Join us in welcoming Jiming Peng, an associate professor from the University of Houston, as he discusses industrial engineering topics. Alums and friends of the program are always welcome.
Quantitative Models and Techniques for Optimal Deployment of Mobile Health Clinic Service to Underserved Vulnerable Communities
Mobile health clinics are vehicles with various medical equipment and patient capacities to provide various health care services including primary care and preventive care. Due to their flexibility and cost-effectiveness, MHCs have been widely adopted to alleviate health disparities by providing affordable and reliable healthcare services to vulnerable communities. To meet the ever-growing demand for MHC services, it is critical to develop quantitative models and techniques to improve the service coverage of MHCs under limited resources. In this talk, we will review recent results in this direction from my research group. Specifically, we will discuss how to integrate multi-source data to forecast the demand for MHC services in different communities based on small-sample learning and nonconvex optimization and how to identify the optimal deployment plan for MHCs via solving the corresponding integer linear optimization with bilinear constraints.
Jiming Peng is an associate professor in the Department of Industrial Engineering at the University of Houston. He received both his bachelor’s and master’s degrees in China and a Ph.D. degree from the Delft University of Technology, the Netherlands, in 2001. After that, he worked at McMaster University in Canada and the University of Illinois at Urbana-Champaign. His research interest lies in optimization modeling, learning, algorithm design and analysis with applications to big data, healthcare and power systems. He has published a research monograph and numerous papers in the flagship journals in the optimization community, such as management science, operations research, mathematical programming etc. His research has been recognized by several awards from both the academic community and industry, and highlighted by NSF in 2018.
