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Events for the 3rd week of June
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PhD Thesis Proposal - Understanding the Role of Emotions in Online Opinion Polarization
Tue, Jun 17, 2025 @ 02:00 PM - 03:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Understanding the Role of Emotions in Online Opinion Polarization
Time and Date: Tuesday, June 17th, 2025 | 2:00p - 3:30p
Location: EEB 631
Committee Members: Kristina Lerman (Chair), Fred Morstatter, Emilio Ferrara, Lindsay Young, and Pablo Barbera
Abstract:
The thesis investigates how emotional dynamics shape ideological divisions in online discourse, particularly in the wake of issue politicization. By integrating social network analysis with cutting-edge natural language processing techniques, the study addresses three central questions: What emotions do individuals express in online discussions? How do others respond to these emotional expressions? And do these emotional exchanges influence opinion formation over time? Analyzing large-scale social media data, the thesis identifies patterns of emotional expression and reception and traces the role of affective in opinion formation. The findings illuminate the emotional undercurrents of polarization, highlighting how networked emotional exchanges can both reflect and intensify political divides in contemporary digital communication environments.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 631
Audiences: Everyone Is Invited
Contact: Ashwin Rao
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. -
PhD Dissertation Defense - Siyi Guo
Wed, Jun 18, 2025 @ 02:00 PM - 03:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Learning Social Representations for Causal Understanding of Heterogeneous and Dynamic Online Behavor
Date and Time: Wednesday, June 8th, 2025 | 2:00pm
Location: RTH 114
Committee Members: Kristina Lerman, Emilo Ferrara, Urbashi Mitra
Abstract: Social media has become a dominant force in shaping public discourse, civic engagement, and individual behavior, offering a rich but challenging environment for studying human beliefs, behaviors, and decision-making at scale. However, modeling user behavior on these platforms is complicated by multiple challenges---the massive volume of data, multi-modality, heterogeneity across users and platforms, rapidly evolving dynamics, scarcity of annotations, and difficulty in causal analysis from observational data.
This dissertation presents a framework for monitoring, explaining, modeling, and intervening in online user behavior, that addresses the challenges of handling complex and dynamic social media data. First, to understand users' reactions to real-world events in a dynamic online environment, we propose an unsupervised methodology for detecting and explaining collective emotional reactions to events, leveraging transformer-based affect modeling and topic-guided explanations. Second, we introduce SoMeR, a self-supervised, multi-view user representation learning framework that captures diverse user behaviors across text, temporal patterns, profiles, and networks, and generalizes across platforms and tasks. Third, we develop DAMF, a domain-adaptive moral foundation inference model that enables robust supervised language modeling from heterogeneous annotated datasets through adversarial training and label distribution balancing. Finally, we propose CausalDANN, a novel framework for estimating causal effects of direct text interventions using LLM-generated counterfactuals and domain adaptation to mitigate distributional shifts.
Together, these contributions advance computational social science by addressing core challenges in tracking and modeling the temporal dynamics and heterogeneity of online user behavior. The methods developed in this thesis integrate causal inference, representation learning, and time series analysis to enable scalable, generalizable, and causally grounded understanding of social media data. In this thesis, I demonstrate their utility by applying the tools to detect and explain online reactions to offline events, identify and forecast harmful behaviors such as coordinated campaigns and hateful speech, understand the evolution of polarized discussions, infer moral values expressed in online language, and evaluate the causal impact of language on moral judgment. This work offers practical tools for researchers and policymakers seeking to better understand and engage with digital populations in complex, polarized, and fast-changing online environments.Location: Ronald Tutor Hall of Engineering (RTH) - 114
Audiences: Everyone Is Invited
Contact: Siyi Guo
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. -
PhD Thesis Proposal - Zhonghao Shi
Wed, Jun 18, 2025 @ 02:30 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Enabling Personalized Multimodal Interaction for Socially Assistive Robots and Agents
Date and Time: 6/18 2:30pm-4pm
Location: Ronald Tutor Hall (RTH) Room 406
Committee Members:
- Prof. Maja MatariÃÂÃÂ (Committee Chair)
- Prof. Gale Lucas
- Prof. Erdem Biyik
- Prof. David Traum
- Prof. Jonathan Tarbox (external)
Abstract:
Socially assistive robots and agents have shown great promise in providing cost-effective support across domains such as education and healthcare. However, existing socially assistive AI systems still lack the ability to engage users in real-time interactions while effectively supporting users’ cognitive learning goals and socio-emotional needs. This dissertation proposal outlines both completed and remaining work toward enabling personalized, multimodal, real-time interaction capabilities between the robots/agents and human users, particularly for users with diverse needs. The work consists of two main research directions: personalized human perception and individualized interaction design. In the area of personalized human perception, the proposal presents completed work on applying domain adaptation methods to enable personalized multimodal affect recognition for children on the autism spectrum [1], and personalized child speech recognition [2]. It also includes work on benchmarking vision-language models to better understand the performance-latency tradeoffs of frontier models for real-time human perception [3]. In the area of individualized interaction design, the proposal discusses the development of an open-source socially assistive robot learning module for accessible and personalized AI and robotics education [4], as well as work on evaluating and personalizing text-to-speech voices for delivering mental health support [5]. Finally, this proposal will present an ongoing efforts developing a personalized multimodal AI tutor to support speech therapy and learning [6].
