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Events for April 27, 2023
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PhD Thesis Proposal - Iordanis Fostiropoulos
Thu, Apr 27, 2023 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Iordanis Fostiropoulos
Committee: L. Itti (Chair), M. Soleymani, S. Nikolaidis, N. Schweighofer (Outside Member)
Title: Towards Learning Generalizable Representations
Abstract: Current work in Machine Learning (ML) research lack systematic tools and methods for evaluating the performance of a ML model on the ability to generalize beyond the train set; where the current accepted practice is on the evaluation of the loss on a test set. Work in ML for defining generalization is abstract and based on anthropocentric measures[65]. While practical metrics in evaluating generalization are poor indicators where there are trade-offs between the metric (such as loss) and the performance of the Deep Neural Network (DNN) to Out-of Distribution examples, such as robustness-accuracy trade-off or hallucinations of transformer models. While algorithmic solutions are often in the form of paradigm shifts that are ad-hoc and domain specific with a lack of consensus in literature. Our work focus on generalization as it pertains on evaluating and improving current ML systems, as opposed to proposing a paradigm shift, where we address three evaluation settings of generalization. First, the generalization of a DNN to learn generalizable representations useful beyond the task it was trained on. Second, the generalization of the learning hyper parameters used to fit a DNN; a meta-model. Third, the learning algorithm generalization, where we evaluate generalization in the context of Continual Learning. We present our work on the analysis and theoretical findings on the short-comings of generalization and provide practical solutions that both confirm and can in-part address the issue. We motivate that the problem of generalization extend well beyond the three areas our work addresses where improvements in algorithms, tools, and methods are required. Finally, based on our empirical observations we discuss several future directions for improving generalization in ML systems.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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Computer Science General Faculty Meeting
Thu, Apr 27, 2023 @ 11:00 AM - 01:00 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- Hybrid
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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DEN@Viterbi - 'Limited Status: How to Get Started' Virtual Info Session
Thu, Apr 27, 2023 @ 12:00 PM - 01:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi for our upcoming Limited Status: How to Get Started Virtual Information Session via WebEx to learn about the Limited Status enrollment option. The Limited Status enrollment option allows individuals with an undergraduate degree in engineering or related field, with a 3.0 GPA or above to take courses before applying for formal admission into a Viterbi graduate degree program.
USC Viterbi representatives will provide a step-by-step guide for how to get started as a Limited Status student and enroll in courses online via DEN@Viterbi as early as the Summer 2023 semester.
Register Now!WebCast Link: https://uscviterbi.webex.com/weblink/register/r03f142c5026622cdf38ca5a2bf09f4d4
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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PhD Dissertation Defense - Lauren Klein
Thu, Apr 27, 2023 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Dissertation Defense - Lauren Klein
Committee Members: Maja Mataric (chair), Pat Levitt, Shrikanth Narayanan, Mohammad Soleymani, and Jesse Thomason
Title: Modeling Dyadic Synchrony with Heterogeneous Data: Validation in Infant Mother and Infant Robot Interaction
Abstract: Our health and wellbeing are intricately tied to the dynamics of our social interactions, or social synchrony. The key components of social synchrony during embodied interactions are temporal behavior adaptation, joint attention, and shared affective states. To create comprehensive representations of nuanced social interactions, computational models of social synchrony must account for each of these components.
The goal of this dissertation is to develop and evaluate approaches for modeling social synchrony during embodied dyadic interactions. We present computational models of social synchrony in two contexts. First, we explore human to human social interactions, where attention and affective states must be inferred through behavioral observations. During embodied interactions, social partners communicate using a diverse range of behaviors, therefore, this work develops approaches for modeling temporal behavior adaptation using heterogeneous data, or data representing multiple behavior types. Next, we explore social synchrony in the context of human to robot interaction. Robots must be equipped with perception modules to establish joint attention and shared affective states based on information about their partners behaviors. To address this need, we develop and evaluate models for attention and affective state recognition. Given the central role of communication in cognitive and social development, this dissertation focuses on interactions that occur during infancy and early childhood. Specifically, we develop and evaluate our approaches using recordings of infant to mother, infant to robot, and child to robot interactions.
The work presented in this dissertation for evaluating and supporting social synchrony enables new opportunities to study the relationships between individual behaviors, joint interaction states, and developmental and health outcomes.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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PhD Thesis Proposal - Adriana Sejfia
Thu, Apr 27, 2023 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Adriana Sejfia
Committee Members: Nenad Medvidovic (chair), Chao Wang, William Halfond, Mukund Raghothaman, Sandeep Gupta, and Jyotirmoy Deshmukh
Title: Systematic Improvement of Deep Learning Based Vulnerability Detection
Abstract: Deep learning based techniques have gained traction in software vulnerability detection. However, the performance of these techniques in data drawn from distributions other than the ones the models have been explicitly trained on has been shown to vary a lot. In this talk, I will present our study on four limitations of the current deep learning based vulnerability detectors and the datasets they use along with solutions we propose to address these limitationsAudiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/97573523067?pwd=aW94cUlkM3IwZmk5L3E2a1ZTTG9SUT09
