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Events for the 5th week of April
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MoBI Seminar: The Brain's Crescendo; How Music Training Impacts Child Development
Mon, Apr 24, 2023 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Assal Habibi, Associate Research Professor of Psychology, Brain and Creativity Institute, University of Southern California
Talk Title: The Brain's Crescendo; How Music Training Impacts Child Development
Abstract: In a multi-year longitudinal study, we have been investigating the effects of a group-based music training program on the development of children, beginning at age 6, using behavioral, neuroimaging, and electrophysiological measures. The target group of children have been participating in the Youth Orchestra of Los Angeles (YOLA) program. This music program is based on the Venezuelan system of musical training known as El-Sistema and offers free music instruction 6-“7 hours weekly to children from underprivileged and under-resourced areas of Los Angeles. The children in the music program have been compared with two groups of children, one involved in a community-based sports program, and another not enrolled in any systematic afterschool training. During this talk, I will share some of the behavioral and neuroimaging results from this study. Over the course of 5 years, we have observed that children in the music group had better performance than comparison groups in musically relevant auditory skills (pitch and rhythm discrimination) and showed an accelerated maturity of auditory processing as measured by cortical auditory evoked potentials. We also observed that children in the music group showed a different rate of cortical thickness maturation between the right and left posterior superior temporal gyrus and higher fractional anisotropy in the corpus callosum, specifically in the crossing pathways connecting superior frontal, sensory, and motor segments. For nonmusical skills, children with music training, compared with children without music training, showed stronger neural activation during a cognitive inhibition task in brain regions involved in response inhibition and decision-making (bilateral pre-SMA/SMA, ACC, IFG). Finally, we observed that parents of children involved in music training, after four years, rated their children higher on the emotional stability personality trait and lower on aggression and on hyperactivity compared to children not involved in music activities despite no differences in these measures before children's entry into the program. Considering a general reduction in art education specifically in the communities where there is limited access to art exposure in general, and specifically to music education, the findings from this study is providing compelling answers to the ongoing discussion about music's role in the education curriculum.
Biography: Assal Habibi is an Associate Research Professor of Psychology at the Brain and Creativity Institute at the University of Southern California. Her research takes a broad perspective on understanding the influence of arts and specifically music on health and development, focusing on how biological dispositions and learning experiences shape the brain and development of cognitive, emotional and social abilities during childhood and adolescence. She is an expert on the use of electrophysiologic and neuroimaging methods to investigate human brain function and has used longitudinal and cross-sectional designs to investigate how implementing music training programs within the school curricula impacts the learning and academic achievement of children from under-resourced communities. Her research program has been supported by federal agencies and private foundations including the NIH, NEA and the GRoW @ Annenberg Foundation and her findings have been published in peer reviewed journals including Cerebral Cortex, Music Perception, Neuroimage and PLoS ONE. Currently, she is the lead investigator of a multi-year study, in collaboration with the Los Angeles Philharmonic and their Youth Orchestra program (YOLA), investigating the effects of early childhood music education on the development of brain function and structure as well as learning skills, cognitive, emotional, and social abilities. Dr. Habibi is a classically trained pianist and has many years of musical teaching experience with children, a longstanding personal passion.
Host: Dr. Karim Jerbi, karim.jerbi.udem@gmail.com and Dr. Richard M. Leahy, leahy@sipi.usc.edu
Webcast: https://usc.zoom.us/j/99532928626?pwd=QjlwM2JGejZLdzNPdWEwc3RSNk0wdz09More Information: MoBI-Seminar-Habibi-042423.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539
WebCast Link: https://usc.zoom.us/j/99532928626?pwd=QjlwM2JGejZLdzNPdWEwc3RSNk0wdz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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PHD Thesis Defense - Christopher Denniston
Mon, Apr 24, 2023 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
PHD Thesis Defense - Christopher Denniston
Title: Active Sensing In Robotic Deployment
Committee Members: Prof. Gaurav S. Sukhatme (Chair), Prof. Jesse Thomason, Prof. David A. Caron
Date/Time: April 24th, 2-4pm
Location: RTH 306 or on Zoom https://usc.zoom.us/j/2869134593
Abstract:
Robots have the potential to greatly increase our ability to measure complex environmental phenomena, such as harmful algae blooms, which can harm humans and animals alike in drinking water. Such phenomena require study and measurement at a scale that is beyond what can be accomplished by robots that plan to completely cover the area. Despite this, many sensing robots still are deployed with non-active behaviors, such as fixed back-and-forth patterns. The lack of deployment of active sensing systems in practice is due to difficulties with problems encountered in the real world. We identify and address solutions for three main issues which plague complex real robotic active sensing deployments.
