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PhD: Mathematics, Florida State University

Research Interests: My primary research focus is machine learning and data science, specifically focusing on network science. I am particularly interested in utilizing topological data analysis (TDA) tools to address challenges in network science, such as opinion dynamics and influence maximization problems and their applications in social, biological, and business networks. 

Selected Publications: 

" Persistence Homology of Networks: Methods and Applications", ME Aktas, E Akbas, A El Fatmaoui, Applied Network Science 4 (61)

" Identifying critical higher-order interactions in complex networks", ME Aktas, T Nguyen, S Jawaid, R Riza, E Akbas, Scientific Reports 11 (21288)

" Bot detection on social networks using persistent homology", M Nguyen, M Aktas, E Akbas, Mathematical and Computational Applications 25 (3), 58

" Proximity-based compression for network embedding", MI Islam, F Tanvir, G Johnson, E Akbas, ME Aktas, Frontiers in big Data 3, 608043

" Network embedding: on compression and learning", E Akbas, ME Aktas, 2019 IEEE International Conference on Big Data (Big Data), 4763-4772

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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.

Selected Publications:

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.

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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.

Selected Publications:

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. 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. 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. 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. 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. www.cdc.gov/mmwr/volumes/68/wr/mm6809e1.htm?s_cid=mm6809e1_w

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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.

Selected Publications:

Situational Awareness in Acute Patient Deterioration: Identifying Student Time to Task. europepmc.org/article/med/33481495

The alternative distribution of the non parametric extended median test CUSUM chart for multiple stream processes
www.tandfonline.com/doi/abs/10.1080/03610926.2020.1850792

A nonparametric CUSUM control chart for multiple stream processes based on a modified extended median test
www.tandfonline.com/doi/abs/10.1080/03610926.2020.1738492

Outlook in life of older adults and their health and community condition
www.tandfonline.com/doi/abs/10.1080/03601277.2020.1795788

The effect of a repeat septic shock simulation on the knowledge and skill performance of undergraduate nursing students 

www.sciedupress.com/journal/index.php/jnep/article/view/18354

Motivation and Postsecondary Enrollment Among High School Students Whose Parents Did Not Go to College
https://journals.sagepub.com/doi/abs/10.1177/0894845320946397

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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.

Selected Publications:

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

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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.

Grants:

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 - June 2025). 

Selected Publications:

Ferguson, A.N., Granger, M., Olsen, I. E., Clark, R. H., Woo, J. G. (2024).  Mortality Risk in US Neonatal Intensive Care Unit Infants by Birth Size Classifications Comparing Three Growth Curves.  Neonatology.

Jordan, N., Ferguson, A.N., Gittner, K., Woo, J.G., Clark, R.H. (2023). Visualizing Chronic Lung Disease Incidence in SAS; An Educational Journey to Data Visualization, SouthEast SAS User Group 2023 Conference Proceedings.

Olsen, I.E., Granger, M., Masoud, W., Clark, R.H., and Ferguson, A.N. (2023). Defining Body Mass Index Using Weight and Length for Gestational Age in the Growth Assessment of Preterm Infants at Birth. American Journal of Perinatology.

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

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 envionments for all students.

Selected Publications:

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. 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, 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). 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.

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PhD: Statistics, Washington University, St. Louis

Research Interests: My research interests lie at the intersection of statistical methodology, data science, and biomedical applications, with a focus on time series analysis, longitudinal data analysis, and causal inference in high-dimensional settings. In time series, I have worked on polyspectral mean estimation and nonlinearity assessment methods, proposed quadratic forecasting to improve on linear approaches, and designed clustering algorithms for financial markets. In longitudinal analysis, my work includes rank-based tests for multi-arm trials, power and sample size methods for longitudinal rank-sum tests, and ensemble survival models for early Alzheimer's prediction. I have also analyzed social media data, building predictive models of electoral campaigns and developing static-time bot detection algorithms. Among my current projects is the identification of causal brain regions linked to neurodegenerative diseases by integrating MRI voxel intensities, genetic instruments, and network information. Additionally, I have contributed to bootstrap theory for extremes and extreme value analysis of global air quality.

Selected Publications:  

McElroy, T. S., Ghosh, D., & Lahiri, S. (2024). Quadratic prediction of time series via auto-cumulants. Sankhya A, 86(1), 431-463.

Xu, X., Ghosh, D., Luo, S., & CPP Integrated Parkinson’s Database. (2025). A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials: Applications to neurodegenerative disorders. Statistics in Biopharmaceutical Research, 1-11.

