Degree: B.S. in Applied Math at Hillsdale College in May 2022
Email: kweisen@purdue.edu
LinkedIn: Karl Weisenburger
GitHub: Karl-Weisenburger
Research Interests: Applied Math, Computational Imaging, Bayesian Estimation, Stochastic Modeling, Deep Learning
Advisors: Gregery Buzzard (Purdue, Mathematics), Charles Bouman (Purdue, Electrical and Computer Engineering)
Collaborator: Matthew Kemnetz (Air Force Institute of Technology, Deptartment of Engineering Physics)
Welcome
Hello! Thanks for visiting my website. I'm a Ph.D. Candidate in Mathematics at Purdue University conducting research in inverse problems in collaboration with the Directed Energy Directorate (RD) at the Air Force Research Laboratory (AFRL).
My research focuses on solving inverse problems, which involves deducing hidden causes or structures from their observed effects—like estimating the turbulent air density inside a wind tunnel from the way it distorts laser beams that pass through it. I specialize in model-based iterative reconstruction methods like plug-and-play and stochastic expectation maximization, which are particularly effective for scenarios with limited or sparse data. My main research application is tomographic wavefront sensing for wind tunnels, where we reconstruct turbulent density fields from optical wavefront data.
Biography
I grew up in Pemberville, Ohio, a small country town south of Toledo, Ohio. I went to Hillsdale College in Hillsdale, Michigan for my undergraduate degree. At Hillsdale, in addition to mathematics, I loved studying philosophy. I moved to Lafayette, Indiana in August 2022 to continue studying and researching applied math as a graduate student at Purdue. In my free time I love reading, biking, and spending time with my family.