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Events for June 18, 2025
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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.
