Research Findings/Project Archive, Academic History, and Presentations/Publications Archive for Kiel Corkran

Hello all, and welcome to Kiel's  archive! 

Introduction to Kiel:  

   Kiel Daniel Corkran is a Bayesian statistician and epidemiologist who specialize in the modeling of infectious diseases in various settings, such nursing homes or urban anchor hospitals. His educational history start with a B.S. in Mathematics from Kansas State University in 2014 and then in 2024 a M.S in Statistics from the University of Missouri-Kansas City. Kiel also received a discipline distinction from UMKC's School of Engineering and Science (SSE) and  for his academic work, student community involvement  and research during his time as a  Masters Student. Also, during his time as a masters student Kiel serve as a predoctoral fellow in the Midwest Virtual Laboratory (MVL) , which was the joint collaboration effort between UMKC and UC Davis's School of Veterinary Medicine to develop and apply novel computational tools and mathematical models to better understand infectious disease transmission in healthcare settings. MVL was funded by the Center for Disease Control (CDC) as part of their infectious diseases modeling pathways program. Research papers accomplished during this time as a MVL-Pathways fellow by Kiel include papers that dealt with modeling the quantitative impact of shared staff upon infectious diseases transmission within two simulated nursing homes and calculation of yearly MRSA transmission patterns set in a urban anchor hospital  using Bayesian inference techniques. A full list of Kiel's presentations as fellow for the CDC can found by simply clicking on the MVL-PATHS-Research Button  

   Additionally, Kiel serve as the statistical director for Multidisciplinary Analysis Research and Clinical Hub (MARCH), which is UMKC's first student led multidisciplinary analysis research team dedicated to clinical research. Examples of work Kiel's helped to direct include poster and presentations ranging in subject from surgical procedures, dermatological issues, effeteness of artificial heart pumps, and neurological disorders. Several of these works also have featured prominent medical conferences, such as  ACG, AASLD, Academic Surgical Congress, American College of Surgeons, and Missouri American College of Cardiologist. 

 Currently, Kiel is working on a interdisciplinary PhD with its primary discipline being Mathematics with an emphasis in statistics and a co-discipline of Bio-Informatics. Projected finish of Kiel's PhD degree will be Fall 2025 and a possible post-doc position in Spring of January 2026.  His primary research focus for his PhD is the creation of advancements in Healthcare Associated Infections (HAI) modeling built from the implantation of Bayesian statistics. Examples of these advancements in modeling include using Bayesian inference techniques to create more effective agent based modeling or expanding the power of the  Delayed Rejection Adaptive Metropolis (DRAM) algorithm by enabling unknown model parameters sets to be entered in a stepwise process. He is also involved in the  Mitigating Alcohol and Drug Abuse Using Artificial Intelligence and Secure Networked Sensing (MADA) project, which is part of the  National Science Foundation (NSF) research trainee programs. Objectives behind the MADA project center around generating new findings  about the potential of AI to assist in the creation of mean prevention strategies for illegal  drug abuse.