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Events for March 21, 2022
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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
