Upcoming Doctoral Dissertations at the Schaefer School

"Typically, operation tasks within the power system field follow a predict-then-optimize framework, in which machine learning (ML) methods are first trained to predict key parameters and then optimization models use these predictions as inputs to determine optimal operational decisions. For instance, renewable energy availability is predicted to serve as inputs for day-ahead operation models. The ultimate goal of such a predict-then-optimize process is to achieve the best operation economics associated with the optimal operation tasks, e.g., minimum operation cost or maximum operation revenue." Read more...

"Robots are increasingly becoming integral parts of our daily lives. Achieving safe and efficient navigation in complex and dynamic environments shared with humans presents significant challenges. This dissertation addresses the challenges of autonomous navigation in dynamic environments by developing learning-based methods that enable robots to navigate collision-free and to respond to natural language instructions. Traditional navigation systems fall short in these scenarios due to their inability to capture human social behaviors. By leveraging human trajectory data and advanced robotic simulation techniques, this research provides innovative solutions to improve robot navigation and human-robot interaction." Read more...

"High-Performance Fiber-Reinforced Cementitious Composite (HPFRCC) represents a family of advanced composite materials with remarkable mechanical properties and durability, but their design and characterization tasks involve unique challenges. Recently, the advances in machine learning techniques have offered new opportunities. This dissertation explores the application of machine learning techniques in the design and characterization of HPFRCC." Read more...

"Access to mental health resources remains a significant challenge worldwide, with over 450 million people affected by mental illness. The gap between those needing support and available caregivers continues to widen. Technological advancements have introduced various solutions, including Smartphone-Based Active Self-Reporting, Lab-Based Physiological Monitoring, Wearable Physiological Sensors, and Passive Behavioral Sensing via Smartphones, to aid in the early diagnosis and continuous monitoring of mental illness. However, these approaches often face scalability challenges due to high costs and significant user burden. To this, the widespread accessibility and ubiquity of smartphones offer an unprecedented opportunity to monitor mental health unobtrusively and continuously. While smartphones provide low-burden solutions to real-time monitoring and intervention, their sensing capability has only been limited to behavioral and social signals. As we know, mental illness is multifaceted and affects the behavioral, physiological, and social aspects of people’s lives. This missed opportunity creates a gap in the sensing capabilities of smartphones for a holistic understanding of a person's mental health." Read more...

"This thesis explores advanced hybrid force-motion control and telemanipulation strategies using redundancy resolution methods for surgical and manufacturing applications. The research focuses on developing control algorithms for robotic tasks under constraints, specifically in surface finishing manufacturing processes and in robot-assisted minimally invasive surgery (MIS)." Read more...

"Active optical sensors capable of faithful operation under challenging conditions, such as strong background radiation or complex scattering environments, are highly desirable for remote sensing applications spanning diverse domains. Examples such as long-range terrestrial mapping, orbital seismology, or non-invasive biomedical imaging, additionally include extreme photon starvation of the probing signal, creating conditions that can be prohibitively challenging for conventional sensors based on linear optics." Read more...

"Public transportation infrastructure systems provide an indispensable means for people to access essential resources such as goods, services, and opportunities. However, normal operations of public transportation infrastructure systems are frequently disrupted in disasters – imposing restrictions on the mobility of people and limiting their ability to access essential resources to maintain life stability and well-being. There is, thus, a pressing need for advanced flow analytics to support resilient and adaptive operations of public transportation infrastructure systems, ensuring continued resource accessibility and public welfare. To address this need, this dissertation proposes a novel artificial intelligence (AI)-driven transportation flow analytics framework." Read more...