[1] Shi, Z., Groechel, T. R., Jain, S., Chima, K., Rudovic, O., & MatariÃÂÃÂ, M. J. (2022). Toward personalized affect-aware socially assistive robot tutors for long-term interventions with children with autism. ACM Transactions on Human-Robot Interaction (THRI), 11(4), 1-28.
[2] Shi, Z., Shi, X., Feng, T., Xu, A., Narayanan, S., & MatariÃÂÃÂ, M. (2025). Examining test-time adaptation for personalized child speech recognition. Paper accepted for presentation at Interspeech 2025.
[3] Shi, Z., Zhao, E., Dennler, N., Wang, J., Xu, X., Shrestha, K., Seita, D., & MatariÃÂÃÂ, M. (2025). HRIBench: Benchmarking vision-language models for real-time human perception in human-robot interaction. Paper accepted for presentation at the International Symposium on Experimental Robotics (ISER) 2025.
[4] Shi, Z.*, O'Connell, A.*, Li, Z.*, Liu, S., Ayissi, J., Hoffman, G., ... & MatariÃÂÃÂ, M. J. (2024, March). Build your own robot friend: An open-source learning module for accessible and engaging AI education. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 21, pp. 23137-23145).
[5] Shi, Z.*, Chen, H.*, Velentza, A. M., Liu, S., Dennler, N., O'Connell, A., & Mataric, M. (2023, March). Evaluating and personalizing user-perceived quality of text-to-speech voices for delivering mindfulness meditation with different physical embodiments. In Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (pp. 516-524).
[6] Shi, Z.*, Chung, D.*, Du, Y., Zhang, J., Raina, S. & MatariÃÂÃÂ, M. J. (2025) Is AI Ready to Support Speech Therapy for Children? A Systematic Review of AI-Enabled Mobile Apps for Pediatric Speech Therapy. Accepted to Interaction Design and Children (IDC) Conference.
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Zhonghao Shi
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. -
Ahmed H. Qureshi (Purdue University) - Harnessing Physics Priors for Efficient and Scalable Robot Motion Learning
Thu, Jun 19, 2025 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ahmed H. Qureshi, Purdue University
Talk Title: Harnessing Physics Priors for Efficient and Scalable Robot Motion Learning
Abstract: This talk will outline the use of physics priors towards creating efficient plug-and-play algorithms for robot motion learning. These algorithms require minimal to no expert data and achieve high efficiency in both training and inference while effectively operating in complex, high-dimensional environments under various constraints. Recent advancements in robot motion learning include methods based on imitation and offline reinforcement learning, which are known to necessitate a significant amount of expert trajectories and entail high training times. In contrast, this presentation will introduce a new class of self-supervised, physics-informed neural motion policy learners. These methods aim to directly solve Partial Differential Equations (PDEs) that govern robot motion without depending on expert data or requiring extensive training resources. Additionally, the talk will discuss how these PDEs can create a new robot-motion-friendly mapping feature. We demonstrate that this new mapping feature is better suited for fast robot motion generation than existing mapping features, such as occupancy maps or Sign Distance Fields. This talk will demonstrate that these new physics-informed approaches outperform state-of-the-art imitation learning and offline reinforcement learning methods in terms of scalability, training efficiency, data efficiency, computational planning speed, path quality, and success rates.
