Research Excellence Award Winners Spring 2022

May 6, 2022
News

Congratulations to our spring 2022 Research Excellence Award Winners:

  • Charles Labuzzetta: Charles Labuzzetta is advised by Professor Zhengyuan Zhu. He was selected for developing innovative statistical and machine learning methods to solve land cover classification problems in remote sensing, which include a machine learning based surface water classification algorithm, deep learning based mapping of best management practice for soil and water conservation, and  conformal prediction under covariate shift and its application to improve image segmentation for land cover classification.
  • Miranda Tilberg: Miranda Tilberg came to us from St Olaf College in the Fall of 2017.  She got her MS degree looking at machine learning methods to recognize shoe prints in 2019.  For her PhD research, she worked with Philip Dixon on statistical issues associated with animal home ranges.  The home range is the area typically used by an animal.  The motivation was understanding how dolphins on the Louisiana coast responded to the Deepwater Horizon oil spill.  She developed a method that provided the first uncertainty estimates for a commonly used estimator of home ranges and a method to cluster home ranges to identify groups of animals with overlapping home ranges.  She received a research excellence award for this work in Spring 2022.  She received her PhD in May 2022 and has started a job with Travelers Insurance.
  • Carlos Llosa Vite: Carlos Llosa was advised by Professor Ranjan Maitra. He was awarded for his pioneering work in the analysis of tensor-variate data by providing  computationally practical but theoretically sound approaches to perform linear regression and analysis of variance when both the responses and the predictors have array-variate (tensor-variate) structure. Carlos also developed inference on elliptically contoured tensor-variate distributions that can improve upon the normal in characterizing bitmap images, and help in their learning. Separately, he provided a flexible family of tensor-variate distributions with Fourier covariance structure (FCS). He also contributed a great deal to a National Institute of Justice grant, where the main objective is to identify matching material fragments using fracture mechanics.
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