Watanabe. (2017). Towards a high quality high school workforce: A longitudinal, demographic analysis of U.S. public school physics teachers. Physics Review Physics Education Research, 13(2), 020112. https://journals.aps.org/prper/abstract/10.1103/PhysRevPhysEducRes.13.020122
Geisler, T., Ray, H.E., Ying, X. (2022). Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance. Journal of Classification. Under Revision.
Zhang, L., Geisler, T., Ray, H. E., Ying, X. (2021). Improving logistic regression on the imbalanced data by a novel penalized log-likelihood function. Journal of Applied Statistics. 1 – 21.
Zhang*, L., Ray, H. E., Priestley, J., Tan, S. (2019). A Descriptive Study of Variable Discretization and Cost-Sensitive Logistic Regression on Imbalanced Credit Data. Journal of Applied Statistics, 47, 568–581.
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
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.
Lauren M. Matheny
PhD: Applied Statistics and Research Methods, University of Northern Colorado
Research Interests: Dr. Matheny’s research expertise and interests are centered on improving patient-reported outcomes through modern psychometric statistical and methodological techniques including Item Response Theory and the Rasch Measurement Model, used to assess and develop data collection methods and outcome instrumentation in health outcomes research, with a special focus in orthopaedics and sports medicine.
As a methodological researcher, she is often found embedding a research study within a study, working simultaneously to not only analyze actual patient outcomes, but also improve the methods in which the data are collected and analyzed, including the instrument itself.
Dr. Matheny’s other primary research interests include data integrity assessment and survey quality control integration, health outcomes and public health study design, survey development, longitudinal study design and statistical modeling, psychometric analysis and assessment of commonly used but problematic instruments in a variety of other fields, as well as analyzing differential item functioning (DIF).
Plancher, K. D., Matheny, L. M., Briggs, K. K., & Petterson, S. C. (2022). Reliability and Validity of the Knee Injury and Osteoarthritis Outcome Score in Patients Undergoing Unicompartmental Knee Arthroplasty. The Journal of Arthroplasty, S0883-5403(22), 00475-2. Epub ahead of print. PMID: 35487406. https://doi.org/10.1016/j.arth.2022.04.026
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(4), 1276-1283. https://doi.org/10.1007/s00167-020-06069-3
Mullens, J., Stake, I. K., Matheny, L. M., Daney, B., & Clanton, T. O. (2021). Relationship between tibiotalar joint space and ankle function following ankle surgery. Foot & Ankle International, 42(3), 314-319. https://doi.org/10.1177/1071100720962490
Nott, E., Matheny, L. M., Clanton, T. O., Lockard, C., Douglass, B. W., Tanghe, K. K., Matta, N., & Brady, A. W. (2021). Accessibility and Thickness of Medial and Lateral Talar Body Cartilage for Treatment of Ankle and Foot Osteochondral Lesions. Foot & Ankle International, 42(10), 1330-1339. https://doi.org/10.1177/10711007211015189
Matheny, L. M., & Clanton, T. O. (2020). Rasch analysis of reliability and validity of scores from the foot and ankle ability measure (FAAM). Foot & Ankle International, 41(2), 229-236. https://doi.org/10.1177/1071100719884554
LaPrade, R. F., Matheny, L. M., Moulton, S. G., James, E. W., & Dean, C. S. (2017). Posterior meniscal root repairs: outcomes of an anatomic transtibial pull-out technique. The American journal of sports medicine, 45(4), 884-891. https://doi.org/10.1177/0363546516673996
Matheny, L. M., Ockuly, A. C., Steadman, J. R., & LaPrade, R. F. (2015). Posterior meniscus root tears: associated pathologies to assist as diagnostic tools. Knee Surgery, Sports Traumatology, Arthroscopy, 23(10), 3127-3131. https://doi.org/10.1007/s00167-014-3073-7
Steadman, J. R., Matheny, L. M., Singleton, S. B., Johnson, N. S., Rodkey, W. G., Crespo, B., & Briggs, K. K. (2015). Meniscus suture repair: minimum 10-year outcomes in patients younger than 40 years compared with patients 40 and older. The American journal of sports medicine, 43(9), 2222-2227. https://doi.org/10.1177/0363546515591260
