A Dynamic Approach to Linear Statistical Calibration with Applications to Microwave Radiometry
Ph.D. Student: Derick Rivers
Faculty Advisor: Ed Boone
This work funded by the National Aeronautics and Space Administration (NASA) and Jenkins Pre-doctoral Fellowship Project (JPFP) involves the development of a calibration algorithm that captures the dynamics of an earth-observing microwave radiometer. Calibration of these highly sensitive instruments is required due to the fact that the current electronic hardware is unable to maintain a stable I/O relationship because of random gain fluctuations. In this project I develop a Bayesian statistical calibration method that computes the posterior distributions for calibration temperatures that reflect a system that is changing due to random gain fluctuations. This model can be used perform calibration in the present of time-varying parameters.
Dispatch, Delivery, and Location Logistics for the Aeromedical Evacuation of Military Casualties
Ph.D. Student: Ben Grannan
Faculty Advisors: Laura McLay, Jason Merrick
My research examines the logistics of aeromedical evacuation in military medical systems to increase survivability of combat casualties. Improved decision making in military medical systems saves lives by providing high-priority casualties with timely medical care. We develop discrete optimization models that leverage Markov decision processes, linear integer programming, and spatial queuing theory to solve hard logistical problems inherent in military medical systems. The models strategically locate and dispatch scarce aeromedical evacuation assets such as the Sikorsky UH-60 Black Hawk helicopter (pictured).
Simultaneously Optimized Follow-Up Designs
Ph.D. Student: Bob Leonard
Faculty Advisor: David Edwards
Optimal follow-up experimentation provides a useful way to augment an initial design when traditional follow-up approaches are not appropriate. When choosing a single criterion for which to optimize the combined design, practitioners often have to deal with tradeoffs. For example, choosing to optimize the D-criterion can help with precision of model parameter estimates but at the possible expense of model misspecification. Past studies have investigated comparing multiple criteria in order to incorporate multiple design objectives into a follow-up design, but only after optimizing a single criterion. The research investigates simultaneously optimizing multiple criteria as a viable alternative to forming follow-up designs. Practical uses of such designs are also investigated by comparing these methods to traditional fold-over techniques and single-criterion optimization approaches.