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Events for February 25, 2021
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PhD Thesis Proposal - Victor Ardulov
Thu, Feb 25, 2021 @ 09:00 AM - 10:30 AM
Thomas Lord Department of Computer Science
University Calendar
Title: Control Theoretic Framework for Measuring, Modeling, and Regulating Human Interaction
Committee:
Shrikanth Narayanan (chair)
Fei Sha
Gale Lucas
David Traum
Tom D Lyon
abstract:
Human interaction is a vital component to a persons' development and well-being. These interactions enable us to over come obstacles and find resolutions that an individual might not be able to. This subject is particularly well studied in the domains of human psychology, where human behavior is diagnostically categorized and the interaction can be utilized in order to improve somebody's health.
Prior work has explored the use of computational models of human behavior to aide in the diagnostic assessment of behavioral patterns. Most recently, novel machine learning methods and access data has invited the to study the dynamics of human interaction on a more granular time-resolution. These dynamics have been used to identify specific moments during interactions that are relevant to the over all assessment of a individuals behavior with respect to their interlocutor. By reformulating this system from the perspective of an operator that can be controlled, it invites the possibility to predict how an individual would react to a specific input from their partner, which itself lends the opportunity to plan out interventions and probes more effectively.
This thesis proposal presents a formulation of human interaction as a control theoretic problem and demonstrates how these frameworks can be utilized to gain insight into improving desired outcomes. In support of the thesis, we will present the application of these techniques to the domain of child forensic interviewing.
Presentation on: February 25th 9 a.m. join via Zoom: https://usc.zoom.us/j/93374500380?pwd=ZHh6UDVXV0NTei9OS3h6TlZCeitDUT09
WebCast Link: https://usc.zoom.us/j/93374500380?pwd=ZHh6UDVXV0NTei9OS3h6TlZCeitDUT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Undergraduate Advisement Drop-in HoursThu, Feb 25, 2021 @ 01:30 PM - 02:30 PM
Thomas Lord Department of Computer Science
Workshops & Infosessions
Do you have a quick question? The CS advisement team will be available for drop-in live chat advisement for declared undergraduate students in our four majors during the spring semester on Tuesdays, Wednesdays, and Thursdays from 1:30pm to 2:30pm Pacific Time. Access the live chat on our website at: https://www.cs.usc.edu/chat/
Location: Online
Audiences: Undergrad
Contact: USC Computer Science
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CS Distinguished Lecture: Rada Mihalcea (University of Michigan) - Moving Away from One-Size-Fits-All Natural Language Processing
Thu, Feb 25, 2021 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rada Mihalcea, University of Michigan
Talk Title: Moving Away from One-Size-Fits-All Natural Language Processing
Series: Computer Science Distinguished Lecture Series
Abstract: The typical approach in natural language processing is to use one-size-fits-all representations, obtained from training one model on very large text collections. While this approach is effective for those people whose language style is well represented in the data, it fails to account for variations between people, and often leads to decreased performance for those in the minority. In this talk, I will challenge the one-size-fits-all assumption, and show that (1) we can identify words that are used in significantly different ways by speakers from different cultures; and (2) we can effectively use information about the people behind the words to build better natural language processing models.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_05SDnJisSNa9_iJj-5PLfw
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Rada Mihalcea is the Janice M. Jenkins Collegiate Professor of Computer Science and Engineering at the University of Michigan and the Director of the Michigan Artificial Intelligence Lab. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Journal of Artificial Intelligence Research, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a program co-chair for EMNLP 2009 and ACL 2011, and a general chair for NAACL 2015 and *SEM 2019. She currently serves as ACL President. She is the recipient of a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009), an ACM Fellow (2019) and a AAAI Fellow (2021). In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.
Host: Xiang Ren
Webcast: https://usc.zoom.us/webinar/register/WN_05SDnJisSNa9_iJj-5PLfwLocation: Online Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_05SDnJisSNa9_iJj-5PLfw
Audiences: Everyone Is Invited
Contact: Computer Science Department
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