Steadman, J. R., Briggs, K. K., Matheny, L. M., & Ellis, H. B. (2013). Ten-year survivorship after knee arthroscopy in patients with Kellgren-Lawrence grade 3 and grade 4 osteoarthritis of the knee. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 29(2), 220-225. https://doi.org/10.1016/j.arthro.2012.08.018
Clanton, T. O., Matheny, L. M., Jarvis, H. C., & Jeronimus, A. B. (2012). Return to play in athletes following ankle injuries. Sports Health, 4(6), 471-474. https://doi.org/10.1177/1941738112463347
Ellis, H. B., Matheny, L. M., Briggs, K. K., Pennock, A. T., & Steadman, J. R. (2012). Outcomes and revision rate after bone–patellar tendon–bone allograft versus autograft anterior cruciate ligament reconstruction in patients aged 18 years or younger with closed physes. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 28(12), 1819-1825. https://doi.org/10.1016/j.arthro.2012.06.016
Steadman, J. R., Matheny, L. M., Briggs, K. K., Rodkey, W. G., & Carreira, D. S. (2012). Outcomes following healing response in older, active patients: a primary anterior cruciate ligament repair technique. The Journal of Knee Surgery, 25(03), 255-260. https://doi.org/10.1055/s-0032-1313742
Sterett, W. I., Steadman, J. R., Huang, M. J., Matheny, L. M., & Briggs, K. K. (2010). Chondral resurfacing and high tibial osteotomy in the varus knee: survivorship analysis. The American journal of sports medicine, 38(7), 1420-1424. https://doi.org/10.1177/0363546509360403
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).
Ferguson, A.N., Olsen, I.E., Clark, R.H., Yockey, B.D., Boardman, J., Biron, K., Jannuzzo, C., Waskiewicz, D., Mendoza, A., Lawson, M.L., Hum, A., 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), Biol. Sep;47(6):564-571. doi: 10.1080/03014460.2020.1817555. Epub 2020 Sep 18.
Ferguson A.N., Grabich S.C., Olsen I.E., Cantrell R., Clark R.H., Ballew W.N., Chou J., Lawson M.L., BMI is a better body proportionality measure than the ponderal index and weight-for-length for preterm infants (2018), Neonatology; 113:108–116. Doi: 10.1159/000480118.
Marvin, M.R., Ferguson, N., Cannon, R.M., Jones, C.M., Brock, G.N., MELDEQ: An alternative Model for End‐Stage Liver Disease score for patients with hepatocellular carcinoma (2015), Liver Transpl. May;21(5):612-22. doi: 10.1002/lt.24098. Epub 2015 Apr 15.
Olsen, I.E., Lawson, M.L., Ferguson, A.N., Cantrell, R., Grabich, S.C., Zemel, B.S., Clark, R.H., BMI curves for preterm infants (2015), Pediatrics. Mar;135(3):e572-81. doi: 10.1542/peds.2014-2777. Epub 2015 Feb 16.
Ferguson, N., Datta, S., Brock, G., msSurv: An R package for nonparametric estimation of multistate models (2012), Journal of Statistical Software September 2012, Volume 50, Issue 14. DOI: 10.18637/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 of work that spans multiple disciplines including biology/genetics, geology, engineering, quality control, business and the latest process optimization. My teaching of graduate Design of Experiments reflects my interests in statistical testing and discovery focused on solving difficult problems. Some of my published works involve evaluating measurement systems for their efficacy in problem resolution. Some of the recent works focus on process optimization and implementation of lean practices to enhance efficiencies. I have consulted with a diverse list of researchers with projects focused on using analytics to uncover unknown patterns in their data.
Kane, V. E. (2022). Communicating Lean Six Sigma Success Alternatives, International Journal of Productivity and Performance Management, Submitted.
Kane, V. E. (2022). Useful Paths for Identifying Lean Six Sigma Improvement Opportunities, Journal of Quality and Reliability Management, 39(8), pp. 2058-2077. (impact factor 2.768) https://doi.org/10.1108/IJQRM-08-2020-0274
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. (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, Mishra P, Lin J, Kar S, Deas N, Linduff C, Niu S, Yang Y, McClendon J, Smith DH, Shelton SL, Gainey CE, Gerard WC, Smith MC, Griffin SF, Gimbel RW, Wang KC. Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study. JMIR Mhealth Uhealth. 2021 Oct 12;9(10):e32301. https://pubmed.ncbi.nlm.nih.gov/34636729/
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/