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SUNMONTUEWEDTHUFRISAT
Events for March 08, 2024
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System Safety SSC 24-2Fri, Mar 08, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
Instruction is given in system safety engineering and management, emphasizing complex, high-technology systems. Engineering methods are illustrated with practical, numerical examples. The principal system safety analysis method is taught with classroom and homework problems. The preparation of a system safety program plan and management of the system safety process in all phases of the system life are examined in depth. A classroom project allows students to apply system safety management and engineering methods while working as a team. Enrichment lectures in special areas of knowledge essential to the system safety process will also be presented. Each student should bring a calculator with statistical functions.
Location: Century Boulevard Building (CBB) - 920
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24ASSC2
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Aircraft Accident Investigation AAI 24-3
Fri, Mar 08, 2024 @ 08:00 AM - 12:00 PM
Aviation Safety and Security Program
University Calendar
The course is designed for individuals who have limited investigation experience. All aspects of the investigation process are addressed, starting with preparation for the investigation through writing the final report. It covers National Transportation Safety Board and International Civil Aviation Organization (ICAO) procedures. Investigative techniques are examined with an emphasis on fixed-wing investigation. Data collection, wreckage reconstruction, and cause analysis are discussed in the classroom and applied in the lab. The USC Aircraft Accident Investigation lab serves as the location for practical exercises. Thirteen aircraft wreckages form the basis of these investigative exercises. The crash laboratory gives the student an opportunity to learn the observation and documentation skills required of accident investigators. The wreckage is examined and reviewed with investigators who have extensive actual real-world investigation experience. Examination techniques and methods are demonstrated along with participative group discussions of actual wreckage examination, reviews of witness interview information, and investigation group personal dynamics discussions.
Location: WESTMINSTER AVENUE BUILDING (WAB) - Unit E
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AAAI3
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EiS Communications Hub Drop-In Hours
Fri, Mar 08, 2024 @ 10:00 AM - 01:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi Ph.D. students are invited to stop by the EiS Communications Hub for one-on-one instruction for their academic and professional communications tasks. All instruction is provided by Viterbi faculty at the Engineering in Society Program.
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?authuser=0
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EiS Communications Hub Drop-In Hours
Fri, Mar 08, 2024 @ 10:00 AM - 01:00 PM
Engineering in Society Program
Student Activity
Drop-in hours for writing and speaking support for Viterbi Ph.D. students
Location: Ronald Tutor Hall of Engineering (RTH) - 222
Audiences: Everyone Is Invited
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
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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
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AI Seminar
Fri, Mar 08, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Yu-Ru Lin, Univ. of Pitt., Univ of Pitt
Talk Title: A Gateway to Trustworthy AI: Using Visual Analytics to Unmask Coincidental Correlations
Abstract: Join Zoom Meeting https://usc.zoom.us/s/99782858348?pwd=MnlSdGlTVWNETGFFbDQ4OWRmakdEQT09 Meeting ID: 997 8285 8348 Passcode: 580559 Register in advance for this webinar: https://usc.zoom.us/webinar/register/WN_xxYy3NkSQpidFYRY3fg_Ew In the realm of machine learning and data-driven decision-making, the risk of spurious and biased associations poses significant challenges to the integrity and reliability of AI systems. In this talk, I will introduce how visual analytic designs can empower data practitioners in navigating these complex issues. First, through a human-in-the-loop workflow, we tackle the problem of AI blindspots in classification models, where key patterns are often missed or misleading. Our design offers visually interpretable statistical methods to quantify and understand concept associations. It also includes debiasing techniques to address misleading patterns in data. Second, we tackle Simpson’s Paradox, a phenomenon where associations in data appear contradictory at different levels of aggregation, leading to cognitive confusion and incorrect interpretations. Our design offers an intuitive causal analysis framework and a human-centric workflow, enabling users to identify, understand, and prevent spurious associations, leading to more accountable causal decision-making. Together, these design frameworks contribute to making AI more trustworthy, offering robust tools for overcoming the challenges of spurious and biased associations in machine learning through advanced visual analytics.
Biography: Website: http://www.yurulin.com/ Yu-Ru Lin is an Associate Professor in the School of Computing and Information and the Research Director of the Institute for Cyber Law, Policy, and Security (Pitt Cyber) at the University of Pittsburgh, where she directs the PITT Computational Social Dynamics Lab (PICSO LAB). Her research lies at the intersection of Computational Social Science, Data Mining, and Visualization. She specializes in using social network and text data along with statistical learning tools and social theories to study phenomena spanning societal events and policy, anomalous behaviors, and other crucially important complex patterns concerning collective attention and actions, as well as human and social dynamics in response to societal risks. Her work has appeared in prestigious scientific venues and has been featured in the press, including WSJ, The Boston Globe, The Atlantic, MIT News, and NPR. She has authored or co-authored more than 100 refereed journal and conference papers and served on more than 50 conference program committees in the areas of big data, network science, and computational social science. She has served as a chair/co-chair of leading computational social science, web mining, and social media conferences such as AAAI ICWSM and TheWebConference/WWW (Web & Society Track). She currently serves as an Editor-in-Chief of AAAI ICWSM and an Associate Editor for multiple journals, including PLOS ONE, Springer EPJ Data Science, Nature's Scientific Reports, and Frontiers in Big Data. She was selected as a Fellow of Kavli Frontiers of Science, National Academy of Sciences (NAS).
Host: Fred Morstatter and Zhuoyu Shi
More Info: https://www.isi.edu/events/4389/ai-seminar-a-gateway-to-trustworthy-ai-using-visual-analytics-to-unmask-coincidental-correlations/
Webcast: https://www.youtube.com/watch?v=2uZOOM6-nooLocation: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://www.youtube.com/watch?v=2uZOOM6-noo
Audiences: Everyone Is Invited
Contact: Pete Zamar
