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Events for February 26, 2024
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Aircraft Accident Investigation AAI 24-3Mon, Feb 26, 2024 @ 08:00 AM - 04:00 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
Mon, Feb 26, 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
Mon, Feb 26, 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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DREAM Industry Mentorship speaker series- with Kim Cooper
Mon, Feb 26, 2024 @ 12:00 PM - 01:00 PM
USC Viterbi School of Engineering
University Calendar
DREAM connects students with experienced industry professionals from a variety of tech and destination companies who help them create a vision for their futures, align their careers around purpose, and build character in the context of growth, reinvention, and constant change. Industry mentors discuss how professional challenges present opportunities for character and leadership development. This event features Kim Cooper, founder and trailblazer in VR/AR/XR, and the challenges and opportunities for entrepreneurs at the intersection of cinema, tech, and design. Co sponsored by Women in Engineering (WIE).
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: Everyone Is Invited
Contact: Elisabeth Arnold Weiss
Event Link: https://cglink.me/2nB/r394798
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AME Seminar
Mon, Feb 26, 2024 @ 01:30 PM - 02:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Zach Patterson, MIT
Talk Title: Blending Soft and Rigid for Physical Intelligence in Robotics
Abstract: Most large animals have a blend of soft and rigid materials in their load-bearing structures. This design principle is largely overlooked by traditional robotics, which favors rigid materials, and by soft robotics, which predominantly uses soft components. Inspired by the natural integration of these materials in the animal kingdom, my research aims to develop robotic systems that combine soft and rigid elements harmoniously, leading to inherent "physical intelligence.” I will begin with an exploration of manipulators that embody this innovative soft-strong paradigm, followed by a discussion on the critical role of advanced control algorithms in harnessing physical intelligence effectively. Next, I will showcase the application of this soft-rigid hybrid approach in creating biomimetic robots, drawing inspiration from marine creatures like sea turtles and echinoderms. These biomimetic robots serve as versatile experimental platforms, enabling us to explore and elucidate questions in biomechanics and paleobiology that are otherwise challenging to address. I will finally discuss how these diverse categories of robots could revolutionize the interactions of intelligent machines with the environment.
Biography: Zach Patterson is a Postdoctoral Associate at the MIT Computer Science & Artificial Intelligence Lab. His research sits at the intersection of robot design, control, and biomimetics with a focus on utilizing soft robotic technologies. Zach received his B.S. in Mechanical Engineering from the University of Pittsburgh in 2017 and his Ph.D. in Mechanical Engineering from Carnegie Mellon University in 2022.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09Location: Olin Hall of Engineering (OHE) - 406
WebCast Link: https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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CSC/CommNetS-MHI Seminar: Ingvar Ziemann
Mon, Feb 26, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ingvar Ziemann, Postdoctoral Researcher | University of Pennsylvania
Talk Title: Sharp rates in dependent learning theory
Series: CSC/CommNetS-MHI Seminar Series
Abstract: In this talk I discuss some recent advances in supervised learning with dependent data. In particular, the emphasis of this talk is to provide an instance-optimal understanding of learning with dependent data for the square loss function. The approach I present yields rates that match and extend known asymptotics even without any realizability assumption. This stands in stark contrast to typical non-asymptotic results which exhibit variance proxies that are deflated multiplicatively by the mixing time of the underlying data-generating process. Indeed, our results instead scale additively with the mixing time and are thereby only affected by second order statistics in the leading term. The key to obtaining this scaling is the introduction of the notion of a weakly sub-Gaussian class, which allows us to invoke mixed tail generic chaining. This notion is general enough to nearly all cover smooth hypothesis classes and a wide range of parametric classes. As a motivating example, I will also discuss our recent work on multi-task learning. Even when the problem itself is realizable, the analysis of a natural “two-stage” estimator decomposes into two supervised learning problems: one which is realizable, and one which is not. In this setting, we demonstrate how our refined understanding of supervised learning with dependent data can be applied to extend and sharpen existing guarantees for iid multi-task learning.
Biography: Ingvar Ziemann is a postdoctoral researcher at the University of Pennsylvania. He received his PhD in November 2022 from the Division of Decision and Control Systems at The Royal Institute of Technology (KTH) under the supervision of Henrik Sandberg. His research is centered on using statistical and information theoretic tools to study learning-enabled control methods, with a current interest in studying how learning algorithms generalize in the context of dynamical systems. Prior to starting his Ph.D., he obtained two sets of Master's and Bachelor's degrees in Mathematics (SU/KTH) and in Economics and Finance (SSE). Ingvar is the recipient of a Swedish Research Council International Postdoc Grant, the 2022 IEEE Conference on Decision and Control Best Student Paper Award, and the 2017 Stockholm Mathematics Center Excellent Master Thesis Award.
Host: Dr. Lars Lindemann
More Info: https://csc.usc.edu/seminars/2024Spring/ziemann.html
More Information: 2024.02.26 CSC Seminar - Ingvar Ziemann.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://csc.usc.edu/seminars/2024Spring/ziemann.html
