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Events for November 22, 2024
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EiS Communications Hub - Tutoring for Engineering Ph.D. Students
Fri, Nov 22, 2024 @ 10:00 AM - 02:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Come to the EiS Communications Hub for one-on-one tutoring from Viterbi faculty for Ph.D. writing and speaking projects!
Location: Ronald Tutor Hall of Engineering (RTH) - 222A
Audiences: Viterbi Ph.D. Students
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
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AME Special Seminar
Fri, Nov 22, 2024 @ 10:00 AM - 11:30 AM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Serhiy Yarusevych, University of Waterloo, Canada
Talk Title: Lifting Surfaces at Aerodynamically low Reynolds numbers: Recent Advances
Abstract: Flow development over lifting surfaces in aerodynamically low Reynolds number flows (Re<500,000) is largely governed by boundary layer separation and subsequent separated sear layer development on the suction side. In the time-averaged sense, rapid laminar-to-turbulent transition in the separated shear layer leads to the formation of a closed recirculating flow region referred to as the Laminar Separation Bubble (LSB). However, LSBs feature rich dynamics associated with the formation and evolution of shear layer roll up vortices leading to laminar-to-turbulent transition. Linear stability analysis confirms that there is a continuous stability spectrum spanning laminar boundary later and separated shear layer regions, linking LSB transition and shear layer vortex shedding to upstream amplification of disturbances that originate from free-stream perturbations in the receptivity region. Flow development in the aft portion of the bubble is highly three-dimensional even on nominally two-dimensional geometries. It manifests in progressive deformation of shear layer vortices and subsequent vortex breakdown. On a finite wing, an open LSB forms due to wing tip and root effects. Away from the affected regions, however, LSB topology and dynamics appear to be quasi two-dimensional despite effective angle of attack variation across the span. Changes in operating conditions, including velocity and angle of attack, can lead to significant transient flow developments associated with bubble bursting (i.e., sudden lengthening or full separation without subsequent reattachment) and LSB re-formation, accompanied by substantial changes in aerodynamic loads.
Biography: Dr. Serhiy Yarusevych is a full professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Canada. He is directing the Fluid Mechanics Research Laboratory focused on multidisciplinary applications of fluid mechanics in engineering and science, including operation of lifting surfaces at low Reynolds numbers, flows over bluff bodies, free shear flows, flow induced vibrations and noise, and flow control. The associated research involves a combination of experimental, analytical, and numerical tools, with the main emphasis placed on experiments involving particle image velocimetry. His research in Canada was interposed by sabbatical leaves at TU Delft and the University of Bundeswehr Munich, in 2013-2014 and 2019-2020, respectively, involving collaborative research with advanced flow diagnostic tools and volumetric measurements. Dr. Yarusevych is an Alexander von Humboldt Fellow, Mercator Fellow, and Associate Fellow of AIAA. Since 2018, Dr. Yarusevych has been serving as an Editor-in-Chief of Experimental Thermal and Fluid Science, Elsevier.
Host: AME Department
Location: Olin Hall of Engineering (OHE) - 406
Audiences: Everyone Is Invited
Contact: Tessa Yao
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Tom Goldstein- AI Safety Issues in Generative Models: Memorization and Detection
Fri, Nov 22, 2024 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Tom Goldstein, Volpi-Cupal Professor of Computer Science at the University of Maryland, and director of the Maryland Center for Machine Learning
Talk Title: AI Safety Issues in Generative Models: Memorization and Detection
Abstract: Machine learning systems are built using large troves of training data that may contain private or copyrighted content. In this talk, I'll survey a number of data memorization issues that arise when sensitive data is used. I'll begin by talking about data privacy issues that arise when using generative models. These models are often created using a training objective that explicitly promotes their ability to regenerate their training data. I'll discuss how diffusion models can reproduce their training data, leading to potential legal issues. I'll also discuss methods for detecting large language model content and explore ways in which the ability to reproduce training data complicates our ability to detect LLM-produced text.
Biography: Tom Goldstein is the Volpi-Cupal Professor of Computer Science at the University of Maryland, and director of the Maryland Center for Machine Learning. His research lies at the intersection of machine learning and optimization, and targets applications in computer vision and signal processing. Professor Goldstein has been the recipient of several awards, including SIAM’s DiPrima Prize, a DARPA Young Faculty Award, a JP Morgan Faculty award, an Amazon Research Award, and a Sloan Fellowship.
Host: Mahdi Soltanolkotbi
More Information: ECE AIF4S Seminar Series Announcement.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) -
Audiences: Everyone Is Invited
Contact: Ana Hernandez
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AI Seminar- Do We Need Large Language Models for Time Series?