Biography: Ahmed Qureshi is an Assistant Professor in the Department of Computer Science at Purdue University. Dr. Qureshi is also currently serving as an Associate Editor for IEEE Transactions on Robotics (TRO) and IEEE Robotics and Automation Letters (RA-L). In 2024, he received the Outstanding Associate Editor award from IEEE RA-L. He has previously served on the program committees of RSS, ICRA, IROS, and CoRL. At Purdue University, Dr. Qureshi directs the Cognitive Robot Autonomy and Learning (CoRAL) Lab. His group conducts fundamental and applied research in robot motion planning and control with the aim of developing robots that can understand the general laws of physics and plan their movements in real time with minimal to no expert demonstrations. His work addresses problems such as scalable and fast motion planning, dexterous manipulation, active perception, and multiagent task and motion planning. Dr. Qureshi's contributions have been recognized with spotlight and best paper awards at various academic venues. Prior to his current roles, he earned his B.S. in Electrical Engineering from NUST, Pakistan, an M.S. in Engineering from Osaka University, Japan, and a Ph.D. in Intelligent Systems, Robotics, and Control from the University of California San Diego.
Host: Daniel Seita
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone (USC) is invited
Contact: CS Faculty Affairs
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. -
CA DREAMS - Technical Seminar Series
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Fri, Jun 20, 2025 @ 12:00 PM - 01:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Puneet Gupta, Professor, UCLA
Talk Title: Scale-out Chiplet Based Systems: Design, Architecture and Pathfinding
Series: CA DREAMS - Technical Seminar Series
Abstract: As conventional technology scaling becomes harder, 2.5D integration provides a viable pathway to building larger systems at lower cost. Waferscale chiplet-based systems, as much as 100X larger than largest modern SoCs pose new opportunities and challenges in their architecture and design. We describe a waferscale GPU concept and discuss our experience designing a 2000 chiplet waferscale processor system, pointing out key challenges and solutions. Next, we describe our ongoing work on developing a cross-stack pathfinding framework for large distributed 2.5D/3D systems, identifying areas where technology development would help design metrics substantially, especially for the important class of distributed machine learning training applications.
Biography: Puneet Gupta received the B.Tech. degree in electrical engineering from the Indian Institute of Technology Delhi, New Delhi, India, in 2000, and the Ph.D. degree from the University of California at San Diego, San Diego, CA, USA, in 2007. He is currently a Faculty Member with the Electrical and Computer Engineering Department, University of California at Los Angeles. He Co-Founded Blaze DFM Inc., Sunnyvale, CA, USA, in 2004 and served as its Product Architect until 2007. He has authored over 200 papers, 18 U.S. patents, a book and two book chapters in the areas of system-technology co-optimization as well as variability/reliability aware architectures. Dr. Gupta is an IEEE Fellow and was a recipient of the NSF CAREER Award, the ACM/SIGDA Outstanding New Faculty Award, SRC Inventor Recognition Award, and the IBM Faculty Award. He currently leads the system benchmarking thrust within SRC JUMP 2.0 CHIME packaging center.
Host: Dr. Steve Crago
More Info: https://www.isi.edu/events/5810/scale-out-chiplet-based-systems-design-architecture-and-pathfinding/
Webcast: https://usc.zoom.us/j/97017422125?pwd=Dbrt8MNMrmBV3xalKQJcAiNsggFJjJ.1&from=addonWebCast Link: https://usc.zoom.us/j/97017422125?pwd=Dbrt8MNMrmBV3xalKQJcAiNsggFJjJ.1&from=addon
Audiences: Everyone Is Invited
Contact: Amy Kasmir
Event Link: https://www.isi.edu/events/5810/scale-out-chiplet-based-systems-design-architecture-and-pathfinding/
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. -
Quantum/Physics Joint Seminar Series - Francesco Anna Mele, Friday, June 20th at 1:30pm in EEB 132 & Zoom
Fri, Jun 20, 2025 @ 01:30 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Francesco Anna Mele, Physics at the Scuola Normale Superiore Pisa, Italy
Talk Title: From Tomography of CV Systems to a New Measure of Non-Gaussianity: The Symplectic Rank
Series: Quantum/Physics Joint Seminar Series
Abstract: Quantum state tomography, aimed at deriving a classical description of an unknown state from measurement data, is a fundamental task in quantum physics. In parallel, non-Gaussianity serves as a crucial resource for quantum information processing in continuous-variable (CV) systems.In this work, we investigate the fundamental limits of quantum state tomography for CV systems, revealing a deep connection between the efficiency in tomography and the degree of non-Gaussianity. Specifically:
We first show that tomography of general non-Gaussian states is extremely inefficient;
In contrast, we demonstrate that tomography of Gaussian states is efficient. To accomplish this, we introduce new tools of independent interest: tight bounds on the trace distance between CV states in terms of the norm distance between their first moments and covariance matrices.
