Jeffrey A. Fessler

Dr. Jeffrey A. Fessler is a recognized authority in the fields of electrical engineering and computer science, with a distinguished career at the University of Michigan. Holding the prestigious title of William L. Root Collegiate Professor, he also serves as a Professor of Biomedical Engineering and Radiology. His extensive research portfolio is centered on statistical signal and image processing, tomographic imaging, and parameter estimation, with a particular focus on machine-learning methods for inverse problems. Dr. Fessler's work is pivotal in advancing the understanding and development of image reconstruction techniques for X-ray CT and MRI. His contributions have significantly impacted the field of biomedical imaging, where his innovative approaches to nonparametric estimation and dictionary learning are highly regarded. His research not only enhances the quality and efficiency of medical imaging but also opens new avenues for exploration in sparsity and inverse problems. Throughout his career, Dr. Fessler has been involved in numerous collaborative projects with leading industry partners such as GE Healthcare, Intel Corporation, and KLA-Tencor. These collaborations underscore his commitment to bridging the gap between academic research and practical applications, ensuring that his work remains at the forefront of technological advancements. In addition to his research, Dr. Fessler is dedicated to mentoring the next generation of engineers and scientists. His teaching and guidance have inspired countless students, many of whom have gone on to make significant contributions in their respective fields. His passion for education and research excellence continues to drive his work, making him a respected figure in the academic community. Dr. Fessler's contributions to the field have been recognized through numerous awards and honors, reflecting his status as a leader in his discipline. His ongoing research endeavors promise to further enhance the capabilities of medical imaging technologies, ultimately improving patient care and outcomes.