SUNMONTUEWEDTHUFRISAT
Events for March 08, 2024
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Quantum Science & Technology Seminar - David Vitali - Friday, March 8th at 10am in EEB 248
Fri, Mar 08, 2024 @ 10:00 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: David Vitali, Univeristy of Camerino, Italy
Talk Title: Quantum Sensing and Quantum State Manipulation in Cavity Optomechanics
Series: Quantum Science & Technology Seminar Series
Abstract: Cavity Optomechanics offers the possibility to generate and manipulate quantum states of mesoscopic mechanical resonators allowing the realization of useful components of quantum networks, and at the same time testing fundamental aspects of physics theories. We will review recent proposals for generating multipartite entangled states of mechanical resonators and also their exploitation for quantum sensing of weak forces and signals.
Biography: David Vitali graduated in Physics at the University of Pisa in 1988 and obtained his PhD in Physics from the Scuola Normale Superiore of Pisa in 1994. He has been Visiting Lecturer at the University of North Texas (USA), at the Ecole Normale Superieure in Paris, at the University of Queensland , Brisbane (Australia), and at the University of Vienna. He is Full Professor of Theoretical Physics at the University of Camerino since 2015. He is the author of 193 publications in international refereed journals, with more than 10700 citations and Hirsch index h = 52 referring to the SCOPUS database. He has carried out research in many subfields of Quantum Optics and Quantum Information Theory, such as entanglement manipulation, quantum communication and quantum key distribution, quantum optics implementation of quantum technologies. In 2015 he was named APS Fellow of the American Physical Society, "For groundbreaking work on cavity opto-mechanics, which proved to provide an ideal and flexible environment for quantum information processing and quantum-limited sensing; for proposing pioneering techniques to control decoherence in quantum systems." In 2021 he was nominated OPTICA Senior Member, and he has coordinated various European projects and many National projects, all related to quantum technologies and quantum optomechanics.
Host: Quntao Zhang, Wade Hsu, Mengjie Yu, Jonathan Habif & Eli Levenson-Falk
More Information: David Vitali Seminar Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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ECE-S Seminar - Zhijian Liu
Fri, Mar 08, 2024 @ 10:30 AM - 11:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Zhijian Liu, PhD Candidate | Massachusetts Institute of Technology
Talk Title: Efficient Deep Learning with Sparsity: Algorithms, Systems, and Applications
Abstract: Machine learning is widely used across a broad spectrum of applications. However, behind its remarkable performance lies an increasing gap between the demand for and supply of computation. On the demand side, the computational costs of machine learning models have surged dramatically, driven by ever-larger input and model sizes. On the supply side, as Moore's Law slows down, hardware no longer delivers increasing performance within the same power budget.
In this talk, I will discuss my research efforts to bridge this demand-supply gap through the lens of sparsity. I will begin by discussing my research on input sparsity. First, I will introduce algorithms that systematically eliminate the least important patches/tokens from dense input data, such as images, enabling up to 60% sparsity without any loss in accuracy. Then, I will present the system library that we have developed to effectively translate the theoretical savings from sparsity to practical speedups on hardware. Our system is up to 3 times faster than the leading industry solution from NVIDIA. Following this, I will touch on my research on model sparsity, highlighting a family of automated, hardware-aware model compression frameworks that surpass manual solutions in accuracy and reduce the design process from weeks of human efforts to mere hours of GPU computation. Finally, I will present several examples demonstrating the use of sparsity to accelerate computation-intensive AI applications, such as autonomous driving, language modeling, and high-energy physics. I will conclude this talk with an overview of my ongoing work and my vision towards building more efficient and accessible AI.
Biography: Zhijian Liu is a Ph.D. candidate at MIT, advised by Song Han. His research focuses on efficient machine learning. He has developed efficient ML algorithms and provided them with effective system/algorithm support. He has also contributed to accelerating computation-intensive AI applications in computer vision, natural language processing, and scientific discovery. His work has been featured as oral and spotlight presentations at conferences such as NeurIPS, ICLR, and CVPR. He was selected as the recipient of the Qualcomm Innovation Fellowship and the NVIDIA Graduate Fellowship. He was also recognized as a Rising Star in ML and Systems by MLCommons and a Rising Star in Data Science by UChicago and UCSD. Previously, he was the founding research scientist at OmniML, which was acquired by NVIDIA.
Host: Mahdi Soltanolkotabi, soltanol@usc.edu | Peter Beerel, pabeerel@usc.edu
More Info: https://usc.zoom.us/j/96790337008?pwd=ZDljTkhHYjRQaUovUmJTSHZhR1ovUT09
Webcast: https://usc.zoom.us/j/96790337008?pwd=ZDljTkhHYjRQaUovUmJTSHZhR1ovUT09More Information: 2024.03.08 ECE Seminar - Zhijian Liu.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
WebCast Link: https://usc.zoom.us/j/96790337008?pwd=ZDljTkhHYjRQaUovUmJTSHZhR1ovUT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://usc.zoom.us/j/96790337008?pwd=ZDljTkhHYjRQaUovUmJTSHZhR1ovUT09
