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Events for the 3rd week of January
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CS Colloquium: Hal Daume (University of Maryland) - Learning Language through Interaction
Mon, Jan 13, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Hal Daume, University of Maryland
Talk Title: Learning Language through Interaction
Series: CS Colloquium
Abstract: To have the broadest possible positive impact, machine learning-based natural language processing systems must be able to (a) learn when limited training data exists for the target tasks, languages (and varieties), and domains of interest, and (b) identify and mitigate potential harms in their use, in particular arising from the signals on which they are trained. I will first present new algorithms and applications for learning language processing systems through interaction with people, where implicit and/or explicit user feedback drives learning. I will then discuss learning challenges around "fairness" and how such interactive learning mechanisms can help address them.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Hal Daumé III is a Perotto Chair Professor in Computer Science and Language Science at the University of Maryland, and a Senior Principal Researcher at Microsoft Research. His research focuses on developing learning algorithms for natural language processing, with a focus on interactive learning methods, and techniques for mitigating harms that can arise from automated systems. He earned his Ph.D. from the University of Southern California in 2006, was an inaugural diversity and inclusion co-chair at NeurIPS 2018, is an action editor for TACL, and is program co-chair for ICML 2020.
Host: Fei Sha
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Marine Carpuat (University of Maryland) - Divergences in Neural Machine Translation
Tue, Jan 14, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Marine Carpuat, University of Maryland
Talk Title: Divergences in Neural Machine Translation
Series: CS Colloquium
Abstract: Despite the explosion of online content worldwide, much information remains isolated by language barriers. While deep neural networks have dramatically improved machine translation (MT), truly breaking language barriers requires not only translating accurately, but also understanding what is said and how it is said across languages. I will first challenge the assumption that translation always preserves meaning, and discuss how to automatically detect when the meaning of a translation diverges from its source. Next, I will show how modeling divergences between MT model hypotheses and reference human translations can improve MT. Finally, I will argue that translation does not necessarily need to preserve all properties of the input and introduce a family of models that let us tailor translation style while preserving input meaning. Taken together, these results illustrate how modeling divergences from common assumptions about translation data can not only improve MT, but also broaden the framing of MT to make it more responsive to user needs.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Marine Carpuat is an Assistant Professor in Computer Science at the University of Maryland. Her research focuses on multilingual natural language processing and machine translation. Before joining the faculty at Maryland, she was a Research Scientist at the National Research Council Canada. She received a PhD in Computer Science and a MPhil in Electrical Engineering from the Hong Kong University of Science & Technology, and a Diplome d'Ingenieur from the French Grande Ecole Supelec. She is the recipient of an NSF CAREER award, research awards from Google and Amazon, best paper awards at *SEM and TALN, and an Outstanding Teaching Award.
Host: Yan Liu
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Computer Science General Faculty Meeting
Wed, Jan 15, 2020 @ 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: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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CS Colloquium: Arjun Guha (University of Massachusetts Amherst) - New Abstractions for New Programming Platforms
Thu, Jan 16, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Arjun Guha, University of Massachusetts Amherst
Talk Title: New Abstractions for New Programming Platforms
Series: CS Colloquium
Abstract: Programmers today have to wrestle with a wide variety of programming platforms. However, traditional programming abstractions and tools were designed for an earlier era, and are often ineffective today, e.g., when building scalable cloud services, reliable robot controllers, and robust web applications. To address these kinds of challenges, we need to rethink the abstractions and tools that programmers employ.
In this talk, we first discuss problems that arise in "serverless computing", which is a new approach to cloud computing. We carefully define an operational semantics for serverless computing, which we then use to 1) formulate correctness criteria, 2) design new modularity mechanisms, and 3) develop a serverless computing accelerator that uses language-based sandboxing and speculative optimizations.
Next, we present fundamental limitations of the web programming model, which affect the design of JavaScript, and make it hard to build robust programming tools that run in web browsers. We address this problem by extending JavaScript with first-class continuations, and efficiently compile the extended language to run in unmodified web browsers.
Finally, we present challenges that arise when debugging robot controllers, and why traditional debugging tools do not help. We present an interactive program repair tool, which uses a MAX-SMT solver to search for corrections to a robot state machine, given a small number of human-provided inputs.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Arjun Guha is an associate professor of Computer Science at the University of Massachusetts Amherst. Using the tools and techniques of programming languages, his research addresses security, reliability, and performance problems in web applications, systems, networking, and robotics. His work has received an ACM SIGPLAN Most Influential Paper Award, an ACM SIGPLAN Distinguished Paper Award, an ACM SIGPLAN Research Highlight, and a Google Faculty Research Award.
Host: Ramesh Govindan
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
