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Events for the 4th week of March
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CS Colloquium: Yue Wang (MIT) - Learning 3D representations with minimal supervision
Mon, Mar 21, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yue Wang , MIT
Talk Title: Learning 3D representations with minimal supervision
Series: CS Colloquium
Abstract: Deep learning has demonstrated considerable success embedding images and more general 2D representations into compact feature spaces for downstream tasks like recognition, registration, and generation. Learning on 3D data, however, is the missing piece needed for embodied agents to perceive their surrounding environments. To bridge the gap between 3D perception and robotic intelligence, my present efforts focus on learning 3D representations with minimal supervision from a geometry perspective.
In this talk, I will discuss two key aspects to reduce the amount of human supervision in current 3D deep learning algorithms. First, I will talk about how to leverage geometry of point clouds and incorporate such inductive bias into point cloud learning pipelines. These learning models can be used to tackle object recognition problems and point cloud registration problems. Second, I will present our works on leveraging natural supervision in point clouds to perform self-supervised learning. In addition, I will discuss how these 3D learning algorithms enable human-level perception for robotic applications such as self-driving cars. Finally, the talk will conclude with a discussion about future inquiries to design complete and active 3D learning systems.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Yue Wang is a final year PhD student with Prof. Justin Solomon at MIT. His research interests lie in the intersection of computer vision, computer graphics, and machine learning. His major field is learning from point clouds. His paper "Dynamic Graph CNN" has been widely adopted in 3D visual computing and other fields. He is a recipient of the Nvidia Fellowship and is named the first place recipient of the William A. Martin Master's Thesis Award for 2021. Yue received his BEng from Zhejiang University and MS from University of California, San Diego. He has spent time at Nvidia Research, Google Research and Salesforce Research.
Host: Ramakant Nevatia
Location: online only
Audiences: By invitation only.
Contact: Assistant to CS chair
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Meet Silvus Technologies! Next Generation Tactical Mesh Networking (Virtual) This is a Viterbi-specific event!
Mon, Mar 21, 2022 @ 12:00 PM - 01:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Meet Silvus Technologies! Next Generation Tactical Mesh Networking (Virtual)
This is a Viterbi-specific event!
Monday, March 21st 12-1 pm
Register Here: https://usc.zoom.us/meeting/register/tJcvduusqTgqHtAk0dDpiCv6YpTEpnjpEoLD
Event Details: This is an intro to Silvus, our technology and an invitation for interested students to join and be a part of our mission. We will begin with our virtual seminar-like presentation, which includes our background, a few unknown facts about Silvus and potential products on the rise. Before we conclude, we will reserve time for student networking and Q&A.
Target Student audience: We want to connect with Seniors and Graduate students who are interested in Engineering, Computer engineering, Electrical engineering, Comp-Sci, and Computer Architecture fields. We are recruiting for both internship and full-time positions. At this time we cannot offer a VISA sponsorship and we are not able to hire a student on CPT or OPT.Location: RSVP in Viterbi Career Gateway
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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CS Colloquium: Erdem Bıyık (Stanford University) - Learning Preferences for Interactive Autonomy
Mon, Mar 21, 2022 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Erdem Bıyık , Stanford University
Talk Title: Learning Preferences for Interactive Autonomy
Series: CS Colloquium
Abstract: In human-robot interaction or more generally multi-agent systems, we often have decentralized agents that need to perform a task together. In such settings, it is crucial to have the ability to anticipate the actions of other agents. Without this ability, the agents are often doomed to perform very poorly. Humans are usually good at this, and it is mostly because we can have good estimates of what other agents are trying to do. We want to give such an ability to robots through reward learning and partner modeling. In this talk, I am going to talk about active learning approaches to this problem and how we can leverage preference data to learn objectives. I am going to show how preferences can help reward learning in the settings where demonstration data may fail, and how partner-modeling enables decentralized agents to cooperate efficiently.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Erdem Bıyık is a fifth-year Ph.D. candidate in the Electrical Engineering department at Stanford. He has received his B.Sc. degree from Bilkent University, Turkey, in 2017; and M.Sc. degree from Stanford University in 2019. He is interested in enabling robots to actively learn from various forms of human feedback and designing altruistic robot policies to improve the efficiency of multi-agent systems both in cooperative and competitive settings. He also worked at Google as a research intern in 2021 where he adapted his active robot learning algorithms to recommender systems.
Host: Heather Culbertson
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: By invitation only.
Contact: Assistant to CS chair
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ECE-EP Seminar - Volker Sorger, Monday, March 21st @ 2pm in EEB 248
Mon, Mar 21, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Volker Sorger, George Washington University
Talk Title: Devices & ASICs for Machine Intelligence and Post-Quantum Cryptography
Abstract: The high demand for AI services in conjunction with a dramatic chip shortage along with technology leaps such as 5/6G networks, cybersecurity threats, and quantum algorithms have resurrected a R&D push for advanced devices, information processing, and computing capability. To address this demand and explore novel technology, unique opportunities exist, for example, given by algorithmic parallelism of mixed-signal non-van Neuman accelerators. Especially electronic-photonic ASIC compute paradigms hold promise to enable non-iterative O(1) runtime complexity, ps-short latency, and TOPS/W throughputs. This opens prospects for next-generation hardware both for AI cloud services but also for accelerating edge computing such as enabled by compact and efficient PIC-CMOS co-designs pushing the SWAP envelope. As both a professor and a co-founder of a venture, in this seminar I will share my latest insights on fundamental complexity scaling and algorithm-hardware homomorphism on the one hand, and device- circuit- and system-level demonstrations on the other. I will introduce a novel memristive photonic RAM capable of zero-static power consumption suitable for AI edge applications and highlight our photonic tensor core ASIC demonstration leveraging parallelism including a software stack. Beyond matrix-matrix multiplication acceleration, I will show our Convolution Theorem-based accelerator enabling 1000x1000 matrix-size convolutions at 100us latency, or about 10x faster than today's GPUs. At the device level I will share advanced optoelectronics and quantum matter including a 50Gbps ITO-based modulator being 1,000x more compact than Silicon PDK solutions, discuss strainoptronic detectors with high gain-bandwidth-product, a 100GHz fast VCSEL, and share a path for an electrically-driven quantum source. Finally, having solved the complex-signal convolution I will show a Montgomery Multiplier for a data-center RSA public-key cryptosystem, and conclude by highlighting our recent post-quantum secure-hash-algorithm (SHA) system accelerating blockchain operations. I will conclude with an R&D outlook for the next decade and share examples of my passion supporting values and programs on diversity & inclusion.
