I am an assistant professor in the Analytics, Information, and Operations Management Area at the University of Kansas School of Business. Before joining the University of Kansas School of Business, I served as a visiting assistant professor at the University of Iowa Tippie College of Business Business Analytics Department. I received my PhD in Computer Science from the University of Iowa in the Winter of 2018 under the supervision of Drs. W. Nick Street and Alberto M. Segre.
My work has appeared in a variety of peer-reviewed journals including the Journal of Management Information Systems (JMIS), Journal of Business Research (JBR), and Expert Systems with Applications (ESWA), among others. My work has also appeared in numerous rigorously peer-reviewed conferences including the SIAM International Conference on Data Mining (SDM), the IEEE International Conference on Health Informatics (ICHI), and the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), among others.
My research interests are broadly in the areas of data mining, machine learning, and business analytics, and emphasize a decision-making perspective. To be more specific, I’ve worked on the inverse classification problem (methods that transform prediction-making machine learning models into prescription-providing models that optimize for a preferred outcome), machine learning explainability (MLX; methods that explain why a machine learning model made the prediction that it did), predicting and analyzing movie success from a pre-production disposition (the movie “green lighting” process), predicting and analyzing consumer mobility during a major socio-economoic disruption (COVID-19; to aid in business decision-making and planning), the third-party logistics-provider freight management problem using deep reinforcement learning, among others. Thus, my work ranges from methodological machine learning innovations to applied, analytics-focused works, and others that fall in between the two.