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Events for September 19, 2019

  • NL Seminar- Allen NLP Tools Workshop

    Thu, Sep 19, 2019 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Seraphina Goldfarb-Tarrant, USC/ISI

    Talk Title: AllenNLP Tools Workshop

    Series: Natural Language Seminar

    Abstract: This is a practical talk that highlights some of the areas where AllenNLP the NLP research library excels, and gives a look at new features being released. It will focus on the ways that use of the library can enable reproducibility, interpretability, and visualizations.



    Biography: Seraphina Goldfarb-Tarrant is a Research Programmer at ISI, doing work in NLG. She finished her Master's at the University of Washington, and is beginning her PhD at the University of Edinburgh.


    Host: Emily Sheng

    More Info: https://nlg.isi.edu/nl-seminar

    Webcast: https://bluejeans.com/s/OUQy4/

    Location: Information Science Institute (ISI) - CR #689

    WebCast Link: https://bluejeans.com/s/OUQy4/

    Audiences: Everyone Is Invited

    Posted By: Peter Zamar

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  • Thesis Proposal - Ryan Julian

    Thu, Sep 19, 2019 @ 12:00 PM - 01:00 PM

    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

    Posted By: 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

    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

    Posted By: Computer Science Department

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