Smith Research Recognized for ‘Potential to Create Positive Societal Changes’

A dissertation by doctoral student Eunseong Jang at the University of Maryland’s Robert H. Smith School of Business has produced new statistical models to expand datasets and effectively present “law enforcement and policymakers with new tools to combat drug trafficking and disrupt illegal supply chains.”

The study, NSF-funded and in collaboration with Associate Professor of Management Science and Statistics Margret Bjarnadottir (primary advisor) and Dean's Professor of Management Science and Operations Management S. Raghu Raghavan (supervisor), has drawn a $6,000-winning prize for its “potential to create positive societal changes” in Colorado State University’s Business for a Better World Dissertation Proposal Competition.

According to the announcement of the results, Jang’s work was evaluated on, and impressed judges with, its “potential benefit to society, theoretical contribution in creating new knowledge, contribution for multiple stakeholders and application of sound research methodology.”

The announcement further summarizes Jang’s winning entry, titled “Spatial Analysis and Optimization Models for Better Understanding of Illicit Drug Supply Chains with Sparse and Incomplete Data”:

Leveraging vast amounts of data to provide industry with new insights has been a defining feature of the information age. Law enforcement organizations face an unusual challenge when attempting to apply modern data analytics to their operations: By the very nature of criminal activities, offenders work to obscure their activities, leaving holes in data sets that befuddle traditional analytic methods.

Eunsong Jang develops new statistical models to help law enforcement augment datasets and provide additional tools to fight the trafficking of cocaine. His research provides new methods to augment existing data on trafficking, including innovative approaches to dealing with sparse and occluded information on drug trafficking. “Even if we have some occurrence or observation about trafficking or by taking the seizure of cocaine, for example, it does not represent the entire nature of the supply chain of illicit drugs because drug enforcement agents might not capture all the vehicles trafficking it,” Jang said.

Building on existing data, Jang focused on disrupting trafficking in three research streams. The first improved spatial analysis for trafficking hotspots in the United States. Another used statistical regression to uncover factors in trafficking patterns in Colombia. Finally, he constructed a data optimization framework to infer cocaine flows across the US. Taken together, this research offers law enforcement and policymakers new tools to combat drug trafficking and disrupt illegal supply chains.

Jang is on track to complete his PhD in operations management and management science in 2026. His primary research interests are the narcotic supply chain and inference of trafficking networks.