PeopleofSuperintelligence

Researcher

Jeffrey A. Fessler

Also known as Jeff Fessler

Jeff Fessler is a professor of electrical engineering and computer science at the University of Michigan whose work on image reconstruction for medical imaging has grown to include neural networks and diffusion models. He earned a PhD in electrical engineering at Stanford in 1990 under Albert Macovski and has worked at Michigan since then, first as a postdoctoral fellow and assistant professor in nuclear medicine. In 2019 and 2021, he coauthored Momentum-Net, an iterative neural network for inverse problems, and BJORK, a method that learns MRI sampling trajectories together with a neural-network reconstruction. He received the IEEE EMBS Technical Achievement Award in 2016, became a Fellow of the Asia-Pacific Artificial Intelligence Association in 2024, and wrote the textbook Linear Algebra for Data Science, Machine Learning, and Signal Processing with Raj Rao Nadakuditi, published in 2024. He is the Root Distinguished University Professor of EECS and was interim chair of Electrical and Computer Engineering from 2024 to 2025.

Born
1964
Badges

Years link to their sources.

Timeline

  1. 1985–1990

    Purdue and a Stanford PhD

    He earns a BSEE from Purdue University in 1985, then an MSEE from Stanford in 1986 and an MS in statistics from Stanford in 1989. As a National Science Foundation Graduate Fellow at Stanford from 1985 to 1988, he completes a PhD in electrical engineering there in 1990 under adviser Albert Macovski.

  2. 1990–1995

    Michigan postdoc and assistant professor

    He starts work at the University of Michigan in 1990. He is a Department of Energy Alexander Hollaender Post-Doctoral Fellow in the Division of Nuclear Medicine from 1991 to 1992, then an assistant professor in Nuclear Medicine and the Bioengineering Program from 1993 to 1995.

  3. 2006–2016

    IEEE Fellow and two awards

    He becomes a Fellow of the IEEE in 2006 for contributions to the theory and practice of image reconstruction. He receives the Edward Hoffman Medical Imaging Scientist Award in 2013 and an IEEE EMBS Technical Achievement Award in 2016. Michigan names him William L. Root Collegiate Chair of EECS in 2016.

  4. 2019–2021

    Momentum-Net and BJORK

    In 2019, he coauthors Momentum-Net with Il Yong Chun, Zhengyu Huang and Hongki Lim, an iterative neural network for inverse problems that combines regression networks with model-based image reconstruction. In 2021, he coauthors BJORK with Guanhua Wang, Tianrui Luo, Jon-Fredrik Nielsen and Douglas C. Noll, which trains a neural-network reconstruction and a B-spline k-space trajectory together for accelerated 2D MRI.

  5. 2023–2024

    Attwood Award and AI fellowship

    In 2023, he receives the Stephen S. Attwood Award, the highest honor the College of Engineering gives a faculty member. In 2024, he becomes a Fellow of the Asia-Pacific Artificial Intelligence Association and receives a Distinguished University Professorship at Michigan.

  6. May 2024

    A linear algebra textbook

    In May 2024, Cambridge University Press publishes Linear Algebra for Data Science, Machine Learning, and Signal Processing, which he wrote with Raj Rao Nadakuditi. Its chapters cover matrix factorization, low-rank approximation, matrix completion and neural network models. He recommends it for his course ECE 551, Matrix Methods for Signal Processing, Data Analysis and Machine Learning.

  7. 2024–2025

    Interim chair of ECE

    From 2024 to 2025, he serves as interim chair of Electrical and Computer Engineering (ECE) at Michigan. In 2025, he becomes an ISMRM Senior Fellow.

  8. As of October 2026

    Root Distinguished Professor at Michigan

    He is the William L. Root Distinguished University Professor of EECS at the University of Michigan, with affiliations in Biomedical Engineering and Radiology. His research covers medical imaging and inverse problems in X-ray CT, MRI, PET and SPECT. His IEEE Signal Processing Society webinar of June 2026 covers diffusion models for MRI and low-dose X-ray CT image reconstruction.

Career

  1. Since 1990University of MichiganWilliam L. Root Distinguished University Professor, Post-Doctoral Fellow

Education

  1. 1990Stanford UniversityPhD
  2. 1989Stanford UniversityMS in statistics
  3. 1986Stanford UniversityMSEE
  4. 1985Purdue UniversityBSEE

From the biography.

  1. Universities had a net outflow of 2,068 moves in AI careersIncludes University of Michigan. Our estimates of career flows and median h-index across universities and companies in AI, including the role of joint appointments and earlier academic jobs.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of Michigan. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  3. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes University of Michigan. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  4. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Michigan. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Jeffrey A. Fessler yet. Send us a free-licensed photo.

  1. Jeff Fessler short biography web.eecs.umich.edu. Checked 21 October 2026.
  2. Fessler, Jeffrey A.: Biographical Information web.eecs.umich.edu. Checked 21 October 2026.
  3. Joint Optimization of Learning-Based Image Reconstruction and Sampling for MRI | Texas ECE ece.utexas.edu. Checked 21 October 2026.
  4. Momentum-Net: Fast and convergent iterative neural network for inverse problems arxiv.org. Checked 21 October 2026.
  5. B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI arxiv.org. Checked 21 October 2026.
  6. Linear Algebra for Data Science, Machine Learning, and Signal Processing, ISBN 9781009418140 betterread.com.au. Checked 21 October 2026.
  7. Fessler, Jeffrey A.: UM EECS Web page web.eecs.umich.edu. Checked 21 October 2026.
  8. ECE 551 (formerly EECS 551) Homepage web.eecs.umich.edu. Checked 21 October 2026.
  9. SPS CI Webinar: Efficient Score-Based Diffusion Models for Imaging Inverse Problems signalprocessingsociety.org. Checked 21 October 2026.