Rate My Professor Mathews Jacob

Mathews Jacob is a Professor of Electrical and Computer Engineering at the University of Virginia, heading the Computational Biomedical Imaging Group (CBIG). His research focuses on image reconstruction, image analysis, and quantification in magnetic resonance imaging (MRI), with applications in ultrahigh-resolution brain MRI, cardiac and pulmonary MRI, and metabolic imaging. He develops machine learning algorithms to solve inverse problems in biomedical imaging. Jacob earned his B.Tech. in Electronics and Communication Engineering from the National Institute of Technology, Calicut, in 1996, M.E. in Signal Processing from the Indian Institute of Science, Bangalore, and Ph.D. in Biomedical Imaging from the Swiss Federal Institute of Technology (EPFL) in 2003. He was a Beckman Postdoctoral Fellow at the University of Illinois at Urbana-Champaign from 2003 to 2006.

Jacob's career includes Assistant Professor positions at the University of Rochester (2007-2011) and University of Iowa (2011-2015), Associate Professor at the University of Iowa (2015-2019), and Professor at the University of Iowa across Electrical and Computer Engineering, Biomedical Engineering, Radiation Oncology, and Radiology (2019-2024), before joining UVA in August 2024. He has received the IEEE Fellowship in 2022 for contributions to computational biomedical imaging, NSF CAREER Award in 2009, American Cancer Society Research Scholar Award in 2011, University of Iowa Research Excellence Award in 2020, Shannon Fellow at UVA in 2024, and Eminent Researcher award from the Virginia Innovation Partnership Corporation in 2024. Jacob is a Distinguished Lecturer for the IEEE Signal Processing Society in 2025 and served as General Chair of the IEEE International Symposium on Biomedical Imaging in 2020. He is Associate Editor for IEEE Transactions on Medical Imaging and was for IEEE Transactions on Computational Imaging (2016-2020). Key publications include 'MoDL: Model Based Deep Learning Architecture for Inverse Problems' (IEEE Transactions on Medical Imaging, 2019), 'Local monotone operator learning using non-monotone operators: MnM-MOL' (IEEE Transactions on Computational Imaging, in press), and 'Dynamic imaging using deep generative SToRM (Gen-SToRM) model' (IEEE Transactions on Medical Imaging, in press). He is senior author on best paper awards at IEEE ISBI (2015, 2021) and best machine learning paper (2019).