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Events for September 16, 2022
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Viterbi How to Get Hired Series: Employer Resume Review
Fri, Sep 16, 2022 @ 10:00 AM - 03:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Throughout the day, there will be Resume Reviews conducted On-Campus. Students will have the opportunity to receive feedback from industry professionals: Alumni, engineers, and recruiters.
Location: TBD
Participating Companies: Acopula Networks, Inc., Acushnet Company, Ballard Construction Inc, Boeing, Chef Koochooloo Inc, Couch Tutors, EY, Granite Construction Company, HRL Laboratories, Illumina, KPMG, Ramboll, REVOLVE, Rhoman Aerospace, Tesla, Turner Construction Company, Visage Energy, W.E. O'Neil (Updated 8/19/2022)
Registration information will be emailed to engineering students after August 29th.
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Student Registration Fri, Sep 16, 2022 @ 11:00 AM - 12:00 PM
USC Viterbi School of Engineering
University Calendar
Student Registration for Great Minds in Stem Conference. Use Discount Code GMiSCon2022 through 9/16 for a $99 registration.
Audiences: Everyone Is Invited
Contact: Raymond USC Viterbi
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Student Registration
Fri, Sep 16, 2022 @ 11:00 AM - 12:00 PM
USC Viterbi School of Engineering
University Calendar
Student Registration for Great Minds in Stem Conference. Use Discount Code GMiSCon2022 through 9/16 for a $99 registration.
More Information: Conference Office Hours.pdf
Audiences: Everyone Is Invited
Contact: Raymond USC Viterbi
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Medical Imaging Seminar Series
Fri, Sep 16, 2022 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yunsong Liu, Electrical and Computer Engineering, University of Southern California
Talk Title: Optimization Methods and Algorithms for Constrained Magnetic Resonance Imaging
Series: Medical Imaging Seminar Series
Abstract: Constrained MRI methods have shown great potential to improve the well-known trade-offs that exist in MRI between data acquisition time, signal-to-noise-ratio, and spatial resolution. In constrained MRI, we utilize prior information about the characteristics of the underlying MRI images to perform data acquisition, image reconstruction, and image analysis tasks more efficiently. This approach generally requires the use of mathematical optimization techniques, although the optimization problems are often challenging to solve efficiently due to their large-scale and non-trivial structure.
In this presentation, I will discuss three novel contributions I have made to mathematical optimization for constrained MRI. First, I will discuss work that utilizes phase constraints to accelerate MRI data acquisition based on non-Fourier radiofrequency encoding. While phase constraints are used classically in MRI, we believe that this is the first time that phase constraints are being applied to enable acceleration along a non-Fourier encoded spatial dimension. We make the novel observation that phase constraints can indeed be successfully used to reduce the number of required non-Fourier encodings, although this requires careful design of the non-Fourier encoding scheme. Results are presented in the context of gSlider, an acquisition method designed for highly-efficient high-resolution diffusion MRI. Second, we will describe a new algorithm we have developed that is designed for the separate regularization of magnitude and phase in MRI reconstruction problems. Our approach is based on a novel application of the proximal alternating linearized minimization algorithm (PALM), and incorporates additional novel features (i.e., Nesterov's momentum and independent selection of the step sizes for each coordinate) to increase convergence speed. Depending on the application, our proposed algorithm can be hundreds of times faster than existing algorithms for this problem. Finally, we will describe a novel algorithm that we have developed for spatial-spectral partial volume compartment mapping with applications to multicomponent diffusion and relaxation MRI. Our proposed algorithm is based on a novel application of the linearized alternating directions method of multipliers (LADMM) approach that takes advantage of the special structure of the inverse problem, and depending on the dataset, can achieve up to 5-fold acceleration compared to previous algorithms for this problem.
Biography: Yunsong Liu is a PhD candidate in Electrical and Computer Engineering at University of Southern California, supervised by Prof. Justin Haldar. He obtained his Bachelor's and Master's degree in Electrical Engineering at Xiamen University, China. He then spent half a year working on structured matrix recovery in Math Department at Hong Kong University of Science and Technology before joining Prof. Haldar's group at USC. His research has been focused on signal processing and optimization with applications in MRI.
Host: Justin Haldar, jhaldar@usc.edu
Webcast: https://usc.zoom.us/j/92068313291?pwd=MnlVUTJrWkRpUVdQYU04S2t4cUVjZz09Location: Online
WebCast Link: https://usc.zoom.us/j/92068313291?pwd=MnlVUTJrWkRpUVdQYU04S2t4cUVjZz09
Audiences: Everyone Is Invited
Contact: Talyia White
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ECE-EP Seminar - Dr. Kaiyuan Yang, Friday, September 16th at 2pm in EEB 248
Fri, Sep 16, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Kaiyuan Yang, Rice University
Talk Title: Tackling the Energy Limitations in Miniaturized Internet of Everything Devices
Abstract: Following Moore's law and Bell's law, miniaturization of electronic devices is continuously transforming the human life and the society. The next generation miniature devices are envisioned to ubiquitously connect physical objects in the world, digitizing cities, homes, industries, and human health and medicine. The major challenge in building these emerging hardware platforms is achieving all the desired sensing, computing, communication, and security functionalities under extreme power and size constraints. In this talk, I will present our recent efforts on unconventional circuit and systems designs to enable millimetric implantable bioelectronic medicine, and escalating security and intelligence of all sorts of edge devices. We take holistic design approaches to alleviate the energy issues without compromising system usability, exploring cross-disciplinary co-design opportunities from materials and devices, all the
way up to computing algorithms and programming languages. Specifically, I will present (1) magnetoelectric power and data transfer technologies to millimeter-sized battery-free bioelectronic implants, with system integrations and validations; (2) hardware-enabled foundational security primitives and modules fitting stringent power and cost budgets; and (3) processing in-memory systems for deep learning and stream processing with cross-layer designs.
Biography: Dr. Kaiyuan Yang is currently an Assistant Professor of ECE at Rice University, USA. He received his B.S. degree in Electronic Engineering from Tsinghua University, China, in 2012, and his Ph.D. degree in Electrical Engineering from the University of Michigan, Ann Arbor, MI, in 2017. His research interests include digital and mixed-signal circuit and system design for secure and intelligent microsystems, bioelectronics, and hardware security.
Dr. Yang is a recipient of the 2022 National Science Foundation (NSF) CAREER award, 2016 IEEE SSCS Predoctoral Achievement Award, and multiple best paper awards from premier conferences in various fields, including 2021 IEEE Custom Integrated Circuit Conference (CICC), 2016 IEEE Symposium on Security and Privacy (Oakland), 2015 IEEE International Symposium on Circuits
and Systems (ISCAS), and the Best Student Paper Award finalist at 2022 RFIC and 2019 CICC. He is currently serving as an associate editor of IEEE TVLSI and a co-chair of SSCS Houston chapter.
Host: Prof. Hashemi, Prof. Chen and Prof. Sideris
More Information: Abstract and Bio-Sept 16-Yang.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
