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Jeffrey Fessler

Teaching signal processing during COVID-19

From adapting to remote office hours to completely redesigning exam content and format, we explore how one class, EECS 551 Matrix Methods for Signal Processing, Data Analysis, & Machine Learning, has had to reinvent itself for the times.

Research to improve medical imaging of the brain receives Magna cum Laude Merit award

The interdisciplinary team was able to dramatically speed up the process while potentially doubling the quality of the image

Magna cum Laude Merit Award for research to detect the progress of diseases such as multiple sclerosis

The researchers’ imaging technique is fast, accurate, and reproducible

Melissa Haskell receives NIH Fellowship for research to improve brain imaging

ECE postdoc Melissa Haskell works on improving functional magnetic resonance imaging so we can better measure and understand brain activity.

A 3D camera for safer autonomy and advanced biomedical imaging

Researchers demonstrated the use of stacked, transparent graphene photodetectors combined with image processing algorithms to produce 3D images and range detection.

ECE and data science: a natural connection

Electrical and Computer Engineering (ECE) faculty and students at Michigan are part of the revolution in data science that is happening today.

Students win prizes for improving image processing techniques for liver cancer detection and much more

Students in EECS 556: Image Processing, explore methods to improve image processing in applications such as biomedical imaging and video and image compression

Gopal Nataraj receives U-M Rackham Predoctoral Fellowship to support high-impact research in medical imaging

Award for outstanding doctoral candidates near the end of their study.

Students earn prizes for improving image processing techniques in EECS 556

The course covers the theory and application of digital image processing, with applications in biomedical images, time-varying imagery, robotics, and optics.

Jeff Fessler receives 2016 IEEE EMBS Technical Achievement Award

Prof. Fessler has revolutionized the theory and practice of medical imaging with his group’s groundbreaking mathematical models and algorithms.

Jeff Fessler voted HKN Professor of the Year for ECE

Prof. Fessler was surprised (and happy) to learn of this unique honor at the end of his final class for the semester.

Jeff Fessler named William L. Root Professor of Electrical Engineering and Computer Science

In addition to being a professor of Electrical Engineering and Computer Science, Fessler is a professor of Biomedical Engineering and Radiology.

A better 3D camera with clear, graphene light detectors

While 3D films are currently made using multiple cameras to reconstruct each frame, this new type of camera could record in 3D on its own.

Jeff Fessler receives Distinguished Faculty Achievement Award

Prof. Fessler has revolutionized medical imaging with groundbreaking mathematical models and algorithms that improve both safety and quality.

Using data science to achieve ultra-low dose CT image reconstruction

Ultra-low dose CT scans that provide superior image quality could not only benefit patients, but they could open up entirely new clinical applications.

We are now one ECE: the merged graduate program in Electrical and Computer Engineering

In recognition of how the Electrical Engineering discipline has evolved, the two graduate programs, Electrical Engineering and Electrical Engineering: Systems, have merged to form one graduate program: Electrical and Computer Engineering.

Prize winning class team project for improved image processing

The project entails investigating a recent paper and both reproducing and extending the research.

Hao Sun earns 3 Paper Awards for medical imaging research

Hao’s research is focused on improving the quality of images from magnetic resonance imaging pulse design.

Mai Le receives CoE Distinguished Leadership Award

Mai has served as Community Service Co-chair of the Graduate Society of Women Engineers since arriving at Michigan in 2011.

Student Spotlight: Mai Le – Finding a better way to diagnose breast cancer with MRI

The research group is using statistical signal processing to create crisper images with only 20% of the data required by a traditional MRI scan.

Gopal Nataraj earns Best Paper Award for improving MRI

Nataraj is using big data techniques to transform the field of medical imaging

Gopal Nataraj receives ISA Fellowship to support research that will improve MRIs

Nataraj’s research aims to generate higher-quality and faster MRI images, resulting in improved diagnostics of neurological disorders and autoimmune diseases.

MCubed A Year Later: A record of fostering innovative research

Several of the cubes enabled research to progress to the point that faculty are applying for larger grants to continue the work.

Jeff Fessler receives 2013 IEEE Edward J. Hoffman Medical Imaging Scientist Award

This award recognizes outstanding contributions to the field of medical imaging science.

Student teams earn prizes for improved image processing techniques in EECS 556

The course covers the theory and application of digital image processing.

2012-13 College of Engineering Awards

Congratulations to the following recipients of 2012-13 College of Engineering Awards!

Prof. Jeff Fessler honored with Distinguished Graduate Mentor Award

Fessler’s students have praised the collegial and collaborative environment of his lab, his careful balancing of freedom and guidance, and his attention to each student.

New technology allows CT scans to be done with a fraction of the conventional radiation dose

“We’re excited to be adding Veo to the measures we already have in place to ensure that we get diagnostic images using the lowest amount of radiation possible.”

Yong Long selected as Barbour Scholar

The Barbour Scholarship, established in 1914, recognizes women at the University of Michigan of the highest academic and professional caliber.

Yong Long receives Best Poster Award for work in medical imaging

Long’s work describes a new algorithm for performing model-based methods in a way that requires less computation yet provides improved image quality.