First, active sensing systems are difficult to use, with complex deployment-time decisions that affect the efficiency of sensing. We describe systems that eschew these decisions, allowing for efficient and automatic deployment. We find that these systems provide a non-technical deployment procedure and outperform hand-tuned behaviors.
Second, active sensing robots tend to perform a survey that maximizes some general goal and requires the user to interpret the collected data. We propose a system that, instead, plans for the specific user task of collecting physical samples at limited, unknown locations. We demonstrate that planning for this specific task while sensing allows for more efficiency in the active sensing behavior.
Finally, existing models for active sensing do not accurately model the interaction of the sensed signal and obstacles in the environment. We propose two novel modeling techniques which allow active sensing of signals which have complex interactions with obstacles, such as electromagnetic waves. Both outperform traditional modeling techniques and enable scalable active sensing to a large number of measurements on a real robot. Additionally, we find that they allow the robot to actively place signal-emitting devices while sensing the signals from these placed devices.
Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: Asiroh Cham
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PhD Thesis Defense - Yannan Li
Tue, Apr 25, 2023 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Yannan Li
Title: Formal Analysis of the Data Poisoning Robustness of K-Nearest Neighbors
Committee members(Lexicographic order): Pierluigi Nuzzo, Mukund Raghothaman, Chao Wang (chair)
Abstract: Data poisoning, which aims to corrupt a machine learning model and change its inference results by changing data elements in its training set, poses a significant threat to machine learning based software systems. However, formally certifying data poisoning robustness is a challenging task. I designed and implemented a set of formal methods for deciding, both efficiently and accurately, the data-poisoning robustness of the k-nearest neighbors (KNN) algorithm, which is a widely-used supervised machine learning technique. First, I developed a method for certifying the data-poisoning robustness of KNN by soundly overapproximating both the learning and inference phases of the KNN algorithm. Second, I developed a method for falsifying data-poisoning robustness, by quickly detecting the truly-non-robust cases using search space pruning and sampling. Finally, I extended these methods to other attack models and fairness certification, thus allowing for a more comprehensive analysis of the robustness of KNN.
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/94891715635?pwd=SFI5VFBtMndhN3BORk5GSjRyS2IzQT09
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PhD Thesis Proposal - Shihan Lu
Tue, Apr 25, 2023 @ 11:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Analysis, Synthesis, and Perception of Multimodal Feedback for Humans and Robots
Committee: Heather Culbertson (Chair), Stefanos Nikolaidis, Gaurav Sukhatme, Jernej Barbic, Shrikanth Narayanan
Date: Tuesday, April 25, 11 am - 1 pm PST
Abstract: Multimodal feedback, including haptic and auditory feedback, is often overlooked in interactive and contact-rich scenarios in the studies with both humans and robots, such as writing on the back of an envelope with a pen or grasping a block in a Jenga game. In this work, I focus on three perspectives related to the multimodal feedback in interactions: (1) Analysis â“ how to extract useful and interpretable features from multimodal feedback; (2) Synthesis â“ how to simulate realistic virtual feedback; and (3) Perception â“ how humans and robots respond to the feedback. I explore these perspectives through tasks of sound modeling, haptic texture design, large-scale texture classification, and state-aware robot manipulation. With these tasks, the objective is to enhance the interactive experience in virtual reality, improve the understanding of crossmodal relationships, and complement visual and tactile sensing in challenging robot manipulation tasks.
Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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CS Colloquium: Wanrong Zhang (Harvard) - Enabling Interactivity to Move Differential Privacy Closer to Practice
Tue, Apr 25, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Wanrong Zhang, Harvard University
Talk Title: Enabling Interactivity to Move Differential Privacy Closer to Practice
Series: CS Colloquium
Abstract: With growing concerns about large-scale data collection and surveillance, the development of privacy-preserving tools can help alleviate public fears about the misuse of personal data. The field of differential privacy (DP) offers powerful data analysis tools that provide worst-case privacy guarantees. However, most of the existing tools in the differential privacy literature only apply to static databases with non-interactive analysis, which release query answers in a single shot. In practice, data analysts often need to perform a sequence of adaptive analyses on data arriving online, which raises the need for interactive data analysis. This development poses two major questions: 1. How can we design interactive mechanisms that strike a better trade-off between privacy and accuracy? 2. Can we combine multiple interactive mechanisms as building blocks to create a more complex DP algorithm?
In this talk, I will discuss some of my work that answers these questions. To answer the first question, I have created a wide set of tools for private online decision-making problems. I will present one example problem for handling online databases---differentially private change-point detection. Second, I will show the optimal composition theorems for composing multiple interactive mechanisms. My work is among the first to address this long-standing gap in the understanding of composition for differential privacy. I will conclude the talk with my future directions.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Wanrong Zhang is an NSF Computing Innovation Fellow in the Theory of Computing group at Harvard John A. Paulson School of Engineering and Applied Sciences. She is also a member of the Harvard Privacy Tools/OpenDP project. Her primary focus is to address new challenges introduced by real-world deployments of differential privacy. Before joining Harvard, she received her Ph.D. from Georgia Institute of Technology. She was selected as a rising star in EECS in 2022 and a rising star in Data Science in 2021. She is a recipient of the Computing Innovation Fellowship from CCC/CRA/NSF.
Host: Jiapeng Zhang
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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DEN@Viterbi: How to Apply Virtual Info Session
Tue, Apr 25, 2023 @ 12:00 PM - 01:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi representatives for a step-by-step guide and tips for how to apply for formal admission into a Master's degree or Graduate Certificate program. The session is intended for individuals who wish to pursue a graduate degree program completely online via USC Viterbi's flexible online DEN@Viterbi delivery method.
Attendees will have the opportunity to connect directly with USC Viterbi representatives and ask questions about the admission process throughout the session.
Register Now!WebCast Link: https://uscviterbi.webex.com/weblink/register/r8f26794575597c288a2f21a96e8cfc5c
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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PHD Thesis Proposal - Woojeong Jin
Tue, Apr 25, 2023 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
PHD Thesis Proposal - Woojeong Jin
Title: Towards a Better Reasoner on Visual Information
Humans acquire knowledge by processing visual information through observation and imagination, which expands our reasoning capability about the physical world we encounter every day. Despite significant progress in solving AI problems, current state-of-the-art models in natural language processing (NLP) and computer vision (CV) have limitations in terms of reasoning and generalization, particularly with complex reasoning on visual information and generalizing to unseen vision-language tasks. This thesis proposal aims to address these shortcomings by presenting a series of works that enable smaller vision-language (VL) models to generalize to new tasks, improve language models by incorporating visual information, and evaluate language models by assessing their ability to reason about the physical world through text.
https://usc.zoom.us/j/98941948220
12 pm on 4/25
Committee Members: Xiang Ren, Ram Nevatia, Jesse Thomason, Robin Jia, Emilio Ferrara.Location: https://usc.zoom.us/j/98941948220
Audiences: Everyone Is Invited
Contact: Asiroh Cham
Event Link: https://usc.zoom.us/j/98941948220
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PHD Thesis Proposal - Peifeng Wang
Tue, Apr 25, 2023 @ 03:00 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
PHD Thesis Proposal - Peifeng Wang
Title: Building Small-Scale but Advanced Language Reasoners
Abstract:
The entanglement of multiple language capabilities within large language models requires expensive scaling to work effectively. I argue that a disassociation of these capabilities from core language skills can enable the creation of smaller, more accessible language models. Additionally, this disassociation will facilitate the development of language models with enhanced reasoning abilities.
This thesis proposal presents three techniques to build small language models with advanced reasoning capabilities. First, I introduce an Imagine&Verbalize framework for generative commonsense reasoning, which decomposes a complex generation task into easier sub-tasks and learns from a diverse set of indirect supervision from multiple domains. Second, I present a knowledge-transferring pipeline which prompts large language models to rationalize for an open-domain question and then trains small language models to answer consistently. Third, I discuss augmenting small LMs with a working memory for coherent language reasoning by tracking the states of the described world.
