Jessica is a statistician and data scientist with experience in methodology, statistics, and machine learning. She is currently the senior manager and principal data scientist managing the data science team and directly engaging in development of advanced analytic solutions to shape business strategy at Chamberlain Group.
Jessica previously worked as a statistician in the Global Security Sciences Division at Argonne National Laboratory and at The Methodology Center within Penn State’s College of Health and Human Development. Her research included analytic methods optimizing threat assessments and countermeasure recommendations for federal facilities; modeling the dynamics of infectious disease outbreaks; and estimating the effects of control measures to inform the design of optimized interventions.
Ph.D. Statistics, The Pennsylvania State University, 2015
M.S. Statistics, Miami University, 2008
B.A. Mathematics & Statistics, Miami University, 2005
B.A. Psychology, Miami University, 2005
Kugler, K.C., Dziak, J.J., & Trail, J.B. (2018). Coding and Interpretation of Effects in Analysis of Data from a Factorial Experiment. Book Chapter in Optimization of Behavioral, Biobehavioral, and Biomedical Interventions.
Collins, L.M., Dziak, J.J., Kugler, K.C., & Trail, J.B. (2014). Factorial Experiments: Efficient Tools for Evaluation of Intervention Components. American Journal of Preventive Medicine, 47 (4): 498—504.
Collins, L.M., Trail, J.B., Kugler, K.C., Baker, T.B., Piper, M.E., & Mermelstein, R.J. (2013). Evaluating individual intervention components: making decisions based on the results of a factorial screening experiment. Translational Behavioral Medicine: Practice, Policy, and Research, 4 (3): 238—251.
Trail, J.B., Collins, L.M., Rivera, D.E., Li, R., Piper, M.E., & Baker, T.B. (2013). Functional data analysis for dynamical system identification of behavioral processes. Psychological Methods, 19 (2), 175—187.
Molloy, L.E., Moore, J., Trail, J., Van Epps, J., & Hopfer, S. (2013). Understanding real-world implementation quality and “active ingredients” of PBIS. Prevention Science, 14(6): 593-605.
Kugler, K.C., Trail, J.B., Dziak, J.J., & Collins, L.M. (2012). Effect coding versus dummy coding in analysis of data from factorial experiments (Technical Report No. 12-120). University Park: The Methodology Center, Penn State.