Information Science & Systems
Dr. Gregory Ramsey
Office: GSBM 502
Ph.D. University of Minnesota - Information & Decision Sciences, 2010
M.B.A. Carnegie-Mellon University - Industrial Administration - Entrepreneurship, 1990
M.S.E.E. Georgia Institute of Technology - Electrical Engineering, 1982
BSEE Duke University - Electrical Engineering, 1980
Gregory Ramsey is an Associate Professor in the Information Science and Systems Department in the Earl G. Graves School of Business and Management. Prof. Ramsey received a B.S. in Electrical Engineering from Duke University, an M.S. in Electrical Engineering from Georgia Institute of Technology, an MSIA from the Tepper School of Business at Carnegie Mellon University, and a Ph.D. in Business Administration (Information and Decision Sciences) from the Carlson School of Management at the University of Minnesota. He has completed a post-doctoral fellowship at the Stern School of Business at New York University. His primary research interests include data analytics, supply chain digitalization, modeling/simulating processes, and process characterization.
data analytics; supply chain digitalization; process modeling/simulating; process characterization
Ramsey, G. (2019). Simulation, Selection, and Mechanical Turk: Can Cases Presented Online Help Us Learn About Shared Decision-making and Medical Malpractice?. Annals of Emergency Medicine., doi: https://doi.org/10.1016/j.annemergmed.2019.03.003.
Ramsey, G., & Bapna, S. (2019). Predicting Patient Turnover: Lessons from Predicting Customer Churn Using Free-Form Call Center Notes. In Kwok Tai Chui, Miltiadis D. Lytras (Ed.), Computational Methods and Algorithms for Medicine and Optimized Clinical Practice (pp. 108-132). IGI Global.
Wells, A., Dennis, S., Kichner, S., Vakalahi, H., Ramsey, G., Marcus Pollock, Randolph Rowel (2019). Closing the Community-Academia Gap to Move Baltimore City to New Heights. In Press, In Greater Baltimore Urban League (Ed.), State of Black Baltimore 2019.
Ramsey, G. W. (2014). Evaluating Policies using Agent-based Simulations: Investigating Policies for Continuity of Care. International Journal of Simulation and Process Modelling, 9 (4), 255-269.
Ramsey, G. & Bapna, S. (2014). A Technique to Exploit Free-Form Notes to Predict Customer Churn. International Journal of Computational Models and Algorithms in Medicine, 4 (4), 16.
Ramsey, G. & Bapna, S. (2014). A European Loyalty Program: Examining Purchase Behavior to Predict Likelihood of Retaining Individual Customers. International Journal of Business and Commerce, 3 (9), 27-35.
Ramsey, G. W., Johnson, P. E., O'Connor, P. J., Sperl-Hillen, J. M., Rush, W. A., George Biltz (2014). Examining Failure in a Dynamic Decision Environment: Strategies for Treating Patients with a Chronic Disease. Annals of Information Systems, 19, 1-15.
Ramsey, G., Johnson, P., O'Connor, P., Sperl-Hillen, J., Rush, W., George Biltz, (2010). Identifying Physician Decision Strategies for Treating Patients with Type 2 Diabetes. American Diabetes Association 70th Scientific Session, Orlando, Florida.
Ramsey, G., Johnson, P., O'Connor, P., Sperl-Hillen, J., Rush, W., George Biltz, (2010). Computational Models for Investigating Success and Failure in Treating Patients with Type 2 Diabetes. 5th INFORMS Workshop on Data Mining and Health Informatics, Arlington, Virginia.
Ramsey, G., Johnson, P., O'Connor, P., Sperl-Hillen, J., & Rush, W. (2010). Using Functional Data Analysis to Identify Physician Decision Strategies which Lead to Better Type 2 Diabetes Patient Outcomes. 1st ACM International Conference on Health Informatics, Austin, Texas.
McCabe, R., Adomavicius, G., Johnson, P., Ramsey, G., Rund, E., William Rush, Patrick O'Connor, JoAnn Sperl-Hillen, (2008). "Using Data Mining to Predict Errors in Chronic Disease Care", Advances in Patient Safety: New Directions and Alternative Approaches. Rockville: Agency for Healthcare Research and Quality.