Biography: Volker J. Sorger is an Associate Professor in the Department of Electrical and Computer Engineering and the Director of the Institute on AI & Photonics, the Head of the Devices & Intelligent Systems Laboratory at the George Washington University. His research areas include devices & optoelectronics, AI/ML accelerators, mixed-signal ASICs, quantum matter & processors, and cryptography. For his work, Dr. Sorger received multiple awards including the Presidential PECASE Award, the AFOSR YIP Award, the Emil Wolf Prize, and the National Academy of Sciences award of the year. Dr. Sorger is an Associate editor for OPTICA, serves on the board of Chip, and was the former editor-in-chief of Nanophotonics. He is a Fellow of Optica (former OSA), a Fellow of SPIE, a Fellow of the German National Academic Foundation, and a Senior Member of IEEE. He is a co-founder of Optelligence Company.
Host: ECE-Electrophysics
More Information: Volker Sorger Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium: Rachee Singh (Microsoft Azure for Operators) - Leveraging over-provisioned cloud networks for next-generation services
Tue, Mar 22, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rachee Singh , Microsoft Azure for Operators
Talk Title: Leveraging over-provisioned cloud networks for next-generation services
Series: CS Colloquium
Abstract: The last decade has seen a large-scale commercialization of cloud computing and the emergence of global cloud providers. Cloud providers are expanding their datacenter deployments and backbone capacity, preparing their infrastructure to meet the challenges of rapidly evolving workloads in the cloud. In this talk, I will re-examine the design and operation choices made by cloud providers during this phase of exponential growth using a cross-layer empirical analysis of the wide-area network (WAN) of a large commercial cloud provider. Despite their crucial role in enabling high performance cloud applications and expensive infrastructure, there are several inefficiencies in both the design and operation of cloud WANs. In this talk, I will focus on improving the performance and cost efficiency of the fiber optical network underpinning cloud WANs. First, I will demonstrate how rate-adaptive physical links can harness 75% more capacity from 80% of the optical wavelengths in a cloud WAN, leading to a gain of over 100 terabits of network capacity with 25% fewer link failures. Second, I will show how to achieve a 40% reduction in the hardware costs of provisioning long-haul WAN capacity by optically bypassing network hops where conversion of light signals from optical to electrical domain is unnecessary and uneconomical. I will show that identifying and fixing these inefficiencies in today's cloud networks is crucial for enabling next-generation cloud services.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Rachee Singh is a senior researcher in the office of the CTO of Microsoft Azure for Operators. Before this, she was a researcher in the Mobility and Networking group of Microsoft Research, Redmond. Her research interests are in computer networking with a focus on wide area network performance and monitoring. Her PhD dissertation was supported by a Google PhD Fellowship in Systems and Networking and it received the CICS Outstanding dissertation award at the University of Massachusetts, Amherst. Recently, she was named a rising star in computer networking by N2Women and a rising star in EECS by UC Berkeley. In a previous life, she developed routing protocol features for Ethernet switches at Arista Networks.
Host: Ramesh Govindan
Location: Olin Hall of Engineering (OHE) - 132
Audiences: By invitation only.
Contact: Assistant to CS chair
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Virtual First-Year Admission Information SessionTue, Mar 22, 2022 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.
Register Here!
Audiences: Everyone Is Invited
Contact: Viterbi Admission
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Webinar for U.S. Active Duty Military & Veteran Prospective Graduate Engineering Students
Tue, Mar 22, 2022 @ 12:00 PM - 01:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi to learn about the funding and support provided to U.S. active duty military and veterans who are interested in pursuing a graduate degree in engineering or computer science. This virtual information session via WebEx will discuss how USC Viterbi's flexible online DEN@Viterbi delivery enables active duty military and veterans to pursue their degree from anywhere in the world.
The session will discuss military funding and scholarships available, student support, enrollment options, FAQs, and more! Attendees will have the opportunity to connect directly with USC Viterbi representatives.
Register Now!WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=edfce0fcb3165cdfc609cc79bf92919ab
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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CS Colloquium: Ariel Barel (Technion, Israeli Institute of Technology) - Applied Deep-Learning methods for Expediting Path Selection in Real-Time MAP
Tue, Mar 22, 2022 @ 01:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ariel Barel, Technion, Israeli Institute of Technology
Talk Title: Applied Deep-Learning methods for Expediting Path Selection in Real-Time MAP
Series: Computer Science Colloquium
Abstract: Multi-Agent Path Finding (MAPF) is an NP-hard problem that plays a key role in numerous domains ranging from warehouse automation to computer games. In this research we are given a large pre-calculated set of legal paths from all possible sources to all possible destinations. The aim is to select paths from this set such that they do not collide with static obstacles nor with each other and minimize the maximal execution time of all tasks (Makespan). This selection should be calculated in near real-time, i.e., extremely fast compared to classic MAPF algorithms.
We investigate how Deep-Learning methods may speed up the search process, as trained Neural Networks have potential to make computations extremely fast. Training dataset may be generated by solving the "online" problem offline. The idea is to train the network to recognize patterns in the training examples and apply them to new, previously unseen, settings of the problem, i.e., new pairs of sources and destinations. The main challenges are definition of NN architecture and input representation.