Fri, Nov 22, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Vinayak Gupta, Lawrence Livermore National Laboratory
Talk Title: Do We Need Large Language Models for Time Series?
Abstract: Abstract-Join Zoom Meeting: https://usc.zoom.us/j/98248194762?pwd=KCPIsauraEJDFnw102leuBjxehbbiM.1 Meeting ID: 982 4819 4762 Passcode: 470845 Register in advance for this webinar: https://usc.zoom.us/webinar/register/WN_78--B06ZRNub3zx6WKvfmg After registering, you will receive a confirmation email containing information about joining the webinar. Visit links below to subscribe and for details on upcoming seminars: https://www.isi.edu/isi-seminar-series/ https://www.isi.edu/events/ Recent large language models (LLMs) have only shown potential for reasoning with text and image data. We explore this reasoning ability with one of the most important data formats: time-series. Capturing the sequential nature of time-series data is crucial to power applications in finance and healthcare. This talk presents a first-of-its-kind benchmark that focuses on truly understanding time-series data and goes beyond the existing evaluations. Additionally, we will discuss the notable limitations of existing works claiming that LLMs can perform forecasting. Our analysis across such models finds that simply removing the LLMs or replacing them with a basic attention layer improved results in most cases, and also led to better scalable solutions.
Biography: Vinayak Gupta is a researcher in the AI Research Group at the Lawrence Livermore National Laboratory. Prior to this, he was a postdoctoral scholar at the University of Washington, Seattle, and an AI Scientist at IBM Research. His research focuses on mining large-scale time-series data, and more recently, he has been working on leveraging LLMs to jointly understand text+time-series. He received his PhD from the Indian Institute of Technology, Delhi in 2022. He was a runner-up in the AI Gamechangers of India and was featured as an AI expert in India AI, the AI initiative of the Government of India. Host: Abel Salinas, POC: Pete Zamar If speaker approves to be recorded for this AI Seminar talk, it will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI.
Host: Abel Salinas and Pete Zamar
More Info: https://www.isi.edu/events/5181/do-we-need-large-language-models-for-time-series/
Webcast: https://www.youtube.com/watch?v=_7v5ICY0L_cLocation: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://www.youtube.com/watch?v=_7v5ICY0L_c
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://www.isi.edu/events/5181/do-we-need-large-language-models-for-time-series/
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Fri, Nov 22, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Charles Murry, Professor of Stem Cell Biology and Regenerative Medicine, KSOM, USC
Talk Title: Regenerating the Heart: Peeling an Onion
Abstract: The concept of human heart regeneration has progressed from a quixotic aspiration to a realistic possibility. Fundamental discoveries in stem cell biology, cell cycle control, and cellular reprogramming have opened new therapeutic avenues into the world’s number one cause of death. In this lecture, I will focus on pluripotent stem cell-based approaches to heart regeneration, touching on key advances in cell sourcing, scaled manufacturing, delivery, efficacy, mechanism of action, engraftment arrhythmias, and immune rejection.
Biography: Dr. Charles (Chuck) Murry received his bachelor’s degree in chemistry from the University of North Dakota, followed by MD-PhD training at Duke University, where he studied myocardial ischemia-reperfusion injury (heart attacks). He did residency training in Anatomic Pathology at the University of Washington, followed by fellowship training in vascular biology and diagnostic cardiovascular pathology. At the UW, Murry was the founding director of the Center for Cardiovascular Biology, and he cofounded and for many years directed the Institute for Stem Cell and Regenerative Medicine. Murry recently was recruited to the University of Southern California Keck School of Medicine, where he chairs the department of Stem Cell Biology and Regenerative Medicine and directs the Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research.
Dr. Murry’s research focuses on stem cell biology, with an emphasis on understanding differentiation of the human cardiovascular system and using these cells to study diseases and to regenerate damaged tissues. His group is a world leader in heart regeneration and is working toward a clinical trial using cardiomyocyte therapy. He has served on many local, national and international committees, spoken widely about stem cells and cardiovascular medicine, and he has received numerous awards for teaching and scientific achievement. Dr. Murry is a past member of the International Society for Stem Cell Research Board of Directors and currently serves on its Manufacturing, Clinical Translation, and Industry Committee.
In addition to his academic work, Murry has worked to promote commercialization of novel cardiovascular therapies. He cofounded BEAT Biotherapeutics, Sana Biotechnology, and most recently a Los Angeles-based startup called StemCardia.
