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Events for March 21, 2023
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Principles in Patient Flow
Tue, Mar 21, 2023 @ 09:00 AM - 05:00 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
The Principles in Patient Flow course will cover industrial engineering concepts and tools to improve your institution├óÔé¼Ôäós patient flow throughout their entire encounter, from check in to check out. Learn the importance of the emergency department in the modern hospital environment.
Delivery options: On Campus and OnlineLocation: Sign into EngageSC to View Location
Audiences: Everyone Is Invited
Contact: Melissa Medeiros
Event Link: https://engage.usc.edu/viterbi/rsvp?id=387009
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ECE-S Seminar - Dr Jiaqi Gu
Tue, Mar 21, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Jiaqi Gu, PhD Candidate | University of Texas at Austin
Talk Title: Light in Artificial Intelligence: Hardware/Software Co-Design for Photonic Machine Learning Computing
Abstract: The proliferation of big data and artificial intelligence (AI) has motivated the investigation of next- generation AI computing hardware to support massively parallel and energy-hungry machine learning (ML) workloads. Photonic computing, or computing using light, is a disruptive technology that can bring orders-of- magnitude performance and efficiency improvement to AI/ML with its ultra-fast speed, high parallelism, and low energy consumption. There has been growing interest in using nanophotonic processors for performing optical neural network (ONN) inference operations, which can make transformative impacts in future datacenters, automotive, smart sensing, and intelligent edge. However, the substantial potential in photonic computing also brings significant design challenges, which necessitates a cross-layer co-design stack where the circuit, architecture, and algorithm are designed and optimized in synergy.
In this talk, I will present my exploration to address the fundamental challenges faced by optical AI and to pioneer a hardware/software co-design methodology toward scalable, reliable, and adaptive photonic neural accelerator designs. First, I will delve into the critical area scalability issue of integrated photonic tensor units and present specialized photonic neural engine designs with domain-specific customization that significantly "compresses" the circuit footprint while realizing comparable inference accuracy. Next, I will present efficient on-chip training frameworks to show how to build a self-learnable photonic accelerator and overcome the robustness and adaptability bottlenecks by directly training the photonic circuits in situ. Then, I will introduce how to close the virtuous cycle between photonics and AI by applying AI/ML to photonic device simulation. In the end, I will conclude the talk with future research directions of emerging domain-specific photonic AI hardware with an intelligent end-to-end co-design & automation stack and deploying it to support real-world applications.
Biography: Jiaqi Gu is a final-year Ph.D. candidate in the Department of Electrical and Computer Engineering at The University of Texas at Austin, advised by Prof. David Z. Pan and co-advised by Prof. Ray T. Chen. Prior to UT Austin, he received his B.Eng. from Fudan University, Shanghai, China, in 2018. His research interests include emerging post-Moore hardware design for efficient computing, hardware/software co-design, photonic machine learning, and AI/ML algorithms.
He has received the Best Paper Award at the ACM/IEEE Asian and South Pacific Design Automation Conference (ASP-DAC) in 2020, the Best Paper Finalist at the ACM/IEEE Design Automation Conference (DAC) in 2020, the Best Poster Award at the NSF Workshop for Machine Learning Hardware Breakthroughs Towards Green AI and Ubiquitous On-Device Intelligence in 2020, the Best Paper Award at the IEEE Transaction on Computer-Aided Design of Integrated Circuits and Systems (TCAD) in 2021, the ACM Student Research Competition Grand Finals First Place in 2021, and Winner of the Robert S. Hilbert Memorial Optical Design Competition in 2022.
Host: Dr Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/j/99786583943?pwd=MnlmNGxQUUIwWXpWbk0wTUhrQWsxZz09More Information: ECE Seminar Announcement 03.21.2023 - Jiaqi Gu.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/99786583943?pwd=MnlmNGxQUUIwWXpWbk0wTUhrQWsxZz09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Yue Zhao (CMU) - Scalable and Automated Systems and Algorithms for Unsupervised ML
Tue, Mar 21, 2023 @ 11:00 AM - 12:00 PM
Computer Science
Conferences, Lectures, & Seminars
Speaker: Yue Zhao, Carnegie Mellon University
Talk Title: Scalable and Automated Systems and Algorithms for Unsupervised ML
Series: CS Colloquium
Abstract: Many real-world events do not have outcome labels. For example, the fraudulence of a transaction remains unknown until it is discovered. This is where unsupervised machine learning (ML) becomes crucial in real-world scenarios as it can make decisions based solely on observations. In this talk, I will address two key challenges in unsupervised ML: (i) developing scalable learning systems that can handle large amounts of data, and (ii) automating the selection of the best ML model. The first part of the talk will cover an ML system called TOD, which can "compile" a diverse group of ML algorithms for GPU acceleration. The second part will describe an automated algorithm called MetaOD, which can select top ML models for various applications without relying on labels or evaluations. Lastly, I will discuss my future plans, including the ML+X initiative, which aims to bring the advantages of ML systems and automation to other domains, and the creation of a fully automated ML pipeline that chooses hardware, systems, and models seamlessly.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Yue Zhao is a Ph.D. candidate at CMU, working with Prof. Leman Akoglu and Prof. Zhihao Jia. He focuses on creating scalable and automated ML systems and algorithms, and has published over 30 papers in top venues such as VLDB, MLSys, JMLR, and NeurIPS. His open-source systems (https://github.com/yzhao062) have been widely deployed in firms and industries such as Morgan Stanley and Tesla, and have received over 15,000 GitHub stars and 10 million downloads. Yue has received the CMU Presidential Fellowship and Norton Graduate Fellowship. More information about him can be found at https://www.andrew.cmu.edu/user/yuezhao2/.
Host: Robin Jia
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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DEN@Viterbi - Online Graduate Engineering Virtual Information Session
Tue, Mar 21, 2023 @ 12:00 PM - 01:00 PM
Distance Education Network, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a virtual information session via WebEx, providing an introduction to DEN@Viterbi, our top ranked online delivery system. Discover the 40+ graduate engineering and computer science programs available entirely online.
Attendees will have the opportunity to connect directly with USC Viterbi representatives during the session to discuss the admission process, program details and the benefits of online delivery.
Register Today!WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=efdd95dd866832ba5f12889f63f11b0b9
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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Epstein Institute - ISE 651 Seminar
Tue, Mar 21, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Brian Denton, Professor and Dept. Chair, Dept. of Industrial & Operations Engineering, University of Michigan, Ann Arbor
Talk Title: Optimization in the Presence of Model Ambiguity in Markov Decision Processes
Host: Dr. Sze-chuan Suen
More Information: March 21, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