This work addresses well-formed environments where agents may wait indefinitely at their sources but must follow a wait-free path once deployed. Moreover, our framework allows assignment of multiple agents per source and requires that all calculations complete before the first deployment, making scheduling a key component of the solution.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Join Zoom Meeting
https://usc.zoom.us/j/98857434920
Meeting ID: 988 5743 4920
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Biography: Dr. Ariel Barel is an academic visitor at the Technion, Israeli Institute of Technology. He received the PhD degrees in Computer Science from the Technion in the field of Distributed Control of Multi-Agent Systems. His current interest also includes Machine Learning implementations to expedite traditional planning algorithms. For more info and publications visit his personal web page https://arielba.cswp.cs.technion.ac.il/
Host: Christopher Leet (cjleet@usc.edu), Sven Koenig (skoenig@usc.edu)
Webcast: https://usc.zoom.us/j/98857434920Location: Online - Zoom
WebCast Link: https://usc.zoom.us/j/98857434920
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Natasha Jacques (Google Brain / UC Berkeley) - Social Reinforcement Learning
Tue, Mar 22, 2022 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Natasha Jacques, Google Brain / UC Berkeley
Talk Title: Social Reinforcement Learning
Series: CS Colloquium
Abstract: Social learning helps humans and animals rapidly adapt to new circumstances, coordinate with others, and drives the emergence of complex learned behaviors. What if it could do the same for AI? This talk describes how Social Reinforcement Learning in multi-agent and human-AI interactions can address fundamental issues in AI such as learning and generalization, while improving social abilities like coordination. I propose a unified method for improving coordination and communication based on causal social influence. I then demonstrate that multi-agent training can be a useful tool for improving learning and generalization. I present PAIRED, in which an adversary learns to construct training environments to maximize regret between a pair of learners, leading to the generation of a complex curriculum of environments. Agents trained with PAIRED generalize more than 20x better to unknown test environments. Ultimately, the goal of my research is to create intelligent agents that can assist humans with everyday tasks; this means leveraging social learning to interact effectively with humans. I show that learning from human social and affective cues scales more effectively than learning from manual feedback. However, it depends on accurate recognition of such cues. Therefore I discuss how to dramatically enhance the accuracy of affect detection models using personalized multi-task learning to account for inter-individual variability. Together, this work argues that Social RL is a valuable approach for developing more general, sophisticated, and cooperative AI, which is ultimately better able to serve human needs.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Natasha Jaques holds a joint position as a Senior Research Scientist at Google Brain and Postdoctoral Fellow at UC Berkeley. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. Natasha completed her PhD at MIT, where her thesis received the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing. Her work has also received Best Demo at NeurIPS, an honourable mention for Best Paper at ICML, Best of Collection in the IEEE Transactions on Affective Computing, and Best Paper at the NeurIPS workshops on ML for Healthcare and Cooperative AI. She has interned at DeepMind, Google Brain, and was an OpenAI Scholars mentor. Her work has been featured in Science Magazine, Quartz, MIT Technology Review, Boston Magazine, and on CBC radio. Natasha earned her Masters degree from the University of British Columbia, and undergraduate degrees in Computer Science and Psychology from the University of Regina.
Host: Jesse Thomason
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: By invitation only.
Contact: Assistant to CS chair
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ISE 651 Epstein Seminar
Tue, Mar 22, 2022 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Alain Rossier, Visiting guest/3rd year PhD student, Oxford University
Talk Title: Asymptotic Analysis of Deep Residual Networks
Host: Dr. Renyuan Xu
More Information: March 22, 2022.pdf
Location: Online/Zoom
Audiences: Everyone Is Invited
Contact: Grace Owh
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Mork Family Department Seminar - Aditya Sood
Tue, Mar 22, 2022 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Aditya Sood, Stanford University
Talk Title: Engineering functionality through dynamic visualization and control of atomic motions
Host: Professor A.Hodge
Location: Social Sciences Building (SOS) - B46
Audiences: Everyone Is Invited
Contact: Heather Alexander
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CS Colloquium: Ram Alagappan (VMware Research Group) - Co-designing Distributed Systems and Storage Stacks for Improved Reliability
Wed, Mar 23, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ram Alagappan , VMware Research Group
Talk Title: Co-designing Distributed Systems and Storage Stacks for Improved Reliability
Series: CS Colloquium
Abstract: Distributed storage systems form the core of modern cloud services. Like many systems software, these systems are built using layering: designers layer distributed protocols (e.g., Paxos, 2PC) upon local storage stacks. Such layering abstracts details about the local storage stack to the layers above, easing development. I will show that such black-box layering, unfortunately, masks vital information, resulting in poor reliability. I will then demonstrate that reliability can be significantly improved by co-designing these layers.
In the first half of the talk, I will show how local storage-layer faults in one node can lead to serious vulnerabilities such as global data loss, corruption, and unavailability in many widely used systems. I then present CTRL, a new foundation that uses the co-design approach to avoid such problems, improving reliability. I implement CTRL in two practical systems and show that CTRL greatly improves resiliency to storage faults while incurring little performance overhead.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ram Alagappan is a postdoctoral researcher at the VMware Research Group. He earned his Ph.D., working with Professors Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau at the University of Wisconsin - Madison. His work has been published at top systems venues and has won three best paper awards (FAST 17, 18, and 20). His dissertation also won an honorable mention for the UW CS Best Dissertation. His open-source frameworks have had a practical impact: these tools have exposed more than 80 severe vulnerabilities across 20 widely used systems. Ideas from his work have been adopted by a financial database to make it more robust.
Host: Ramesh Govindan
Location: online only
Audiences: By invitation only.
Contact: Assistant to CS chair
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Computer Science General Faculty Meeting
Wed, Mar 23, 2022 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: TBD
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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AME Seminar
Wed, Mar 23, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: George Tynan, UC San Diego
Talk Title: Status and Outlook for Controlled Fusion as a Firm Zero-Carbon Energy Source
Abstract: Controlled fusion research has been pursued since the 1950s by most of the world's developed economies due to many attractive characteristics of this seemingly elusive technology. In 2021, inertial confinement fusion experiments at LLNL reached the threshold of fusion ignition while magnetic confinement experiments in the UK demonstrated that the ITER device nearing completion in France should, for the first time, produce a burning plasma in which fusion heating dominates the system. In parallel, a rapidly developing industry with $4B of private-sector funding has emerged and is pursuing a wide variety of approaches for controlled fusion. This talk will summarize the key elements of these developments, and sketch out the characteristics that fusion-based energy systems will need to demonstrate if they are to compete economically in the emerging zero-carbon energy system of the mid-century.
