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PhD Thesis Proposal - Chuizheng Meng

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