Dr. Medal is the co-PI of a grant titled “Machine-Learning-Enabled Modeling for High-Dimensional Dynamics of Materials Processes,” and funded by the StART seed funding program organized by the Science Alliance at the University of Tennessee (https://scialli.utk.edu/)
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I have done a bit of work on optimizing the design of transportation networks, mostly under the theme of usability. In a project funded by the U.S. Department of Transportation, we examined how to optimally retrofit a transportation network to make it more pedestrian-friendly (Rashidi et al., 2016). To accomplish this, we formulated an optimization model for optimally allocating limited resources to different traffic calming actions such as adding sidewalks and crosswalks. In another paper, we developed a model for designing a transportation network to make it more accessible to pedestrians (Li, Medal, and Qu, 2019).
Bhuiyan, T. H., Nandi, A. K., Medal, H., and Halappanavar, M. (2017) Risk-averse bi-level Stochastic Network interdiction model for Cyber-security. Technical report, Mississippi State University, Starkville, MS.
Gedik, R., Medal, H., Rainwater, C., Pohl, E., and Mason, S. (2014) Vulnerability assessment and re-routing of freight trains under disruptions: A coal supply chain network application. Transportation Research Part E, 71, 45–57.
Medal Research Group
The University of Tennessee, Knoxville
Industrial and Systems Engineering
518 John D. Tickle Building