Biography: George Tynan studies the fundamental physics of turbulent transport in hot confined plasmas using both smaller scaled laboratory plasma devices as well as large scale fusion experiments located around the world. In addition, he is investigating how solid material surfaces interact with the boundary region of fusion plasmas, and how the materials are modified by that interaction. He is also interested in the larger issue of transitioning to a sustainable energy economy based upon a mixture of efficient end use technologies, large scale deployment of renewable energy sources, and incorporation of a new generation of nuclear technologies such as advanced fission and fusion reactor systems. He received his Ph.D. in 1991 from the Department of Mechanical, Aerospace, and Nuclear Engineering at the University of California, Los Angeles. He then spent several years studying the effect of sheared flows on plasma turbulence on experiments located in the Federal Republic of Germany and at Princeton Plasma Physics Laboratory, and worked in industry developing plasma sources for use in investigating the creation of submicron-scale semiconductor circuits. He joined the UCSD faculty in 1999 where he worked to establish a graduate program in plasma physics within the School of Engineering. He has served as Associate Vice Chancellor for Research, Associate Dean of Engineering, is co-founding Director of the UC San Diego Deep Decarbonization Initiative, and is currently Department Chair of Mechanical and Aerospace Engineering at the UC San Diego Jacobs School of Engineering.
Host: AME Department
More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09
Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09Location: James H. Zumberge Hall Of Science (ZHS) - 252
WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09
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A Study Break w/ Tesla: Weekly Series Feb 9 - April 13 (Virtual)
Wed, Mar 23, 2022 @ 06:00 PM - 06:45 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
A Study Break w/ Tesla is a series of professional workshops presented by the Hardware + Cell Engineering Internship Recruiting Team that will be offered on Wednesday evenings from February through April, 6:00 pm -6:45 pm.
Each event will offer a 25-minute presentation on a specific topic, followed by a 20-minute opportunity for participants to ask questions and network with the Tesla team.
Event: An Intro to Hardware Engineering | March 23 - RSVP HERE
Description: This session will provide an introduction to co-op and internship opportunities within the Hardware Engineering team at Tesla. Prior to attending, visit the Tesla career page for a base understanding of the intern opportunities available.
External employer-hosted events and activities are not affiliated with the USC Career Center. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participant's responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.eduLocation: RSVP in Viterbi Career Gateway
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Epirus Info Session and Tech Talk (On-Campus)
Wed, Mar 23, 2022 @ 06:00 PM - 07:30 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Epirus Info Session and Tech Talk (On-Campus)
This is a Viterbi-specific On-Campus session!
Location: Ronald Tutor Hall (RTH) Room 211
March 23rd 6:00 - 7:30 pm
Full Time & Internship Opportunities
No, my organization does not offer visa sponsorship
About Epirus: We design and build cutting-edge, high-powered microwave systems that can transform industries and the world. With our continued emphasis on innovation, we have created directed energy systems that surpass current capabilities that will aid in defense and climate change initiatives.
Come hear more about our innovative technology, like our
MOST POWERFUL PHASED ARRAY IN THE WORLD and how we just closed our Series C funding round valued at $1.35 Billion!
Our Info Session will be led by our Senior Vice President of Engineering and our Tech Talk will be led by one of our Senior Principal Electrical Engineers!Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Shift SC: Digital Well-being Workshop #2
Wed, Mar 23, 2022 @ 07:30 PM - 08:30 PM
Viterbi School of Engineering Student Organizations
Workshops & Infosessions
Come join Shift SC for its second digital well-being workshop, part of an interactive, engaging series that aims to highlight the impact of technology on mental health. Shift SC is a student organization that focuses on the human-centered responsibility of technology and serves as a space for students to promote awareness and discuss the implications of technology on society. Chipotle (vegetarian options included) will be catered to the first 40 individuals who join our event! Follow @shift.sc on Instagram for further updates!
Location: Social Sciences Building (SOS) - B46
Audiences: Everyone Is Invited
Contact: Denis Mac
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ECE-EP Seminar - Dejan Markovic, Thursday, March 24th at 10am in EEB 248 & via Zoom
Thu, Mar 24, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dejan Markovic, UCLA
Talk Title: The Future of Computing and Neuromodulation
Abstract: This talk will discuss future technologies addressing unmet needs in science, medicine, and engineering. Data-driven attentive computing requires runtime flexible and efficient hardware and software. Simple hardware leads to complex software (e.g. FPGA) and simple software leads to complex hardware (e.g. CPU). Runtime reconfigurable arrays (RTRAs) balance hardware and software to enable spatial and temporal flexibility for dynamic or uncertain environments. RTRA features multi-program tenancy, multi-size compile, and priority handling for >100x compute capacity gains over FPGA, and within 5x of (inflexible) hardware accelerators, as shown on a blind signal classification use case. Medical implants also require efficiency and flexibility, with heavily constrained size, weight and power, for novel clinical research and therapeutic systems. Despite notable clinical successes (e.g. Parkinson's disease), limitations in existing devices prevent them from expanding to other indications such as mental health or Alzheimer's disease. I will discuss the Neuro-stack, a versatile closed-loop system, verified in human subject experiments, towards miniaturized neural duplex of the future. These applications also reveal opportunities in system-level design automation to address design productivity and system assembly challenges.
Biography: Dejan MarkoviÄ is a Professor of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA). He is also affiliated with UCLA Bioengineering Department, Neuroengineering field. He completed the Ph.D. degree in 2006 at the University of California, Berkeley, for which he was awarded 2007 David J. Sakrison Memorial Prize. His current research is focused on implantable neuromodulation systems, domain-specific compute architectures, and design methodologies. Dr. MarkoviÄ co-founded Flex Logix Technologies, a semiconductor IP startup, in 2014, and helped build foundational technology of Ceribell, a medical device startup. He received an NSF CAREER Award in 2009. In 2010, he was a co-recipient of ISSCC Jack Raper Award for Outstanding Technology Directions. He also received 2014 ISSCC Lewis Winner Award for Outstanding Paper. Prof. Markovic is a Fellow of the IEEE.
