Alfred P. Navato’s PhD Dissertation Defense

Title:
Enabling Anomaly Detection in Complex Chemical Mixtures Through Multimodal Data Fusion

Date:
6/26/2023

Time:
10:00:00 AM

Location:
SH 210,

Committee Members:
Prof. Mueller (Advisor)
Prof. Erdogmus
Prof. Ioannidis
Prof. Onnis-Hayden

Abstract:
Recently innovations in machine learning and data processing are increasingly tied to ensuring useability and interpretability when these methods are applied within end-user domains.  One societally important example of such a domain is management and operations of water infrastructure in cities, where data collection is currently costly and limited, enabling analytics have the potential to generate real impact for urban communities, and correctness of results is critical to protect human and environmental health.  This dissertation holistically considers issues of generalizability, transferability, and applicability of a range of data fusion and machine learning approaches across end-user domains within the context of solution building for improved real-time management of wastewater infrastructure.  The first chapter provides an overview of the challenges associated with anomaly detection within the wastewater field and reviews the performance of various anomaly detection techniques implemented in other disciplines.  The second chapter discusses the barriers and opportunities in cross-disciplinary pollination of data fusion techniques.  The third chapter presents development of an unsupervised approach facilitating quantitative characterization of the complex background which is wastewater, necessary to be able to implement any automated operational interventions.  The fourth chapter develops an approach for cost-minimization/information-maximization design of a sensor to facilitate specifically detection of chemical anomalies (defined as inflow events that might compromise wastewater treatment facilities) by using machine learning and feature selection techniques to minimize the number of input signals needed to achieve reasonable accuracies.  Together the third and fourth chapters provide a clear, explainable, actionable pathway forward in envisioning next generation wastewater infrastructure, demonstrating novel and impactful use of data fusion and machine learning techniques in a real-world context.