Venue: zoom at https://usc.zoom.us/j/97850702935?pwd=ekJ0K1RMM045Tk1EQUV1OUEvOE5iQT09
Date and time: 3:00pm-4:30pm on April 25th
Committee Members: Xiang Ren (chair), Filip Ilievski, Swabha Swayamdipta, Ram Nevatia, Emilio Ferrara
Location: https://usc.zoom.us/j/97850702935?pwd=ekJ0K1RMM045Tk1EQUV1OUEvOE5iQT09
Audiences: Everyone Is Invited
Contact: Asiroh Cham
Event Link: https://usc.zoom.us/j/97850702935?pwd=ekJ0K1RMM045Tk1EQUV1OUEvOE5iQT09
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Epstein Institute - ISE 651 Seminar
Tue, Apr 25, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Dinakar Gade, Product Manager, Xpress Optimization
Talk Title: Recent Trends and Evolution in Optimization Solvers
Host: Prof. Suvrajeet Sen
More Information: April 25, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Colloquium: Silvia Sellan (University of Toronto) - "Geometry +": A Tour of Geometry Processing Research
Tue, Apr 25, 2023 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Silvia Sellan, University of Toronto
Talk Title: "Geometry +": A Tour of Geometry Processing Research
Series: CS Colloquium
Abstract: From virtual reality to 3D printing, all the way through self-driving cars and the metaverse, today's technological advances rely more and more on capturing, creating and processing three-dimensional geometry. In this talk, we will show how geometry processing can empower other areas of Computer Science to find new research questions and solutions. Specifically, we will focus on our latest progress on realtime fracture simulation for video games, an algorithmic fairness analysis of gender in the Computer Graphics literature and a quantification of the uncertainty associated with several steps of the Geometry Processing pipeline
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Silvia is a fourth year Computer Science PhD student at the University of Toronto. She is advised by Alec Jacobson and working in Computer Graphics and Geometry Processing. She is a Vanier Doctoral Scholar, an Adobe Research Fellow and the winner of the 2021 University of Toronto Arts & Science Dean's Doctoral Excellence Scholarship. She has interned twice at Adobe Research and twice at the Fields Institute of Mathematics. She is also a founder and organizer of the Toronto Geometry Colloquium and a member of WiGRAPH. She is currently looking to survey potential future postdoc and faculty positions, starting Fall 2024
Host: Oded Stein
Location: Seeley G. Mudd Building (SGM) - 124
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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Partners in Materials Research Seminar Series - DR. LOUIS PEREZ of Apeel Sciences
Tue, Apr 25, 2023 @ 04:00 PM - 05:00 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Dr. Louis Perez, Apeel Sciences
Talk Title: From Idea to Product: Leveraging Chemistry and Materials to Reduce Food Waste
Host: Mork Family Department of Chemical Engineering and Materials Science and The USC Materials Consortium
More Information: Confirmed Flyer. Louis Perez.pdf
Location: James H. Zumberge Hall Of Science (ZHS) - 252
Audiences: Everyone Is Invited
Contact: Monique Garcia
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Abbott Coffee Chat
Tue, Apr 25, 2023 @ 05:00 PM - 07:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Coffee Chat with Abbott!
Date: Tuesday, April 25, 2023
Time: 5:00 p.m. - 7:00 p.m.
Location: Ronald Tutor Hall (RTH) 211
Please join the Abbott Cardiovascular Team for a Coffee Chat. Feel free to bring your questions about Abbott, resumes, interviewing, business and engineering, and more!
Event overview, who will be speaking, details and what your opportunities are, and any other pertinent information. Nicole Hill, Tyler Smith, and Dean Khan will be speaking about their personal journeys and about Abbott Cardiovascular.
What majors and class levels are you interested in connecting with? Chemical Engineering, Mechanical Engineering, Industrial Systems, Computer Engineering, Supply Chain, Electrical Engineering
Are you recruiting for internships, full time, or both? Networking session, and 2024 intern recruiting.