Host: ECE-Electrophysics
More Information: Dejan Markovic Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium: Tesca Fitzgerald (Carnegie Mellon University) - Learning to address novel situations through human-robot collaboration
Thu, Mar 24, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tesca Fitzgerald, Carnegie Mellon University
Talk Title: Learning to address novel situations through human-robot collaboration
Series: CS Colloquium
Abstract: As our expectations for robots' adaptive capacities grow, it will be increasingly important for them to reason about the novel objects, tasks, and interactions inherent to everyday life. Rather than attempt to pre-train a robot for all potential task variations it may encounter, we can develop more capable and robust robots by assuming they will inevitably encounter situations that they are initially unprepared to address. My work enables a robot to address these novel situations by learning from a human teacher's domain knowledge of the task, such as the contextual use of an object or tool. Meeting this challenge requires robots to be flexible not only to novelty, but to different forms of novelty and their varying effects on the robot's task completion. In this talk, I will focus on (1) the implications of novelty, and its various causes, on the robot's learning goals, (2) methods for structuring its interaction with the human teacher in order to meet those learning goals, and (3) modeling and learning from interaction-derived training data to address novelty.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Tesca Fitzgerald is a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University. Her research is centered around interactive robot learning, with the aim of developing robots that are adaptive, robust, and collaborative when faced with novel situations. Before joining Carnegie Mellon, Dr. Fitzgerald received her PhD in Computer Science at Georgia Tech and completed her B.Sc at Portland State University. She is an NSF Graduate Research Fellow (2014), Microsoft Graduate Women Scholar (2014), and IBM Ph.D. Fellow (2017).
www.tescafitzgerald.com
Host: Heather Culbertson
Location: Olin Hall of Engineering (OHE) - 132
Audiences: By invitation only.
Contact: Assistant to CS chair
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Cognizant Information Session - Full Time & Internship Opportunities (Virtual)
Thu, Mar 24, 2022 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Cognizant Information Session - Full Time & Internship Opportunities (Virtual)
Thursday, March 24, 2022
11am - 12pm
Are you ready to start a career journey in IT and professional services?
Meet our recruiters, learn about Cognizant and the exciting opportunities for current/recent university graduates.
Cognizant is looking for driven, collaborative, and highly motivated individuals to join us. We currently have full time (Technology & Consulting) and internship opportunities for Juniors and Seniors.
Register now to learn more about Cognizant.
sign up link: https://app.jigsawinteractive.com/u/LSx2D4
External employer-hosted events and activities are not affiliated with the USC Viterbi Career Connections Office. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participants' responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.eduLocation: Virtual
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Virtual First-Year Admission Information Session
Thu, Mar 24, 2022 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.
Register Here!
Audiences: Everyone Is Invited
Contact: Viterbi Admission
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CS Colloquium: Seo Jin Park (MIT CSAIL) - Towards Interactive Big Data Processing Through Flash Burst Parallel Systems
Thu, Mar 24, 2022 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Seo Jin Park , MIT CSAIL
Talk Title: Towards Interactive Big Data Processing Through Flash Burst Parallel Systems
Series: CS Colloquium
Abstract: Today, many organizations store big data on the cloud and lease relatively small clusters of instances to run analytics queries, train machine learning models, and more. However, the exponential data growth, combined with the slowdown of Moore's law, makes it challenging (if not impossible) to run such big data processing tasks in real-time. Most applications run big data workloads on timescales of several minutes or hours and resort to complex, application-specific optimizations to reduce the amount of data processing required for interactive queries. This design pattern hurts developer productivity and restricts the scope of applications that can use big data. However, as we have many servers in a cloud datacenter, a natural question is "can we borrow thousands of servers briefly to accelerate big data processing enough to be interactive?"
In this talk, I'll share my vision to enable massively parallel data processing even for very short-duration (1-10 ms), which I call "flash bursts." This will empower interactive, real-time applications (e.g., cyber security attack defense, self-driving cars or drones, etc) to utilize much larger data than before. For this moonshot, I take a two-pronged approach. First, I restructure important big data applications (analytics and DNN training) so that they can run efficiently in a flash burst fashion. On this prong, the talk will focus on how I efficiently scaled distributed sorting to 100+ servers even for a 1-millisecond time budget. Second, I rebuild various layers in distributed systems to reduce overheads of flash burst scaling. On this prong, I will focus on how I removed the overheads of consistent replication.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Seo Jin Park is a postdoctoral researcher at MIT CSAIL. He received a Ph.D. in Computer Science from Stanford University in 2019, advised by John Ousterhout. He is broadly interested in distributed systems, focusing on low-latency systems: scaling low-latency data processing, optimizing consensus protocols (both standard and byzantine), suppressing tail-latencies, and building efficient performance debugging tools. His Ph.D. study was supported by Samsung Scholarship.
Host: Barath Raghavan
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: By invitation only.
Contact: Assistant to CS chair
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Mork Family Department Seminar - Jason Bates
Thu, Mar 24, 2022 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Jason Bates, University of Wisconsin-Madison
Talk Title: Catalysis beyond the binding site: reactions on crowded surfaces and in packed pores
Host: Professor A.Hodge
Location: Kaprielian Hall (KAP) - 147
Audiences: Everyone Is Invited
Contact: Heather Alexander
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CS Colloquium: Feras Saad (MIT) - Scalable Structure Learning and Inference via Probabilistic Programming
Thu, Mar 24, 2022 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Feras Saad, MIT
Talk Title: Scalable Structure Learning and Inference via Probabilistic Programming
Series: CS Colloquium
Abstract: Probabilistic programming supports probabilistic modeling, learning, and inference by representing sophisticated probabilistic models as computer programs in new programming languages. This talk presents efficient probabilistic programming-based techniques that address two fundamental challenges in scaling and automating structure learning and inference over complex data.
