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Events for November 16, 2022
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Healthcare Labor ManagementWed, Nov 16, 2022 @ 11:00 AM - 12:00 PM
Executive Education
Conferences, Lectures, & Seminars
Speaker: TBD, TBD
Talk Title: Healthcare Labor Management
Abstract: The USC Viterbi School of Engineering's Healthcare Labor Management course offered in partnership with the Institute of Industrial and Systems Engineers (IISE) will provide an understanding and overview of critical aspects of designing and executing a comprehensive labor management program.
Host: Executive Education
More Info: https://viterbiexeced.usc.edu/healthcare-labor-management-course-page/
Audiences: Registered Attendees
Contact: Corporate and Professional Programs
Event Link: https://viterbiexeced.usc.edu/healthcare-labor-management-course-page/
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DEN@Viterbi: How to Apply Virtual Info Session
Wed, Nov 16, 2022 @ 11:00 AM - 12:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi representatives for a step-by-step guide and tips for how to apply for formal admission into a Master's degree or Graduate Certificate program. The session is intended for individuals who wish to pursue a graduate degree program completely online via USC Viterbi's flexible online DEN@Viterbi delivery method.
Attendees will have the opportunity to connect directly with USC Viterbi representatives and ask questions about the admission process throughout the session.
Register Now!WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=ecd2626ed617b316b446484a4ed9c56f0
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Nov 16, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Hayk Martiros, Skydio
Talk Title: Frontiers of Autonomous Flight and Real-Time 3D Reconstruction
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: At Skydio, we ship autonomous robots that are flown at scale in unknown environments every day by our customers to capture incredible video, automate dangerous inspections, build digital twins, and protect the lives of soldiers and first responders. These robots operate intelligently and make decisions at high speed using their onboard cameras and algorithms. We've invested a decade of R&D into handling complex visual scenarios and building a robust pipeline for visual navigation, obstacle avoidance, and rapid trajectory planning. On top of that, we're building a rich ecosystem of real-time 3D reconstruction technology to enable 360 global localization and map building on our drones.
During the talk, I will discuss the technology and impact of our core navigation stack and 3D Scan technology, and what research frontiers lie ahead. I plan to share visual examples of the algorithms in action, and connect to how these products solve pressing global challenges and enable next-generation operations across multiple industries. I will also introduce SymForce, our library for fast symbolic computation, code generation, and nonlinear optimization. This library powers many of our algorithms, and we have just published and open-sourced it as a contribution to the robotics community.
Biography: Hayk is a roboticist leading the autonomy group at Skydio, building robust visual autonomy to enable the positive impact of drones. Hayk has worked at Skydio since 2015 and was one of its first employees, where he contributed to all of Skydio's core autonomy systems. He now focuses on technical management of world-class engineers and researchers. Hayk's technical interests are in computer vision, deep learning, nonlinear optimization, systems architecture, and symbolic computation. His previous works include novel hexapedal robots, collaboration between robot arms, micro-robot factories, solar panel farms, and self-balancing motorcycles. Hayk was born in Yerevan, Armenia and grew up in Fairbanks, Alaska. He did his undergraduate study at Princeton University and graduate study at Stanford University.
Host: Somil Bansal, somilban@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3QLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3Q
Audiences: Everyone Is Invited
Contact: Talyia Whtie
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PhD Defense - Aleksei Petrenko
Wed, Nov 16, 2022 @ 03:00 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Aleksei Petrenko
Thesis title: High-Throughput Methods for Simulation and Deep Reinforcement Learning
Committee members: Gaurav S. Sukhatme (chair), Stefanos Nikolaidis, Jesse Thomason, Mike Zyda, and Rahul Jain
Location: RTH 306
Date: November 16. 2022
Time: 3 pm
Zoom link: https://usc.zoom.us/j/8712894950
Thesis abstract:
Advances in computing hardware and machine learning have enabled a data-driven approach to robotic autonomy where control policies are learned from raw data via interactive experience collection and learning. In this thesis we discuss a specific implementation of this approach: we show how control policies can be trained in simulated environments using model-free deep reinforcement learning techniques and then be deployed on real robotic systems.
