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Events for March 04, 2020

  • CS Colloquium: Peng Qi (Stanford University) - Explainable and Efficient Knowledge Acquisition from Text

    Wed, Mar 04, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Peng Qi, Stanford University

    Talk Title: Explainable and Efficient Knowledge Acquisition from Text

    Series: CS Colloquium

    Abstract: Human languages have served as the media for our knowledge over generations. With the rise of the digital world, making use of the knowledge that is encoded in text has become unprecedentedly important yet challenging. In recent years, the NLP community has made great progress towards operationalizing textual knowledge by building accurate systems that answer factoid questions. However, largely relying on matching local text patterns, these systems fall short at their ability to perform complex reasoning, which limits our effective use of textual knowledge. To address this problem, I will first talk about two distinct approaches to enable NLP systems to perform multi-step reasoning that is explainable to humans, through extracting facts from natural language and answering multi-step questions directly from text. I will then demonstrate that beyond static question answering with factoids, true informativeness of answers stems from communication. To this end, I will show how we lay the foundation for reasoning about latent information needs in conversations to effectively exchange information beyond providing factoid answers.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Peng Qi is a Computer Science PhD student at Stanford University. His research interests revolve around building natural language processing systems that better bridge between humans and the large amount of textual information we are engulfed in. He is excited about building scalable and explainable AI systems, and has worked on extracting knowledge representations from text, question answering involving complex reasoning, and multi-lingual NLP.

    Host: Xiang Ren

    Location: Ronald Tutor Hall of Engineering (RTH) - 109

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar

    Wed, Mar 04, 2020 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Jonathan Sprinkle, University of Arizona

    Talk Title: Cyber-Physical Systems for Vehicle-in-the-Flow Traffic Flow Control

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: This talk describes previous and ongoing research in traffic flow control that involve the University of Arizona CAT Vehicle Testbed. The focus of the research is real-time control of vehicle velocity in order to effect the velocity of other vehicles in the flow. Research and results are told through the lens of several physical validation experiments. The first experiment explores how to dampen emerging waves in traffic that are due to congestive effects. This experiment grew out of theory of how traffic flow could be improved through sparse velocity control (e.g., ~5% of the vehicles) in the flow. The second experiment examines an analogous case, where 100% of the vehicles are controlled, though this time using off-the-shelf (rather than customized) cruise control algorithms. The talk will examine the hypotheses, methods, and results of these experiments, and explore the theory and motivation for the research as a means to provide insights into the obtained results. The research was sponsored by the National Science Foundation under award CNS-1446435, the Department of Energy through contract DE-EE0008872, and is collaborative work with Benedetto Piccoli, Benjamin Seibold, Dan Work, and Alexandre Bayen.

    Biography: Dr. Jonathan Sprinkle is the Litton Industries John M. Leonis Distinguished Associate Professor of Electrical and Computer Engineering at the University of Arizona. In 2013 he received the NSF CAREER award, and in 2009, he received the UA's Ed and Joan Biggers Faculty Support Grant for work in autonomous systems. His work has an emphasis for industry impact, and he was recognized with the UA "Catapult Award" by Tech Launch Arizona in 2014, and in 2012 his team won the NSF I-Corps Best Team award. From 2017-2019 he served as a Program Director at the National Science Foundation in the division of Computer and Networked Systems. His research interests and experience are in cyber-physical systems control and engineering, and he teaches courses ranging from systems modeling and control to mobile application development and software engineering.

    Host: Paul Bogdan, pbogdan@usc.edu

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Posted By: Talyia White

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  • AME Seminar

    Wed, Mar 04, 2020 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Greg Ver Steeg, USC

    Talk Title: Challenges and Opportunities for Accelerating Scientific Discovery with Deep Learning

    Abstract: The successes of neural networks in computer vision and natural language processing have not easily translated into breakthroughs in other scientific domains. I will discuss some of the principles behind learning representations of data with deep learning and how we have adapted these ideas to study problems like gene expression, neuroimaging, and clinical health records. I will conclude with a speculative discussion about whether these methods can benefit domains that traditionally rely on large-scale numerical simulations like computational fluid dynamics.

    Biography: Dr. Greg Ver Steeg is a Research Lead at ISI and Research Associate Professor in USCs CS department. He has slowly transitioned from PhD research at Caltech on detecting quantum entanglement to his current work on detecting hidden variables in more diverse domains using information theory and machine learning. His work has been recognized with an AFOSR Young Investigator Award and an Amazon Research Award.

    Host: AME Department

    More Info: https://ame.usc.edu/seminars/

    Location: James H. Zumberge Hall Of Science (ZHS) - 159

    Audiences: Everyone Is Invited

    Posted By: Tessa Yao

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