First, I will describe scalable structure learning methods that make it possible to automatically synthesize probabilistic programs in an online setting by performing Bayesian inference over hierarchies of flexibly structured symbolic program representations, for discovering models of time series data, tabular data, and relational data. Second, I will present fast compilers and symbolic analyses that compute exact answers to a broad range of inference queries about these learned programs, which lets us extract interpretable patterns and make accurate predictions in real time.
I will demonstrate how these techniques deliver state-of-the-art performance in terms of runtime, accuracy, robustness, and programmability by drawing on several examples from real-world applications, which include adapting to extreme novelty in economic time series, online forecasting of flu rates given sparse multivariate observations, discovering stochastic motion models of zebrafish hunting, and verifying the fairness of machine learning classifiers.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Feras Saad is a PhD candidate in Computer Science at MIT working at the intersection of programming languages, probabilistic machine learning, and computational statistics. His research is accompanied with a collection of popular open-source probabilistic programming systems used by collaborators at Intel, Takeda, Liberty Mutual, IBM, and the Bill & Melinda Gates Foundation for practical applications of structure learning and probabilistic inference. Feras' MEng thesis on probabilistic programming and data science has been recognized with the 1st Place Computer Science Thesis Award at MIT.
Host: Mukund Raghothaman
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: By invitation only.
Contact: Assistant to CS chair
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Meet Visa: Crypto Development Program (Virtual)
Thu, Mar 24, 2022 @ 04:00 PM - 05:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
RSVP HERE: https://www.wayup.com/i-Technology-j-Meet-Visa-Crypto-Development-Program-Visa-801319635388880/?utm_source=direct&utm_medium=vepromotion&utm_campaign=Visa&refer=visref_VE-Crypto-Development-Program-March-28179244
Join Cuy Sheffield (VP Crypto at Visa) and Alex Chiang (Sr. Manager, Crypto Strategy) for a discussion on Visa's Crypto Development Program opportunity, learn about exciting opportunities and programs in crypto and tips to prepare for a career in crypto!
The Crypto Development Program is an 18-month rotational development experience designed to build a fully fluent cryptocurrency team now and for the future. You will enjoy three distinct business rotations that provide you with practical experience of different areas within the emerging cryptocurrency ecosystem at Visa. These are Crypto Product, Crypto Solutions, and Digital Partnerships. The program supports Visa's mission to build a strong entry level pipeline of talent with deep subject matter expertise in the Crypto space. In addition to meaningful rotations, Associates are given training & development, mentoring, networking and leadership exposure.
Can't wait to see you there!
(Please note: In order to qualify, candidates must currently be completing a Bachelor's degree program and graduate between December 2021 - August 2022. Permanent authorization to work in the U.S. is a precondition of employment for this position. Visa will not sponsor applicants for work visas in connection with this position.)
About Us:
Visa is a payments technology company. The beating heart of our company is VisaNet, our global processing network that enables digital payments to happen securely and reliably in the blink of an eye. Our mission is to connect the world through the most innovative, reliable and secure payments network-enabling individuals, businesses and economies to thrive.
External employer-hosted events and activities are not affiliated with the USC Viterbi Career Connections Office. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participant's responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.eduLocation: Virtual
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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GRIDS Talk: Bridging the Gap Between Analyst and Business Types
Thu, Mar 24, 2022 @ 07:30 PM - 08:30 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Date:Thursday, March 24
Time: 7:30pm
Location: Virtual
Sign Up: https://forms.gle/qn74h13BZD4gRNzT6
With 10 years in the analytics industry in both B2B and B2C businesses like Dollar Shave Club, Workfront and Adobe, Brett Kobold will discuss important lessons he has learned in advancing his career. In this GRIDS Talk, analyst-types will learn techniques to translate the complexity of data analysis into tangible results while business/strategy types will learn how to communicate with analyst-types in order to get the best results. Walking out of this presentation you will have a better understanding how to work with both sides of this complex ecosystem.
Sign up here to receive the Zoom link!
External employer-hosted events and activities are not affiliated with the USC Viterbi Career Connections Office. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participant's responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.eduLocation: Virtual
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Grammar Tutorials
Fri, Mar 25, 2022 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
INDIVIDUAL GRAMMAR TUTORING FOR VITERBI UNDERGRADUATE AND GRADUATE STUDENTS
Meet one-on-one with Viterbi faculty, build your grammar skills, and take your writing to the next level!
Viterbi faculty from the Engineering in Society Program (formerly the Engineering Writing Program) will help you identify and correct recurring grammatical errors in your academic writing, cover letters, resumes, articles, presentations, and dissertations.
Bring your work, and let's work together to clarify your great ideas!
Contact helenhch@usc.edu with questions.
Location: Zoom
Audiences: Graduate and Undergraduate Students
Contact: Helen Choi
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CS Colloquium: Chuang Gan (MIT-IBM Watson AI Lab) - Neuro-Symbolic AI for Machine Intelligence
Fri, Mar 25, 2022 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Chuang Gan, MIT-IBM Watson AI Lab
Talk Title: Neuro-Symbolic AI for Machine Intelligence
Series: CS Colloquium
Abstract: Machine intelligence is characterized by the ability to understand and reason about the world around us. While deep learning has excelled at pattern recognition tasks such as image classification and object recognition, it falls short of deriving the true understanding necessary for complex reasoning and physical interaction. In this talk, I will introduce a framework, neuro-symbolic AI, to reduce the gap between machine and human intelligence in terms of data efficiency, flexibility, and generalization. Our approach combines the ability of neural networks to extract patterns from data, symbolic programs to represent and reason from prior knowledge, and physics engines for inference and planning. Together, they form the basis of enabling machines to effectively reason about underlying objects and their associated dynamics as well as master new skills efficiently and flexibly.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Chuang Gan is a principal research staff member at MIT-IBM Watson AI Lab. He is also a visiting research scientist at MIT, working closely with Prof. Antonio Torralba and Prof. Josh Tenenbaum. Before that, he completed his Ph.D. with the highest honor at Tsinghua University, supervised by Prof. Andrew Chi-Chih Yao. His research interests sit at the intersection of computer vision, machine learning, and cognitive science. His research works have been recognized by Microsoft Fellowship, Baidu Fellowship, and media coverage from BBC, WIRED, Forbes, and MIT Tech Review. He has served as an area chair of CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, ACL, and an associate editor of IEEE Transactions on Image Processing.
