Member Highlight: Mathews Jacob

Member Highlight: Mathews Jacob
Professor, Electrical and Computer Engineering
University of Virginia, USA

Mathews Jacob - I am a professor in the Department of ECE at the University of Virginia. My research interests include computational medical imaging. I received my B.Tech from National Institute of Technology Calicut, M.E from the Indian Institute of Science, and Ph.D. from Swiss Federal Institute of Technology. I was a Beckman postdoctoral fellow at the University of Illinois at Urbana Champaign. It was my previlage to work with several excellent PhD students and post-docs, who received two best paper awards (2015 & 2021) and one best machine learning paper award (2019). I was fortunate to have received the NSF CAREER award, Research Scholar Award from American Cancer Society, and Faculty Excellence Award for Research from University of Iowa and am a Fellow of the IEEE. I served as the general chair of IEEE International Symposium on Biomedical Imaging, 2020 and distinguished lecturer of the IEEE Signal Processing Society. I am currently the associate editor of the IEEE Transactions on Medical Imaging and has served as the associate editor of IEEE Transactions on Computational Imaging from 2016-20.

We approached Mathews Jacob to learn more:

Q: Why did you choose to become a faculty in the field of signal processing?

I was incredibly fortunate to have outstanding mentors early in my career who opened my eyes to the beauty of mathematics and computational methods. They not only shaped my understanding but also ignited a deep passion for using these tools to make sense of the world and improve it. To me, Signal Processing is a perfect blend of elegance and impact—it allows one to have fun with sophisticated mathematical tools, while solving high-impact real-world problems. In recent years, most science and engineering disciplines are becoming increasingly data-driven, where collecting enormous quantities of data and extract meaningful insights using them is the standard practice. Signal Processing and machine learning provide a powerful framework for tackling these challenges, making the field more exciting and relevant than ever. What makes this journey even more rewarding is the opportunity to collaborate with scientists across disciplines—physics, biology, medicine - where the data originates. I’ve always believed that the most exciting discoveries happen at the intersection of fields, which is a fun place to be.

My greatest motivation for becoming a faculty member in this field is to share the above sense of excitement and discovery with the next generation of scientists and engineers. Teaching and research are deeply intertwined for me - I see it as both a privilege and a joy to work with talented students, to learn from them, and to inspire them in return. There is nothing more rewarding than watching young minds grow, explore, and push the boundaries of what is possible.

Q: How does your work affect society?

I work in the field of medical imaging, where physics, computation, and biology come together to improve human health. In recent years, my focus has been on developing signal processing and machine learning algorithms for next-generation magnetic resonance imaging (MRI). MRI has rapidly embraced computational methods, transforming how scans are performed. Traditional MRI scans can be long and uncomfortable, requiring patients to remain still for extended periods. By accelerating MRI acquisition, computational techniques reduce scan times, making the experience more accessible and comfortable for a wider range of patients. Faster imaging also means hospitals can serve more patients, reducing wait times and improving access to critical diagnostics. Beyond speed, these computational approaches enhance image reconstruction, enabling high-quality imaging with less data.

This leads to more accurate diagnoses while minimizing the need for repeat scans, reducing patient exposure to contrast agents or anesthesia. Additionally, AI-driven image analysis helps radiologists interpret scans more efficiently, reducing human error and bringing AI-assisted diagnostics closer to clinical adoption. At its core, my work is about leveraging technology to make medical imaging faster, more accurate, and more accessible - ultimately improving patient care and advancing the future of healthcare.

Q: What challenges have you had to face to get to where you are today?

I was trained as a signal and image processor during my early years and PhD. One of the biggest challenges was while I transitioned from a pure image processing background to MR image acquisition and reconstruction during my postdoctoral years. At the same time, I was navigating the demands of securing an academic position and establishing my research group, making this period both intellectually and professionally challenging. However, this transition turned out to be one of the best decisions of my life. It pushed me beyond my comfort zone, giving me the confidence to work at the intersection of multiple disciplines, collaborate with experts from diverse backgrounds, and contribute to different areas of research. It also prepared me to embrace new challenges, such as diving into the rapidly evolving field of machine learning, where advances are happening at breathtaking speed. Looking back, these challenges shaped my ability to adapt, learn continuously, and push the boundaries of what is possible in medical imaging.

Q: What advice would you give to scientists/engineers in signal processing?

My advice to scientists and engineers in signal processing is to develop a deep understanding of the application domain or the signals you are working with—whether it’s medical imaging, speech processing, communications, or any other field. A strong mathematical and algorithmic foundation is crucial, but true innovation comes from knowing the nuances of your data and the real-world challenges associated with it. The most impactful contributions often come from those who bridge the gap between theory and practical applications. At the same time, one should not shy away from exploring new areas. Signal processing is constantly evolving, and the problems and solutions of the future will look very different from those of today. Emerging fields often present exciting opportunities for those willing to step beyond traditional boundaries.