Can you offer Visa sponsorship? Are you able to hire a student on CPT or OPT? We require unrestricted work eligibility for hiring students.Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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WIE Senior Send Off
Tue, Apr 25, 2023 @ 06:10 PM - 08:10 PM
USC Viterbi School of Engineering
Student Activity
Come celebrate our seniors with WIE; whether you are supporting your senior friends, graduating, or just coming for some fun, join us for dinner and DIY fun! Bring your friends for a final celebration before the end of this semester! *senior standing status must be verified for senior gifts
Location: Sign into EngageSC to View Location
Audiences: Everyone Is Invited
Contact: Maia Calderon-Ramos
Event Link: https://engage.usc.edu/WIE/rsvp?id=389333
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End of Year Party
Wed, Apr 26, 2023 @ 05:00 PM - 08:00 PM
USC Viterbi School of Engineering
Student Activity
Celebrate the end of the year with your friends, great food and enjoy the evening before finals kick in!! VGSA Ambassadors from Women In Engineering (WIE), National Society for Black Engineers (NSBE) and Queers in Engineering, Science, and Technology (QuEST) are very eager to welcome you to the end of year celebration.
Come and hang out with us for one last time before the semester ends.Location: Sign into EngageSC to View Location
Audiences: Everyone Is Invited
Contact: Akshita Swaminathan
Event Link: https://engage.usc.edu/viterbi/rsvp?id=389425
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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
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BME Seminar Speaker, Dr. Jianping Fu
Fri, Apr 28, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jianpin Fu , Professor, Mechanical Engineering, Biomedical Engineering, Cell & Developmental Biology, University of Michigan
Talk Title: Stem cell and developmental bioengineering
Host: BME Professor Keyue Shen - Zoom Available Upon Request
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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PhD Thesis Defense - Cho-Ying Wu
Fri, Apr 28, 2023 @ 11:30 AM - 12:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Cho-Ying Wu
Committee Members: Ulrich Neumann (chair), Laurent Itti, Andrew Nealen, C.C. Jay Kuo
Title: Meta Learning for Single Image Depth Prediction
Abstract: Predicting geometry from images is a fundamental and popular task in computer vision and has multiple applications. For example, predicting ranges from ego view images can help robots navigate through indoor spaces and avoid collisions. Additional to physical applications, one can synthesize novel views from single images with the help of depth by warping pixels to different camera positions. Further, one can fuse depth estimation from multiple views and create a complete 3D environment for AR VR uses.
In the dissertation, we aim to discover a better learning strategy, meta learning, to learn a higher level representation. The learned representation more accurately characterizes the depth domain. Our presented meta learning approach attains better performance without involving extra data or pretrained models but directly focuses on learning schedules. Then, we closely evaluate the generalizability on our collected Campus Data and demonstrate meta learning's ability in sub, single, multi dataset levels.
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/9340884176
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PhD Thesis Defense - Gozde Sahin
Fri, Apr 28, 2023 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Gozde Sahin
Title: Towards More Occlusion-Robust Deep Visual Object Tracking
Committee Members: Prof. Laurent Itti (chair), Prof. Ulrich Neumann, Prof. Keith Jenkins
Abstract: Visual object tracking (VOT) is considered as one of the principal challenges in computer vision, where a target given in the first frame is tracked in the rest of the video. Major challenges in VOT include factors such as rotations, deformations, illumination changes, and occlusions. With the widespread use of deep learning models with strong representative power, trackers have evolved to better handle the changes in the targets appearance due to factors like rotations and deformations. Meanwhile, robustness to occlusions has not been as widely studied for deep trackers and occlusion representation in VOT datasets has stayed low over the years.
In this work, we focus on occlusions in deep visual object tracking and examine whether realistic occlusion data and annotations can help with development and evaluation of more occlusion-robust trackers. First, we propose a multi-task occlusion learning framework to show how much occlusion labels in current datasets can help improve tracker performance in occluded frames. We discover that lack of representation in VOT datasets creates a barrier for developing and evaluating trackers that focus on occlusions. To address occlusions in visual tracking more directly, we create a large video benchmark for visual object tracking: The Heavy Occlusions in Object Tracking (HOOT) Benchmark. HOOT is specifically tailored for evaluation, analysis and development of occlusion-robust trackers with its extensive occlusion annotations. Finally, using the annotations in HOOT, we examine the effect of occlusions on template update and propose an occlusion-aware template update framework that improves the tracker performance under heavy occlusions.
Location: Hedco Neurosciences Building (HNB) - 100
Audiences: Everyone Is Invited
Contact: Melissa Ochoa