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Events for December 13, 2023

  • Computer Science General Faculty Meeting

    Wed, Dec 13, 2023 @ 12:00 PM - 02:00 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: Ronald Tutor Hall of Engineering (RTH) - 526

    Audiences: Invited Faculty Only

    Contact: Assistant to CS Chair

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  • PhD Thesis Defense - Kexuan Sun

    Wed, Dec 13, 2023 @ 02:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    Committee members: 
    Prof. Jay Pujara
    Prof. Aiichiro Nakano
    Prof. Gerard Hoberg
     
    Title: Advances in Understanding and Leveraging structured data for knowledge-intensive tasks   
     
    Abstract:  Over the past few decades, the Web has evolved into an essential information hub. Among the vast repository of information, structured data, including well-organized tables, charts, and knowledge graphs, distinguishes itself as a valuable source of knowledge. This dissertation investigates techniques for understanding and harnessing such structured data to enhance knowledge-intensive applications. The first part of the dissertation focuses on tabular data. I first investigate approaches for understanding complex table structures by introducing an automated hybrid probabilistic system that identifies sub-structures within tables and their relationships, offering potential benefits for downstream tasks like data integration. I then explore approaches for selecting valuable information to answer questions relying heavily on financial tables. We approach this task by leveraging case-based reasoning, adapting solutions from existing questions to answer new questions effectively. The second part of the dissertation delves into the realm of KGs. I begin by investigating scientific KGs construction and empirically explore techniques that combine inherent graph structures and external entity-associated information. Additionally, I introduce a novel approach for accurately selecting important information from KGs to answer general-domain questions. These advances are necessary to fully exploit multi-source integrated systems that leverage unstructured and structured information together for knowledge delivery.    

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 203

    Audiences: Everyone Is Invited

    Contact: CS Events

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  • Thesis Proposal (Zihao He)

    Wed, Dec 13, 2023 @ 03:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    Committee members:
    Kristina Lerman (Chair)
    Emilio Ferrara
    Jonathan May
    Fred Morstatter
    Marlon Twyman
     
    Title: Exploring Polarization and Ideological Difference of Online Communities Through Language Models
     
    Abstract: The proliferation of diverse information sources and social platform interactions has led to increased ideological polarization, presenting unique challenges in understanding and quantifying these divides. This thesis tackles the nuanced task of analyzing ideological polarization of  online communities through language models. First, I extract contextualized topic embeddings from a pretrained language model, focusing on identifying polarized topics within various information sources. Next, I use a generative language model to probe into the ideological dimensions within social media discourse, specifically examining Twitter conversations around key political figures; this approach uncovers the complexities within user interactions and the formation of opinion clusters. Finally, I investigate the alignment between the affective responses of large language models and human ideologies. 

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 131A

    Audiences: Everyone Is Invited

    Contact: CS Events

    Event Link: https://usc.zoom.us/j/98773410609?pwd=SXQzekVMZjZ6dVhSdWJCRGlrVlFFZz09

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