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  • PhD Thesis Proposal - Chuizheng Meng

    Wed, Oct 19, 2022 @ 03:30 PM - 05:00 PM

    Computer Science

    University Calendar


    Phd Candidate: Chuizheng Meng

    Title: Trustworthy Spatiotemporal Prediction Models

    Committee:
    Prof. Yan Liu (chair)
    Prof. Salman Avestimehr
    Prof. Aram Galstyan
    Prof. Greg Ver Steeg
    Prof. Craig Knoblock


    Abstract:
    With the great success of data-driven machine learning methods, concerns with the trustworthiness of machine learning models have been emerging in recent years. From the modeling perspective, the lack of trustworthiness amplifies the effect of insufficient training data. Purely data-driven models without constraints from domain knowledge tend to suffer from over-fitting and losing the generalizability on unseen data. Meanwhile, concerns with data privacy further obstruct the availability of data from more providers. On the application side, the absence of trustworthiness hinders the application of data-driven methods in domains such as spatiotemporal forecasting, which involves data from critical applications including traffic, climate, and energy. My thesis proposal constructs spatiotemporal prediction models with enhanced trustworthiness from both the model and the data aspects. For model trustworthiness, the proposal focuses on improving the generalizability of models via the integration of physics knowledge. For data trustworthiness, the proposal proposes a spatiotemporal forecasting model in the federated learning context, where data in a network of nodes is generated locally on each node and remains decentralized. Future works towards the completion of the thesis will target at amalgamating the trustworthiness from both aspects and combine the generalizability of knowledge-informed models with the privacy preservation of federated learning for spatiotemporal modeling.

    WebCast Link: https://usc.zoom.us/j/99153030181?pwd=ZGJHK1Zha1VHa2ZVNjRUcUNXaFdPZz09

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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