Ghosh, D., & Luo, S. (2025). A non-parametric U-statistic testing approach for multi-arm clinical trials with multivariate longitudinal data. Journal of Multivariate Analysis, 105447.

Ghosh, D., Boettcher, W. A., Johnston, R., & Lahiri, S. (2025). THANOS: A Predictive Model of Electoral Campaigns Using Twitter Data and Opinion Polls. Data Science in Science, 4(1), 2484180.

Sotoudeh‐Paima, S., Segars, W. P., Ghosh, D., Luo, S., Samei, E., & Abadi, E. (2024). A systematic assessment and optimization of photon‐counting CT for lung density quantifications. Medical physics, 51(4), 2893-2904.

Tushar, Fakrul Islam, et al. "Virtual lung screening trial (VLST): An in silico study inspired by the national lung screening trial for lung cancer detection." Medical Image Analysis 103 (2025): 103576.

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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.

Selected Publications: 

Swahn, M. H., Gittner, K. B., Lyons, M. J., Nielsen, K., Mobley, K., Culbreth, R., Palmier, J., Johnson, N. E., Matte, M., & Nabulya, A. (2024). Advancing mHealth Research in Low-Resource Settings: Young Women’s Insights and Implementation Challenges with Wearable Smartwatch Devices in Uganda. Sensors, 24(17), 5591. https://doi.org/10.3390/s24175591

Gittner, K. B., Gittner, L. S., Dennis, J. A., & Matheny, L. M. (2022). VetStar: Justice-involved veterans successful mental health diversion. Journal of Veterans Studies, 8(1), 1-14.

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.

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PhD: Electrical Engineering, Florida State University

Research Interests: My research lies at the intersection of artificial intelligence (AI), medical imaging, and healthcare analytics. I focus on developing deep learning and computer vision methods for cancer detection, medical image segmentation, and clinical decision support. Recently, my work has expanded into vision-language models (VLMs) and multimodal learning, integrating imaging with clinical and textual data to enhance diagnostic accuracy and interpretability. I am also interested in the scalable deployment of AI models for real-world healthcare applications.

Selected Publications: 

Imran, M., Krebs, J. R., Sivaraman, V. B., Zhang, T., Kumar, A., Ueland, W. R., Fassler, M. J., Huang, J., Sun, X., Wang, L., Shi, P., Rokuss, M., Baumgartner, M., Kirchhof, Y., Maier-Hein, K. H., Isensee, F., Liu, S., Han, B., Nguyen, B. T., Shin, D., Ji-Woo, P., … Shao, W. (2025). Multi-class segmentation of aortic branches and zones in computed tomography angiography: The AortaSeg24 Challenge. arXiv preprint arXiv:2502.05330. https://doi.org/10.48550/arXiv.2502.05330

Imran, M., Krebs, J. R., Gopu, V. R. R., Fazzone, B., Sivaraman, V. B., Kumar, A., Viscardi, C., Heithaus, R. E., Shickel, B., Zhou, Y., Cooper, M. A., & Shao, W. (2024). CIS-UNet: Multi-class segmentation of the aorta in computed tomography angiography via context-aware shifted window self-attention. Computerized Medical Imaging and Graphics, 118, 102470. https://doi.org/10.1016/j.compmedimag.2024.102470

Imran, M., Nguyen, B., Pensa, J., Falzarano, S. M., Sisk, A. E., Liang, M., DiBianco, J. M., Su, L. M., Zhou, Y., Joseph, J. P., Brisbane, W. G., & Shao, W. (2024). Image registration of in vivo micro-ultrasound and ex vivo pseudo-whole mount histopathology images of the prostate: A proof-of-concept study. Biomedical Signal Processing and Control, 96(Part B), 106657. https://doi.org/10.1016/j.bspc.2024.106657

Imran, M., Haq, B., Elbasi, E., Topcu, A. E., & Shao, W. (2024). Transformer-based hierarchical model for non-small cell lung cancer detection and classification. IEEE Access, 12, 145920–145933. https://doi.org/10.1109/ACCESS.2024.3449230

Fatima, S., Imran, M., Ullah, A., Arif, M., & Noor, R. (2022). A unified technique for entropy enhancement–based diabetic retinopathy detection using hybrid neural network. Computers in Biology and Medicine, 145, 105424. https://doi.org/10.1016/j.compbiomed.2022.105424

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PhD: Environmental Engineering, Vanderbilt University

Research Interests: My research aims to integrate statistical, economic, and agent-based modeling to analyze decision-making under uncertainty in risk-related applications. In particular, my research focuses on quantifying resilience to natural hazards, past and projected, and using Bayesian approaches to better assess investments in climate change adaptation and mitigation.

