Research Interests
Statistical signal processing, detection theory, estimation theory, image reconstruction, machine learning, inverse problems, sparse data recovery, statistical modeling
Research Summary
Dissertation: "Source detection and image reconstruction with position-sensitive gamma-ray detectors"
Advisor: Professor Jeffrey A. Fessler
My research at the Univeristy of Michigan focuses on methods of approximating estimation and detection performance in applications where the data is recorded with complex sensors. I am currently applying performance approximation methods to position-sensitive gamma-ray detectors, an application where the number of measurements is Poisson and model mismatch is often present. I am working with the Radiation Measurement Group at the University of Michigan to test the performance approximation methods with real data.
I also work on regularization design and minimization algorithms for image reconstruction with Poisson data.
My work in gamma-ray detection and imaging is motivated by a broad interest in algorithms for extracting data from noisy measurements and their performance. I look forward to expanding my work to incorporate other types of data and sensors in the future.
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