Rate My Professor Mathews Jacob

Jacob's research focuses on machine learning algorithms for image reconstruction and inverse problems, particularly in magnetic resonance imaging (MRI), including ultrahigh resolution brain MRI, cardiac and pulmonary MRI, and metabolic imaging. His lab develops mathematical tools and applied algorithms for medical image reconstruction, analysis, and quantification, collaborating with clinical researchers in areas such as magnetic resonance imaging, near infrared spectroscopic imaging, and microscopy. 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, special issue on Deep Learning for Image Reconstruction, in press). He holds major NIH grants, including "Model Based Deep Learning for Ultra-High Resolution Multi-Contrast MRI," "Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI," and "High Resolution MR Spectroscopic Imaging for Alzheimer's Disease and Related Dementias." Awards include Fellow of the IEEE (2022) for contributions to computational biomedical imaging, Shannon Fellow at University of Virginia (2024), Eminent Researcher from Virginia Innovation Partnership Corporation (2024), Distinguished Lecturer of the IEEE Signal Processing Society (2025), NSF CAREER Award (2009), Research Scholar Award from American Cancer Society (2011), and senior authorship on best paper awards at IEEE ISBI (2015, 2019, 2021). He serves as Associate Editor for IEEE Transactions on Medical Imaging, was Associate Editor for IEEE Transactions on Computational Imaging (2016-2020), and General Chair of IEEE International Symposium on Biomedical Imaging (2020).