Prof. Florian Knoll
Friedrich-Alexander University Erlangen-Nuremberg
Date/Time: Friday, February 4th, 12:30 pm
Zoom Meeting ID: 941 2666 3216 (Passcode: 896970)
Abstract: Recent developments in deep learning, as well as widespread access to powerful computing resources and large datasets, have the potential to change the way medical imaging is performed. Building on the frameworks of inverse problems, variational optimization, and compressed sensing, I will discuss the potential to make MR imaging faster, easier to use, and more patient-friendly and accessible. I will cover both methodological developments as well as clinical translation and validation and discuss ongoing developments as well as currently open research questions.
About the Speaker: Florian Knoll received his Ph.D. in electrical engineering in 2011 from Graz University of Technology. From 2013 to 2015, he was a Postdoctoral Research Fellow, and from 2015 to 2021, Assistant Professor for Radiology at the Center for Biomedical Imaging at NYU Grossman School of Medicine. Since 2021, he has been the Professor and Head of the Computational Imaging Lab at the Department of Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander University Erlangen Nuremberg. He holds an R01, R21, and a P41 TR&D project award from NIH. His research interests include iterative MR image reconstruction, parallel MR imaging, Compressed Sensing, and Machine Learning.