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Events for April 28, 2025

  • Repeating EventEiS Communications Hub - Tutoring for Engineering Ph.D. Students

    Mon, Apr 28, 2025 @ 10:00 AM - 12:00 PM

    Viterbi School of Engineering Student Affairs

    Workshops & Infosessions


    Viterbi Ph.D. students are invited to drop by the Hub for instruction on their writing and speaking tasks!  All tutoring is one-on-one and conducted by Viterbi faculty.

    Location: Ronald Tutor Hall of Engineering (RTH) - 222A

    Audiences: Viterbi Ph.D. Students

    View All Dates

    Contact: Helen Choi

    Event Link: https://sites.google.com/usc.edu/eishub/home


    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 Colloquium: Alessandro Vespignani (Northeastern University) - From Data to Decisions: Computational Approaches to Dynamics, Behavior, and Forecasting in Complex Systems

    Mon, Apr 28, 2025 @ 10:00 AM - 11:00 AM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Alessandro Vespignani, Northeastern University

    Talk Title: From Data to Decisions: Computational Approaches to Dynamics, Behavior, and Forecasting in Complex Systems

    Abstract: The behavior of complex socio-technical systems is shaped by dynamic interactions across multiple layers of data, infrastructure, and human behavior. Understanding and anticipating these dynamics is essential for improving resilience, governance, and societal well-being. In this talk, I will explore how advanced computational modeling—leveraging network science, dynamical systems, AI, and machine learning—can help decode, simulate, and forecast the behavior of large-scale interconnected systems. Starting from the study of contagion phenomena—biological, informational, and behavioral—I will highlight how data-driven modeling frameworks have advanced our ability to monitor and forecast collective dynamics in real time. I will then present applications in public health and infectious disease management, where such models have informed epidemic preparedness, intervention design, and decision-making. Moving beyond contagion, I will discuss the broader implications of these tools in understanding adaptive behavior, feedback mechanisms, and systemic risk in socio-technical systems. By integrating empirical data with mechanistic and machine learning models, we can begin to build predictive frameworks that support decision-making across domains—from public health to digital ecosystems—where complexity is the rule, not the exception.    
     
    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Alessandro Vespignani is the Sternberg Family Distinguished University Professor at Northeastern University, where he directs the Network Science Institute. His research lies at the intersection of computational modeling, data science, and complex systems, with a focus on contagion phenomena such as epidemics, information diffusion, and collective behavior. Vespignani builds large-scale predictive models that integrate real-world data using mechanistic simulations and machine learning to understand and forecast the dynamics of interconnected systems. He currently leads the CDC-funded EPISTORM center, a national effort to build the next generation of epidemic forecasting infrastructure, integrating diverse data streams such as wastewater surveillance, viral genomics, and high-resolution mobility data. Vespignani has authored over 200 scientific publications in leading journals, including Nature, Science, and PNAS. He is also the author of several books and monographs on complex systems and network science. He is a Fellow of the American Physical Society, the AAAS, and the Network Science Society.

    Host: Cyrus Shahabi

    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.

  • PhD Dissertation Defense - Dong Ho Lee

    Mon, Apr 28, 2025 @ 04:00 PM - 06:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    Title: Improving Language Model Through Context
     
    Date and Time: Monday, April 28th, 2025 - 4:00p - 6:00p
     
    Location: KAP 165
     
    Committee Members: Jay Pujara (Chair), Xiang Ren, robin Jia, Fred Morstatter, amd Meisam, Razaviyayn
     
    Abstract: Contextual cues are important in recent LM research, enabling models to reason effectively, handle complex tasks, and exhibit social intelligence through context-aware interactions. My research proposes foundational groundwork for the systematic study and practical incorporation of multiple contextual information into LM. Specifically, I address three key research questions: (1) Can LMs effectively learn from context during inference?; (2) Does adding context during training enhance model behavior?; (3) Can LMs dynamically generate and refine context to improve its output quality?To explore these questions, I explore a variety of contextual cues including (a) human-provided explanations [TriggerNER (ACL 2020), AutoTriggER (EACL 2023), LEAN-LIFE (ACL 2020), XMD (ACL 2023)]; (b) in-context examples [FewNER (ACL 2022), LLM-Data-Creation (EMNLP 2023), TKG-LLM (EMNLP 2023)]; (c) dialogue context [Normvio-RT (EMNLP 2023), LoCoMo (ACL 2024), REALTALK (2025)]; and (d) model-generated context [QUEST(2025)].
     
    Zoom Link: https://usc.zoom.us/j/7469763888?pwd=UzAveW81ZGF5ZCt1Vkoxd09DUml0dz09

    Location: Kaprielian Hall (KAP) - 165

    Audiences: Everyone Is Invited

    Contact: Dong Ho Lee


    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.