Host: Ram Nevatia
Location: online only
Audiences: By invitation only.
Contact: Assistant to CS chair
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ECE Seminar: Distributed Systems: Rigorous Theoretical Foundations Unlock Promising Gains
Fri, Mar 25, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Mohammad Ali Maddah-Ali, Research Scientist, Department of Electrical Engineering, Stanford University
Talk Title: Distributed Systems: Rigorous Theoretical Foundations Unlock Promising Gains
Abstract: Over the last twenty years, we have witnessed several revolutionary technologies, from communication networks to learning platforms to blockchains, that have profoundly changed our daily lives. Often, these platforms are modeled, designed, and operated based on intuition and folk wisdom. In this talk, we challenge some of those common beliefs. We show that by meticulously elaborating the key performance bottlenecks from first principles, we can propose counterintuitive solutions grounded in rigorous analysis that unlock considerable scaling gains in several areas:
1) In wireless communications, the delay in acquiring channel information is a significant bottleneck in supporting multiple users at a time. Contrary to popular belief, we demonstrate that even completely outdated channel information can be used for interference management and enabling simultaneous communications, thus alleviating the bottleneck of channel training.
2) In content delivery networks, folk wisdom design is to maximize the likelihood of serving a request from the local cache (hit rate); thus, the performance is bottlenecked by the size of an individual cache. We propose a fundamentally new approach with a gain that scales with the sum of the cache sizes in the network, rather than an individual cache size.
3) In distributed learning, we demonstrate that training with combined data samples (i.e., erasure-coded samples), rather than raw samples, can significantly improve the reliability and convergence rate. Moreover, we highlight the surprising role of approximation theory in circumventing a major bottleneck in designing practical coded training procedures.
We conclude with promising directions for further investigation: in particular, the challenges in adding decentralized trust and accountability to these systems, to place control over them back in the hands of individuals rather than big corporations.
Biography: Mohammad Ali Maddah-Ali received the B.Sc. degree from the Isfahan University of Technology, the M.Sc. degree from the University of Tehran, and the Ph.D. degree from the Department of Electrical and Computer Engineering, University of Waterloo, Canada. From 2008 to 2010, he was a Postdoctoral Fellow in the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley. From 2010 to 2020, he was working at Bell Labs, Holmdel, NJ, as a communication network research scientist. He also worked as a faculty member at the Department of Electrical Engineering, Sharif University of Technology. Currently, he is a research scientist at the Department of Electrical Engineering, Stanford University.
Dr. Maddah-Ali is a recipient of several awards including the IEEE International Conference on Communications (ICC) Best Paper Award in 2014, the IEEE Communications Society and IEEE Information Theory Society Joint Paper Award in 2015, and the IEEE Information Theory Society Paper Award in 2016. He is currently serving as an associate editor for the IEEE Transactions on Information Theory and a lead editor for The IEEE Journal on Selected Areas in Information Theory.
Host: Dr. Keith Chugg, chugg@usc.edu
Webcast: https://usc.zoom.us/j/98149159985?pwd=cWFsVnRkZXRKcTlWYllMcy9Rempmdz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/98149159985?pwd=cWFsVnRkZXRKcTlWYllMcy9Rempmdz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Advanced Manufacturing Seminar
Fri, Mar 25, 2022 @ 10:00 AM - 11:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Mostafa Bedewy, University of Pittsburgh
Talk Title: Manufacturing for the Future: Carbon-Based Flexible Neural Interfaces
Abstract: Abstract: Nanocarbons like graphene, carbon nanotubes (CNTs), and nanofibers are promising for various applications including advanced electronic devices, novel energy systems, and next-generation healthcare diagnostics. This is owing to the excellent physical, chemical and electrochemical properties arising from the ordered atomic structure, the hierarchical nanoscale morphology, and tunable chemistry of nanocarbons. In particular, high surface area carbon electrodes for biosensors and neural interfaces have consistently been shown to have superior performance when compared to state-of-the-art metal electrodes. Nevertheless, major manufacturing challenges still hinder our ability to scalably produce nanocarbon-based electrodes with tailored morphology and surface chemistry, especially on flexible substrates. Unlike different transfer technique of CVD-grown nanocarbons, this talk will focus on a unique bottom-up approach for directly growing different types of graphenic nanocarbons on polymer films by laser irradiation. The speaker will show how this direct-write process, often referred to as laser-induced graphene (LIG), can be controlled to produce spatially-varying morphologies and chemical compositions of LIG electrodes, by leveraging gradients of laser fluence. Moreover, a method will be introduced to control the heteroatom doping of these LIG electrodes based on controlling the molecular structure of the polymer being lased. Finally, a demonstration of these functional LIG electrodes as electrochemical biosensors will be presented for the detection of the neurotransmitter dopamine with nanomolar sensitivity.
Biography: Dr. Mostafa Bedewy leads the NanoProduct Lab at the University of Pittsburgh. His research interests include carbon nanomaterials, laser processing, nanomanufacturing and micromanufactuing, chemical vapor deposition (CVD), and biology-assisted manufacturing. Dr. Bedewy received the Frontiers of Materials Award from the Minerals, Metals and Materials Society (TMS) in 2022, Outstanding Young Investigator Award from the Institute of Industrial and Systems Engineers Manufacturing and Design (IISE M&D) Division in 2020, Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers (SME) in 2018, the Ralph E. Powe Junior Faculty Enhancement Award from the Oak Ridge Associated Universities (ORAU) in 2017, the Robert A. Meyer Award from the American Carbon Society in 2016, and many other prestigious awards.
