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Events for December 10, 2021
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DEN@Viterbi: How to Apply Virtual Info Session
Fri, Dec 10, 2021 @ 09:00 AM - 10:00 AM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi representatives for a step-by-step guide and tips for how to apply for formal admission into a Master's degree or Graduate Certificate program. The session is intended for individuals who wish to pursue a graduate degree program completely online via USC Viterbi's flexible online DEN@Viterbi delivery method.
Attendees will have the opportunity to connect directly with USC Viterbi representatives and ask questions about the admission process throughout the session.
Register Now!WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=e5b659b5af59cae9d39c38b8b90838d69
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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Medical Imaging Seminar Series
Fri, Dec 10, 2021 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Talk Title: Improved Regularized Simultaneous Multi-slice (SMS) Imaging Reconstruction
Series: Medical Imaging Seminar Series
Abstract: MRI acquisitions are inherently slow, necessitating the use of accelerated imaging. Simultaneous multi- slice (SMS) imaging has gained substantial interest by providing improved coverage with minimum signal- to-noise ratio (SNR) loss in accelerated MRI and has been widely integrated into large-scale projects such as Human Connectome Project. However, ultra-high accelerations are prone to noise amplification and residual aliasing artifacts, necessitating new reconstruction techniques that can successfully suppress both. In this talk, we will present recently developed techniques for regularized SMS reconstruction. We will first introduce two model-based algorithms that simultaneously reduce noise amplification and inter-leakage artifacts. Subsequently, we will concentrate on physics-guided deep learning reconstruction for SMS MRI with applications in fMRI. Finally, we will discuss an alternative way to view the multi-coil encoding operator in physics-guided DL reconstruction for improved generalizability in dynamic contrast-enhanced MRI.
Biography: Omer Burak Demirel is a PhD candidate at the University of Minnesota working with Prof. Mehmet Akçakaya. Prior to the University of Minnesota, he received the B.S. and M.S. degrees from Bilkent University, Ankara, Turkey in January 2015 and June 2017, respectively. His research interests include image processing, MRI acquisition methods, image reconstruction techniques and accelerated MRI. He is a recipient of an AHA predoctoral fellowship focusing on improved image reconstruction techniques for cardiac MRI.
Host: Krishna Nayak, knayak@usc.edu
Webcast: https://usc.zoom.us/j/95249648177?pwd=RHNsSnlhMk0vaEtPeExXRkRPOE55dz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://usc.zoom.us/j/95249648177?pwd=RHNsSnlhMk0vaEtPeExXRkRPOE55dz09
Audiences: Everyone Is Invited
Contact: Talyia White
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PhD Thesis Proposal - Séb Arnold
Fri, Dec 10, 2021 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Séb Arnold
Friday, Dec 10, 2021 @ 03:00 PM - 05:00 PM
Committee members: Chair: Prof. Maja Mataric, Prof. Fei Sha, Prof. Yan Liu, Prof. Stefanos Nikolaidis, Prof. Jesse Thomason, Prof. Salman Avestimehr (ECE)
Title:
Quickly solving new tasks, with meta-learning and without.
Abstract:
This thesis proposal seeks to answer how learning systems can reuse and adapt their knowledge to quickly solve new test tasks. We first show how to improve the test task performance of meta-learning algorithms (eg, MAML) by carefully choosing which tasks to train on -- even when these test tasks are unknown a priori. We then zero in on these algorithms and uncover modeling pitfalls that completely prevent fast adaptation; fortunately, there exist simple remedies. Leveraging those insights, we conclude with the challenge of quickly solving new tasks using off-the-shelf models, which were trained without meta-learning.
Zoom link: https://usc.zoom.us/j/94965325337
WebCast Link: https://usc.zoom.us/j/94965325337
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
