PhD Candidate
University of Virginia
Areas of Interest
- Artificial Intelligence
- Cyber-Physical Systems and Design Automation
Poster
Formal Logic enhanced Learning for Cyber-Physical Systems
Abstract
Deep Neural Networks are broadly applied and have outstanding achievements for prediction and decision-making support for Cyber-Physical Systems (CPS). However, for large-scale and complex integrated CPS with high uncertainties, DNN models are not always robust, often subject to anomalies, and subject to erroneous predictions, especially when the predictions are projected into the future (errors grow over time). To increase the robustness of DNNs for CPS, in my work, I developed a novel formal logic enhanced learning framework with logic-based criteria to enhance DNN models to follow system critical properties and build well-calibrated uncertainty estimation models. Trained in an end-to-end manner with back-propagation, this framework is general and can be applied to various DNN models. The evaluation results on large-scale real-world city datasets show that my work not only improves the accuracy of predictions and effectiveness of uncertainty estimation, but importantly also guarantees the satisfaction of model properties and increases the robustness of DNNs. This work can be applied to a wide spectrum of applications, including the Internet of Things, smart cities, healthcare and many others.
Bio
Meiyi Ma is a Ph.D. candidate in the Department of Computer Science at the University of Virginia, working with Prof. John A. Stankovic and Prof. Lu Feng. Her research interest lies at the intersection of Machine learning, Formal Methods, and Cyber-Physical Systems. Specifically, her work integrates formal methods and machine learning, and applies new integrative solutions to build safe and robust integrated Cyber-Physical Systems, with a focus on smart city and healthcare applications. Meiyi’s research has been published in top-tier machine learning and cyber-physical systems conferences and journals, including NeurIPS. She has received multiple awards, including the Link Lab Outstanding Graduate Research at the University of Virginia and the Best Master Thesis Award. She is serving as the information director for ACM Transactions on Computing for Healthcare and reviews for multiple conferences and journals. She also served as organizing committees for several international workshops.