Host: Center for Advanced Manufacturing
More Info: https://usc.zoom.us/webinar/register/WN_OMywkH2iRSmzYMtYVM-frQ
Webcast: https://usc.zoom.us/webinar/register/WN_OMywkH2iRSmzYMtYVM-frQWebCast Link: https://usc.zoom.us/webinar/register/WN_OMywkH2iRSmzYMtYVM-frQ
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://usc.zoom.us/webinar/register/WN_OMywkH2iRSmzYMtYVM-frQ
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PhD Defense - Chaoyang He
Fri, Mar 25, 2022 @ 11:00 AM - 12:30 PM
Thomas Lord Department of Computer Science
University Calendar
Time: 11AM - 12:30PM, March 25th, 2022
Committee Members: Salman Avestimehr (Chair), Mahdi Soltanolkotabi, Murali Annavaram, Ram Nevatia, Xiang Ren
Zoom Link: https://usc.zoom.us/my/usc.chaoyanghe
Title: Federated and Distributed Machine Learning at Scale: From Systems to Algorithms to Applications
Abstract:
Federated learning (FL) is a machine learning paradigm that many clients (e.g. mobile/IoT devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. It has shown huge potential in mitigating many of the systemic privacy risks, regulatory restrictions, and communication costs resulting from traditional, centralized machine learning and data science approaches in healthcare, finance, smart city, autonomous driving, and the Internet of things. Though it is promising, landing FL into trustworthy data-centric AI infrastructure faces many realistic challenges from learning algorithms (e.g., data heterogeneity, label deficiency) and distributed systems (resource constraints, system heterogeneity, security, privacy, etc.), requiring interdisciplinary research in machine learning, distributed systems, and security/privacy. Driven by this goal, this thesis focuses on scaling federated and distributed machine learning end-to-end, from algorithms to systems to applications.
In the first part, we focus on the design of the distributed system for federated and distributed machine learning. We propose FedML, a widely adopted open-source library for federated learning, and PipeTransformer, which leverages automated elastic pipelining for efficient distributed training of Transformer models. FedML supports three computing paradigms: on-device training using a federation of edge devices, distributed training in the cloud that supports exchanging of auxiliary information beyond just gradients, and single-machine simulation of a federated learning algorithm. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). In PipeTransformer, we design an adaptive on the fly freeze algorithm that can identify and freeze some layers gradually during training, and an elastic pipelining system that can dynamically allocate resources to train the remaining active layers. More specifically, PipeTransformer automatically excludes frozen layers from the pipeline, packs active layers into fewer GPUs, and forks more replicas to increase data-parallel width.
In the second part, we propose a series of algorithms to scale up federated learning by breaking many aforementioned constraints, such as FedGKT, an edge-cloud collaborative training for resource-constrained clients, FedNAS, a method towards automation on invisible data via neural architecture search, SpreadGNN, effective training on decentralized topology, SSFL, tackling label deficiency via personalized self-supervision, and LightSecAgg, the lightweight and versatile secure aggregation. Most algorithms are compatible with each other. Specially, we unified all implementations under the FedML framework. Therefore, under the complex constraints of the real world, the orchestration of these algorithms has the potential to greatly enhance the scalability of federated learning.
Finally, we further propose FedML Ecosystem, which is a family of open research libraries to facilitate federated learning research in diverse application domains. FedNLP (Natural Language Processing), FedCV (Computer Vision), FedGraphNN (Graph Neural Networks), and FedIoT (Internet of Things). Compared with TFF and LEAF, FedNLP and FedCV greatly enrich the diversity of data sets and learning tasks. FedNLP supports various popular task formulations in the NLP domain, such as text classification, sequence tagging, question answering, seq2seq generation, and language modeling. FedCV can help researchers evaluate the three most representative tasks: image classification, image segmentation, and object detection. Moreover, FedGraphNN is the first FL research platform for analyzing graph-structured data using Graph Neural Networks in a distributed computing manner, filling the gap between federated learning and the data mining field. Going beyond traditional AI applications, FedIoT further extends FL to perform in wireless communication (e.g., 5G) and mobile computing (e.g., embedded IoT devices such as Raspberry PI, smartphones running on Android OS).
WebCast Link: https://usc.zoom.us/my/usc.chaoyanghe
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Astani Civil and Environmental Engineering Seminar
Fri, Mar 25, 2022 @ 12:30 PM - 01:30 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Kristopher McNeill, Department of Environmental Systems Science, ETH Zurich
Talk Title: An Environmental Chemist View of Biodegradable Plastics
Abstract: Contamination of the environment with plastic is a long-recognized problem, but in recent years, there has been a remarkable increase in public attention and outcry regarding plastic pollution. The low cost and durability of plastic materials, which make them desirable for many applications, are the same factors that contribute to their accumulation. The low cost lowers the barrier to short- term and single use applications and the durability means that, once in the environment, these materials are highly persistent. On this latter point, there is a growing interest and market for non- persistent, biodegradable plastic materials, which could help the problem of accumulation of plastic in the environment. This presentation will focus on several key questions about these alternative biodegradable materials: How do we know that a material is really biodegrading instead of just breaking down into microplastic? How does the receiving environment affect biodegradability? Are there applications where biodegradable plastics are viable alternatives to conventional plastics? What are the challenges that we face from an environmental chemistry perspective?
Biography: Prof. Kris McNeill received his B.A. in Chemistry from Reed College (Portland, Oregon) in 1992 and his Ph.D. in Chemistry from the University of California, Berkeley in 1997. At Berkeley, he was co-advised by Professors Robert Bergman and Richard Andersen. Following his PhD, he switched his research focus from organometallic chemistry to environmental chemistry. He was a postdoctoral researcher at MIT from 1997 to 1999 with Prof. Philip Gschwend in the department of Civil and Environmental Engineering. McNeill began his independent career as a faculty member at the University of Minnesota in the Department of Chemistry, holding ranks of Assistant Professor (2000-2006) and Associate Professor (2007-2009). In 2009, Kris McNeill joined the faculty of ETH Zurich, where he continues to apply physical organic chemistry to the study of environmental processes.
Host: Dr. Daniel McCurry
More Info: https://usc.zoom.us/j/93390473354 Meeting ID: 933 9047 3354 Passcode: 527888
Location: Ray R. Irani Hall (RRI) - 421
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
Event Link: https://usc.zoom.us/j/93390473354 Meeting ID: 933 9047 3354 Passcode: 527888