Host: Peter Yingxiao Wang- Chair of Alfred E. Mann Department of Biomedical Engineering
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Carla Stanard
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ISSS - Dr. Farhana Sheikh, Friday, Nov. 22nd at 2pm in EEB 132
Fri, Nov 22, 2024 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Farhana Sheikh, Principal Engineer, Intel
Talk Title: FPGA-Chiplet Architectures and Circuits for 2.5D/3D 6G Intelligent Radios
Series: ISSS
Abstract: The number of connected devices is expected to reach 500 billion by 2030, which is 59-times larger than the expected world population. Objects will become the dominant users of next-generation communications and sensing at untethered, wireline-like broadband performance, bandwidths, and throughputs. This sub-terahertz 6G communication and sensing will integrate security and intelligence. It will enable a 10x to 100x increase in peak data rates. FPGAs are well positioned to enable intelligent radios for 6G when coupled with high-performance chiplets incorporating RF circuits, data converters, and digital baseband circuits incorporating machine learning and security. This talk presents use of 2.5D and 3D heterogeneous integration of FPGAs with chiplets, leveraging Intel's EMIB/Foveros technologies with focus on one emerging application driver: FPGA-based 6G sub-THz intelligent wireless systems. Nano-, micro-, and macro-3D heterogeneous integration is summarized, and previous research in 2.5D chiplet integration with FPGAs is leveraged to forge a path towards new 3D-FPGA based 6G platforms. Challenges in antenna, packaging, power delivery, system architecture design, thermals, and integrated design methodologies/tools are briefly outlined. Opportunities to standardize die-to-die interfaces for modular integration of internal and external circuit IPs are also discussed.
Biography: Farhana Sheikh is a Principal Engineer and Research Manager at Altera (Intel), where she leads technology pathfinding and the Advanced Chiplet Technologies Team. With over 15 years of experience in ASIC and DSP/communications research, she specializes in 2D/3D chiplet and FPGA integration for wireless and sensing applications. She initiated the AIB-3D open-source specification, published over 50 papers, and filed 22 patents. She has received multiple IEEE ISSCC Outstanding Paper Awards (2020, 2019, 2012) and serves as IEEE SSCS Distinguished Lecturer (2023-2024). Dr. Sheikh earned her M.Sc. and Ph.D. from UC Berkeley in 1996 and 2008 respectively, and actively promotes women in circuits through IEEE SSCS.
Host: MHI - ISSS, Hashemi, Chen and Sideris
More Info: https://usc.zoom.us/j/94125605508
More Information: MHI_Seminar_Flyer_Farhana_Sheikh_Nov22_2024.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: https://usc.zoom.us/j/94125605508
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PhD Thesis Defense - Pengmiao Zhang
Fri, Nov 22, 2024 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense: Pengmiao Zhang
Committee: Prof. Murali Annavaram, Prof. Rajgopal Kannan, Prof. Viktor K. Prasanna (Chair), Prof. Cauligi Raghavendra, Prof. Vatsal Sharan
Title:Machine Learning for Memory Access Prediction and Data Prefetching
Abstract:
Modern applications often experience performance bottlenecks due to memory system limitations. Data prefetching can hide memory access latency by predicting and loading data before it is needed. Machine Learning (ML) algorithms present a promising opportunity to enhance prefetching strategies. However, developing a high-performance ML-based prefetcher presents the following challenges: 1. ML modeling for memory access prediction, including extracting features from historical patterns, identifying future access targets, and designing models to capture their correlations. 2. Domain specific irregular memory access patterns due to multi-core execution and processing phases. 3. Balancing ML model complexity with hardware constraints, ensuring low-latency predictions while maintaining high performance. 4. Coordinated management of multiple prefetchers for ensemble prefetching. In this dissertation, we develop highly optimized ML models for data prefetching. First, to efficiently predict memory accesses for prefetching, we propose TransFetch, a novel attention-based approach that models prefetching as a multi-label classification problem. Second, we introduce a Domain Specific Machine Learning approach for prefetching, utilizing the context of architecture and computation to build high-performance ML-based prefetchers. Using this approach, we develop MPGraph and GraFetch to accelerate the execution of graph applications. Third, towards practical hardware deployment of ML-based prefetchers, we propose a novel tabularization approach that uses table hierarchies to approximate neural networks. We introduce DART, a table-based neural network prefetcher, and Net2Tab, a flexible tabularization framework. Lastly, we present ReSemble, an adaptive framework that uses reinforcement learning to optimize the coordination of multiple prefetchers. Our ML-based prefetchers show significant IPC improvements, demonstrating their performance advantages.
Bio: Pengmiao Zhang is a sixth-year PhD candidate in Computer Engineering, advised by Professor Viktor K. Prasanna. He received his BS degree in Electrical Engineering from Northeastern University (China) and MEng degree in Electrical Engineering from Harbin Institute of Technology. His research interests include machine learning for computer systems, memory system optimizations, and efficient machine learning.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Julia Mittenberg-Beirao
Event Link: https://usc.zoom.us/j/9379439223
