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Events for September
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CS Colloquium: Guy Hoffman (Cornell University) - Designing Robots for Collaboration and Companionship
Tue, Sep 10, 2019 @ 01:30 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Guy Hoffman, Cornell University
Talk Title: Designing Robots for Collaboration and Companionship
Series: Computer Science Colloquium
Abstract: Designing robots for human interaction is a multifaceted challenge involving the robot's intelligent behavior, physical form, mechanical structure, and interaction schema. The Cornell Human-Robot Collaboration and Companionship (HRC^2) lab develops and studies human-centered robots, combining methods from AI, Mechanical Design, and Human-Computer Interaction. This talk focuses on four recent projects from our lab: A collaborative wearable robotic "third arm", a robot that helps human designers make better decisions, an emotive robotic skin that can produce goosebumps and spikes, and an open-source social robotics construction kit that is based on craft materials.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Guy Hoffman is an Assistant Professor and the Mills Family Faculty Fellow in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Prior to that he was Assistant Professor at IDC Herzliya and co-director of the IDC Media Innovation Lab. Hoffman holds a Ph.D from the MIT Media Lab. He heads the Human-Robot Collaboration and Companionship (HRC^2) group, studying the algorithms, interaction schema, and designs enabling close interactions between people and personal robots in the workplace and at home. Among others, Hoffman developed the world's first human-robot joint theater performance, and the first real-time improvising human-robot Jazz duet. His research papers won several top academic awards, including Best Paper awards at HRI and robotics conferences in 2004, 2006, 2008, 2010, 2013, 2015, 2018, and 2019. His TEDx talk is one of the most viewed online talks on robotics, watched more than 3 million times.
Host: Computer Science Department
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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Computer Science General Faculty Meeting
Wed, Sep 18, 2019 @ 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: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Thesis Proposal - Ryan Julian
Thu, Sep 19, 2019 @ 12:00 PM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: The Adaptation Base Case: Understanding the Challenge of Continual Robot Learning
Date/Time: Thursday, September 19th 12pm
Location: RTH 406
Candidate: Ryan Julian
Committee: Prof. Gaurav Sukhatme (adviser), Prof. Joseph Lim, Prof. Heather Culbertson, Prof. Stefanos Nikolaidis, Prof. SK Gupta, Dr. Karol Hausman
Abstract:
Much of the promise of reinforcement learning (RL) for robotics is predicated on the idea of hands-off continual improvement: that these systems will be able to use machine learning to improve their performance after deployment. Without this property, RL does not compare very favorably to hand-engineered robotics. The research community has successfully shown that RL can train agents which are at least as good, or better than, hand-engineered controllers after a single large-scale up-front training process. Furthermore, multi-task and meta-learning has research shown that we can learn controllers which adapt to new tasks, by reusing data and models from related tasks. What is not well-understood is whether we can make this adaptation process continual. The overall schematic off-policy multi-task RL algorithms suggests these might make good continual learners, but we don't if know that's actually the case. In this presentation, I'll review the recent history of adaptive robot learning research, and enumerate the most important unanswered questions which prevent us from designing continual multi-task learners. I'll then outline a research agenda which will answer those questions, to provide a road map to continual multi-task learning for robotics.
Location: 406
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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CS Tech Talk: Lyft Level 5 Tech Talk
Thu, Sep 19, 2019 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Anjie Liang, Robert Pinkerton, Alice Chuang, Lyft Level 5
Talk Title: Lyft Level 5 Tech Talk
Series: Computer Science Colloquium
Abstract: Come learn more about our Lyft Core and Level 5 self-driving teams!
Swag will be provided!
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: For the tech talk, we welcome the following speakers:
Anjie Liang, Software Engineer
Anjie is a software engineer on Data Infrastructure for Level 5, a team responsible for indexing and serving all the data that is collected on the autonomous vehicles. Before Lyft, she was completing her undergrad at the University of Texas at Austin. Considering the large amounts of data that is collected on the cars every day, and the many distributed systems needed to process that data, Anjie's first year of working full time has been full of learning opportunities and interesting challenges.
Robert Pinkerton, Hardware Engineer
Rob is a systems engineer at Lyft Level 5, a team responsible for the architecture and requirements definition of our self-driving cars. Before Lyft, he was a systems engineer at SpaceX where he worked on various aspects of the Falcon 9 and Falcon Heavy Launch vehicles, including launching a car into space. Rob has performed graduate study in Systems Engineering and Electrical Engineering at Cornell and Stanford University respectively. He is extremely passionate about turning complex systems into products that improve our lives in a meaningful and sustainable way.
Alice Chuang, Software Engineer
Alice is a Software Engineer on Mapping Algo for Level 5, a team that uses Computer Vision and Machine Learning to leverage the data to build maps for autonomous vehicles. Alice graduated from Columbia in the City of New York and after interning last summer, she returned as a full time engineer at Level 5! So far, Alice's experiences at Lyft have been very insightful and exciting.
Host: Computer Science Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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Center for Knowledge-Driven Interdisciplinary Data Science (CKIDS)
Mon, Sep 23, 2019 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science, Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Odd Erik Gundersen, Adjunct Associate Professor, Norwegian University of Science and Technology
Talk Title: Reproducibility in AI: Standing on the Feet of Giants
Series: Invited Lecture Series
Abstract: First, we need a common understanding of what reproducibility is. Then, I will talk about some of the challenges we face related to reproducing empirical AI research and give some examples of studies that have tried to reproduce results from AI and machine learning. Having this understanding we can identify what we need to do to improve the reproducibility of our own experiments.
Biography: Odd Erik Gundersen is an adjunct associate professor at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, where he teaches courses and supervises master students in AI. He received his PhD from the Norwegian University of Science and Technology. Gundersen has applied AI in the industry, mostly for startups, since 2006. He has conducted several analysis of reproducibility in the artificial intelligence and machine learning literature, and has developed guidelines for reproducibility in data science. Currently, he investigates how AI can be applied in the renewable energy sector and for driver training.
For more information and future speakers, please visit:
https://sites.usc.edu/ckids/events/invited-lecture-series/
Host: Yolanda Gil, Director of Center for Knowledge-Driven Interdisciplinary Data Science (CKIDS)
More Info: https://sites.usc.edu/ckids/events/invited-lecture-series/
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
Event Link: https://sites.usc.edu/ckids/events/invited-lecture-series/
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Computer Science General Faculty Meeting
Wed, Sep 25, 2019 @ 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: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