Publications:

Johnson, Paul M.; Baroud, Hiba; Philip, Craig; Abkowitz, Mark. (2022). An integrated approach to evaluating inland waterway disruptions using economic interdependence, agent-based, and Bayesian models. The Engineering Economist

Johnson, Paul M.; Barbour, William; Camp, Janey V.; Baroud, Hiba. (2022). Using machine learning to examine freight network spatial vulnerabilities to disasters: A new take on partial dependence plots. Transportation Research Interdisciplinary Perspectives

Johnson, Paul M.; Brady, Corey E.; Philip, Craig; Baroud, Hiba; Camp, Janey V.; Abkowitz, Mark. (2020). A Factor Analysis Approach Toward Reconciling Community Vulnerability and Resilience Indices for Natural Hazards. Risk Analysis

Johnson, Paul M.; Bennartz, Ralf; Camp, Janey V. (2019). Using machine learning to quantify the impacts of genetically modified crops on US midwest corn yields. Applied Geography

Philip, Craig E.; Johnson, Paul. (2018). The Maritime Safety Journey: An unlikely and remarkable story. Marine News

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PhD:  Information Systems, University of Maryland, Baltimore County (UMBC)

Research Interests: User-generated and machine-generated content from AI, the web, and social media provides a constant stream of information on human and machine behavior that can be monitored for different topics. This data includes, but is not limited to, AI prompts and responses (e.g., ChatGPT), social media (e.g., Twitter), research search engines (e.g., PubMed), online reviews (e.g., Amazon), and forum discussions (e.g., Reddit). In particular, I collect large datasets, design and develop data analysis frameworks, and utilize mixed methods, including both computational (quantitative) and qualitative approaches, to address important social, technological, and health issues. I have been collaborating internally with researchers in different fields and externally with researchers at other universities to develop my research projects. Through this work, I have contributed to the data science, computational social science, information science, information systems, and computer science disciplines as well as to health, psychology, politics, and communications. 

Selected Publications: 

Mohajeri, K., & Karami, A. (2025). Computationally Intensive Research: Advancing a Role for Secondary Analysis of Qualitative Data. Journal of the Association for Information Systems, 26(3), 832-849. https://aisel.aisnet.org/jais/vol26/iss3/3/

Karami, A., Qiao, Z., Zhang, X., Kharrazi, H., Bozorgi, P., & Bozorgi, A. (2024). Health Use Cases of AI Chatbots: Identification and Analysis of ChatGPT Prompts in Social Media Discourses. Big Data and Cognitive Computing, 8(10), 130. https://doi.org/10.3390/bdcc8100130

Mohammadi, E., & Karami, A. (2022). Exploring research trends in big data across disciplines: A text mining analysis. Journal of Information Science, 48(1), 44-56. https://doi.org/10.1177/0165551520932855

Karami, A., Clark, S. B., Mackenzie, A., Lee, D., Zhu, M., Boyajieff, H. R., & Goldschmidt, B. (2022). 2020 US presidential election in swing states: Gender differences in Twitter conversations. International Journal of Information Management Data Insights, 2(2), 100097. https://doi.org/10.1016/j.jjimei.2022.100097

Bozorgi, P., Porter, D. E., Eberth, J. M., Eidson, J. P., & Karami, A. (2021). The leading neighborhood-level predictors of drug overdose: a mixed machine learning and spatial approach. Drug and alcohol dependence, 229, 109143. https://doi.org/10.1016/j.drugalcdep.2021.109143

Karami, A., Kadari, R. R., Panati, L., Nooli, S. P., Bheemreddy, H., & Bozorgi, P. (2021). Analysis of geotagging behavior: Do geotagged users represent the Twitter population?. ISPRS International Journal of Geo-Information, 10(6), 373. https://doi.org/10.3390/ijgi10060373

Karami, A., White, C. N., Ford, K., Swan, S., & Spinel, M. Y. (2020). Unwanted advances in higher education: Uncovering sexual harassment experiences in academia with text mining. Information Processing & Management, 57(2), 102167. https://doi.org/10.1016/j.ipm.2019.102167

Shin, G., Jarrahi, M. H., Fei, Y., Karami, A., Gafinowitz, N., Byun, A., & Lu, X. (2019). Wearable activity trackers, accuracy, adoption, acceptance and health impact: A systematic literature review. Journal of biomedical informatics, 93, 103153. https://doi.org/10.1016/j.jbi.2019.103153

