Michael (Miki) Lustig

Michael (Miki) Lustig

Title
Associate Professor
Department
Division of Computer Science/EECS
Division of Electrical Engineering/EECS
Phone
(510) 643-9338
Research Expertise and Interest
medical imaging, MRI, compressed sensing
Research Description

Michael (Miki) Lustig is an Associate Professor in EECS. He joined the faculty of the EECS Department at UC Berkeley in Spring 2010. He received his B.Sc. in Electrical Engineering from the Technion, Israel Institute of Technology in 2002. He received his Msc and Ph.D. in Electrical Engineering from Stanford University in 2004 and 2008, respectively. His research focuses on computational imaging methods in medical imaging, particularly Magnetic Resonance Imaging (MRI). Miki is a Fellow of the Society of Magnetic Resonance in Medicine.

In the News

March 21, 2022

‘Off label’ use of imaging databases could lead to bias in AI algorithms, study finds

Significant advances in artificial intelligence (AI) over the past decade have relied upon extensive training of algorithms using massive, open-source databases. But when such datasets are used “off label” and applied in unintended ways, the results are subject to machine learning bias that compromises the integrity of the AI algorithm, according to a new study by researchers at the University of California, Berkeley, and the University of Texas at Austin.
February 1, 2016

Savvy Software Lightens MRI Burden

The Bakar Fellows Program supports Michael Lustig’s collaborations with clinicians and industry to speed adoption of the new MRI imaging strategies.

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In the News

March 21, 2022

‘Off label’ use of imaging databases could lead to bias in AI algorithms, study finds

Significant advances in artificial intelligence (AI) over the past decade have relied upon extensive training of algorithms using massive, open-source databases. But when such datasets are used “off label” and applied in unintended ways, the results are subject to machine learning bias that compromises the integrity of the AI algorithm, according to a new study by researchers at the University of California, Berkeley, and the University of Texas at Austin.
February 1, 2016

Savvy Software Lightens MRI Burden

The Bakar Fellows Program supports Michael Lustig’s collaborations with clinicians and industry to speed adoption of the new MRI imaging strategies.

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