We build towards this vision by developing tools for efficient simulation and learning under a constrained computational budget. We improve systems design of reinforcement learning algorithms and simulators to create high-throughput GPU-accelerated infrastructure for rapid experimentation. We then apply these systems and algorithms to continuous control problems in challenging domains. We first consider the problem of quadrotor swarm coordination. By scaling up training in a CPU-based flight simulator we train robust policies that are able to control physical quadrotors flying in tight formations. We then use large batch reinforcement learning in a massively parallel physics simulator IsaacGym to learn dexterous object manipulation with a multi-fingered robotic hand and we transfer these skills from simulation to reality using automatic domain randomization.
The high-throughput learning infrastructure developed for these and other projects is released as an open-source codebase "Sample Factory 2.0" to facilitate and accelerate further progress in the field.
Location: Ronald Tutor Hall of Engineering (RTH) - 306
WebCast Link: https://usc.zoom.us/j/8712894950
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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AME Seminar
Wed, Nov 16, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Khalid Jawed, UCLA
Talk Title: Reduced Order Modeling and Inverse Design of Flexible Structures by Machine Learning
Abstract: Recent advances in highly deformable structures necessitate simulation tools that can capture nonlinear geometry and nonlinear material. We propose machine learning, neural networks (NN) in particular, to capture this nonlinearity and solve highly nonlinear inverse problems in structural mechanics. Two representative problems will be discussed in this talk.
In the first problem, we use NN to reduce the number of variables and speed up the simulation by orders of magnitude. As a test case, we explore the dynamical simulation of a slinky, a pre-compressed elastic helix that is widely used as a toy for children. However, most often the deformation of a slinky can be fully captured by the deformation of its helix axis. Instead of simulating the entire helical structure, the axis of the helix is a reduced-order representation of this system. We use NN to store the elastic forces of the slinky in its reduced-order representation, utilizing the concept of neural ordinary differential equations. The NN is trained using data from a fine-grained 3D rod simulation called the Discrete Elastic Rods (DER). Once the elastic forces in the reduced representation are stored in the NN, force balance equations can be solved in this representation for the dynamic simulation. This results in savings in computational time without much impact on its physical accuracy.
In the second problem, we explore shape-morphing structures that spontaneously transition from planar to 3D shapes. This is a transformative technology with broad applications in soft robotics and deployable systems. However, realizing these morphing structures that can achieve certain target shapes is challenging and typically involves a painstaking process of trials and errors with complex local fabrication and actuation. We propose a rapid design approach for fully soft structures that can achieve targeted 3D shapes through a fabrication process that happens entirely on a 2D plane. By combining the strain mismatch between layers in a composite shell and locally relieving stress by creating kirigami cuts, we are able to create 3D free buckling shapes from planar fabrication. However, the large design space of the kirigami cuts and strain mismatch presents a challenging task of inverse form finding. We develop a symmetry-constrained active learning approach to learn how to explore the large design space strategically. Interestingly, we report that, given a target 3D shape, multiple design solutions exist, and our physics-guided machine learning approach can find them in a few hundred iterations. Desktop-controlled experiments and finite element simulations are in good agreement in examples ranging from peanuts to flowers.
Acknowledgment: Our lab is supported by the National Science Foundation (Award numbers: IIS-1925360, CMMI-2053971, CMMI-2101751, CAREER-2047663, OAC-2209782, CNS-2213839), the National Institute of Food and Agriculture of the US Department of Agriculture (Award # 2021-67022-34200, 2022-67022-37021), and the Department of Energy (Smart Manufacturing Institute, UCLA).
Biography: M. Khalid Jawed is an Assistant Professor in the Department of Mechanical and Aerospace Engineering of the University of California, Los Angeles, and the Principal Investigator of the Structures-Computer Interaction Laboratory. He received his Ph.D. and Master's degrees in Mechanical Engineering from the Massachusetts Institute of Technology in 2016 and 2014, respectively. He holds dual Bachelor's degrees in Aerospace Engineering and Engineering Physics from the University of Michigan, Ann Arbor. He also served as a Postdoctoral Researcher at Carnegie Mellon University. He received the NSF CAREER Award in 2021, the outstanding teaching award from UCLA in 2019, the outstanding teaching assistant award from MIT in 2015, and the GSNP best speaker award at the American Physical Society March Meeting in 2014.
Dr. Jaweds research interests lie at the intersection of structural mechanics and robotics, emphasizing a data-driven and artificially intelligent approach to the modeling and design of programmable smart structures. Current research projects include robotic manipulation of flexible structures, numerical simulation of highly deformable structures, soft robotics, and robotics for precision agriculture.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