Karami, A., Gangopadhyay, A., Zhou, B., & Kharrazi, H. (2018). Fuzzy approach topic discovery in health and medical corpora. International Journal of Fuzzy Systems, 20(4), 1334-1345.  https://doi.org/10.1007/s40815-017-0327-9

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PhD: Computational Mathematics, University of North Carolina at Greensboro

Research Interests: My research interests include statistical genetics, high-dimensional data analysis, robust statistics, and deep neural networks, with a strong focus on practical applications in genomics and cancer research. I have developed robust statistical methods to address the challenges of complex high-dimensional data, including data contamination, heteroscedasticity, and structured sparsity. My work extends to advanced methodologies for outlier detection and variable selection, which I actively apply in cancer clinical trials to identify predictive biomarkers and enhance prognostic models. Moving forward, I aim to integrate AI techniques into areas of high-dimensional data and robust statistics, focusing on improving real-world data analysis across various domains, particularly within the health sciences.

Selected Publications: 

Knutson, T.P., Luo, B., Kobilka, A. et al. (2024). AR alterations inform circulating tumor DNA detection in metastatic castration resistant prostate cancer patients. Nat Commun 15, 10648. https://doi.org/10.1038/s41467-024-54847-1

Nixon, A. B., Liu, Y., Yang, Q., Luo, B., Starr, M. D., Brady, J. C., … & Halabi, S. (2024). Prognostic and predictive analyses of circulating plasma biomarkers in men with metastatic castration-resistant prostate cancer treated with docetaxel/prednisone with or without bevacizumab. Prostate Cancer and Prostatic Diseases, 1-8. https://doi.org/10.1038/s41391-024-00794-3

Slovin, S. F., Knudsen, K., Halabi, S., de Leeuw, R., Shafi, A., Kang, P., Wolf, S., Luo, B., … & Kelly, K. (2023). Randomized Phase II Multicenter Trial of Abiraterone Acetate With or Without Cabazitaxel in the Treatment of Metastatic Castration-Resistant Prostate Cancer. Journal of Clinical Oncology, 41(32), 5015-5024. https://doi.org/10.1200/JCO.22.02639

Halabi, S., Yang, Q., Roy, A., Luo, B., Araujo, J. C., Logothetis, C., … & Kelly, W. K. (2023). External validation of a prognostic model of overall survival in men with chemotherapy-naive metastatic castration-resistant prostate cancer. Journal of Clinical Oncology, 41(15), 2736-2746. https://doi.org/10.1200/JCO.22.02661

Luo, B., Gao, X., & Halabi, S. (2022). Penalized weighted proportional hazards model for robust variable selection and outlier detection. Statistics in Medicine, 41(17), 3398-3420. https://doi.org/10.1002/sim.9424

Luo, B., & Gao, X. (2022). High-dimensional robust approximated M-estimators for mean regression with asymmetric data. Journal of Multivariate Analysis, 192, 105080. https://doi.org/10.1016/j.jmva.2022.105080

Luo, B., & Gao, X. (2022). A high-dimensional M-estimator framework for bi-level variable selection. Annals of the Institute of Statistical Mathematics, 74(3), 559-579. https://doi.org/10.1007/s10463-021-00809-z

Peng, Y., Luo, B., & Gao, X. (2022). Robust Moderately Clipped LASSO for Simultaneous Outlier Detection and Variable Selection. Sankhya B, 84(2), 694-707. https://doi.org/10.1007/s13571-022-00279-0

Nixon, A.B., Halabi, S., Liu, Y., Starr, M.D., Brady, J.C., Shterev, I., Luo, B., … & George, D. J. (2022). Predictive biomarkers of overall survival in patients with metastatic renal cell carcinoma treated with IFNα±bevacizumab: results from CALGB 90206 (Alliance). Clinical Cancer Research, 28(13), 2771-2778. https://doi.org/10.1158/1078-0432.CCR-21-2386

Luo, B., Yang, Q., & Halabi, S. (2021). Variable selection approaches in high-dimensional space. Modern Statistical Methods for Health Research, 301-327. https://doi.org/10.1007/978-3-030-72437-5_14

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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).

Selected Publications: 

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. 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. 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. 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. 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.         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. 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. 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.           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. 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.  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. 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. doi.org/10.1177/0363546509360403

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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.

Selected Publications: 

Healthcare
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.

Education
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 

Methods Development
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.

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PhD: Educational Psychology: Cognition and Development, University of Georgia

Research Interests:  Structural equation modeling, multilevel modeling, psychometrics, learning sciences 

Selected Publications:

Vaughn, A., Johnson, M., & Taasoobshirazi, G. (2020). I mpostor 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. 

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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.

Selected Publications:

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. 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