PhD Defense -Sara Mohammadinejad

PhD Candidate: Sara Mohammadinejad

Title: Learning logical abstractions from sequential data

Committee: Jyotirmy Deshmukh, Chao Wang, Jesse Thomason, Mukund Raghothaman, Paul Bogdan

Sequential data refers to data where order between successive data-points is important. Time-series data and spatio-temporal data are examples of sequential data. Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. The Internet-of-Things, complex sensor networks, multi-agent cyber-physical systems are all examples of spatially distributed systems that continuously evolve in time, and such systems generate huge amounts of spatio-temporal data. Designers often look for tools to extract high-level information from such data. Traditional machine learning (ML) techniques for sequential data offer several solutions to solve these problems; however, the artifacts trained by these algorithms often lack interpretability. A definition of interpretability by Biran and Cotton is: Models are interpretable if their decisions can be understood by humans.

Formal parametric logic, such as Signal Temporal Logic (STL) and Spatio-temporal Reach and Escape Logic (STREL) are seeing increasing adoption in the formal methods and industrial communities as go-to specification languages for sequential data. Formal parametric logic are machine-checkable, and human-understandable abstractions for sequential data, and they can be used to tackle a variety of learning problems that include but are not limited to classification, clustering and active learning. The use of formal parametric logic in the context of machine learning tasks has seen considerable amount of interest in recent years. We make several significant contributions to this growing body of literature. This dissertation makes five key contributions towards learning formal parametric logic from sequential data. (1) We develop a new technique for learning STL-based classifiers from time-series data and provide a way to systematically explore the space of all STL formulas. (2) We conduct a user study to investigate whether STL formulas are indeed understandable to humans. (3) As an application of our STL-based learning framework, we investigate the problem of mining environment assumptions for cyber-physical system models. (4) We develop the first set of algorithms for logical unsupervised learning of spatio-temporal data and show that our method generates STREL formulas of bounded description complexity. (5) We design an explainable and interactive learning approach to learn from natural language and demonstrations using STL. Finally, we showcase the effectiveness of our approaches on case studies that include but are not limited to urban transportation, automotive, robotics and air quality monitoring.

WebCast Link: https://usc.zoom.us/j/94145108434?pwd=R3M0Smh0ZVp6UkR4S2hiamdhdjlMUT09

Audiences: Everyone Is Invited

Contact: Lizsl De Leon