We then explore the intermediate regime, establishing that tomography becomes progressively harder as the level of non-Gaussianity increases.
The latter result naturally leads to the concept of symplectic rank: a novel non-Gaussianity monotone that satisfies remarkable operational and resource-theoretic properties. Mathematically, the symplectic rank of a pure state is the number of symplectic eigenvalues of the covariance matrix that are strictly larger than the ones of the vacuum. Importantly, the symplectic rank is non-increasing under post-selected Gaussian operations, leading to strictly stronger no-go theorems for Gaussian conversion than those previously known.
The talk will be based on: https://arxiv.org/pdf/2405.01431, https://arxiv.org/pdf/2411.02368, and https://arxiv.org/pdf/2504.19319 .
Biography: Francesco Anna Mele was born in Italy in 1997. He received the B.Sc. and M.Sc. degrees in physics from the University of Pisa, Italy, in 2021, and the Diploma degree in physics from Scuola Normale Superiore of Pisa, Italy, in 2021. He is currently pursuing a Ph.D. in Physics at the Scuola Normale Superiore of Pisa, Italy, under the supervision of Vittorio Giovannetti and Ludovico Lami. He is currently visiting the California Institute of Technology (Caltech) until August 15. His research interests include all aspects of quantum information and computation.
Host: Quntao Zhuang, Eli Levenson-Falk, Jonathan Habif, Daniel Lidar, Kelly Luo,k Todd Brun, Tony Levi, Stephan Haas
More Info: https://usc.zoom.us/j/91779790215?pwd=ZlygfD3dO1htFllfQ0n8HyIaLNnYjL.1
More Information: Francesco Mele -June 20, 2025.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: https://usc.zoom.us/j/91779790215?pwd=ZlygfD3dO1htFllfQ0n8HyIaLNnYjL.1
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. -
Robotics and Autonomous Systems Center (RASC) Seminar
Fri, Jun 20, 2025 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering, Thomas Lord Department of Computer Science, USC School of Advanced Computing
Conferences, Lectures, & Seminars
Speaker: Prof. Dinesh Jayaraman, University of Pennsylvania
Talk Title: Engineering Better Robot Learners: Exploration and Exploitation
Abstract: Industry is placing big bets on "brute forcing" robotic control, but such approaches ignore the centrality of resource constraints in robotics on power, compute, time, data, etc. Towards building a true engineering discipline of robotics, my research group has been "exploiting and exploring" robot learning: exploiting to push the limits of what can be achieved with today's prevalent principles at various resource constraints, and "exploring" better design principles for efficient and minimalist robots in the future. As examples of “exploit”, we have trained quadruped robots to perform circus tricks on yoga balls and robot arms to perform household tasks in entirely unseen scenes with unseen objects. As examples of “explore”, we are studying the sensory requirements of robot learners: what sensors do they need and when do they need them during training and task execution? In this talk, I will highlight these examples and discuss some lessons we have learned in our research towards better-engineered robot learners.
Biography: Dinesh Jayaraman is an assistant professor at the University of Pennsylvania's CIS department and GRASP lab. He leads the Perception, Action, and Learning (Penn PAL) research group, which works at the intersections of computer vision, robotics, and machine learning. Dinesh received his PhD (2017) from UT Austin, before becoming a postdoctoral scholar at UC Berkeley (2017-19). Dinesh's research has received a Best Paper Award at CORL '22, a Best Paper Runner-Up Award at ICRA '18, a Best Application Paper Award at ACCV '16, the NSF CAREER award '23, an Amazon Research Award '21, and been covered in The Economist, TechCrunch, and several other press outlets. His webpage is at: https://www.seas.upenn.edu/~dineshj/
Host: Prof. Erdem Biyik
Webcast: https://usc.zoom.us/j/97616702619?pwd=aiV4aX7mgVCUO3qUVmJ5DIWipZBy12.1Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/97616702619?pwd=aiV4aX7mgVCUO3qUVmJ5DIWipZBy12.1
Audiences: Everyone Is Invited
Contact: ERDEM BIYIK
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.
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