Human behavior plays an important role in infectious disease epidemics. The choice of preventive actions taken by individuals can completely change the epidemic outcome. Computational epidemiologists usually employ large-scale agent-based simulations of human populations to study disease outbreaks and assess intervention strategies. Such simulations rarely take into account the decision-making process of human beings when it comes to preventive behaviors. Absence of realistic agent behavior can undermine the reliability of insights generated by such simulations and might make them ill-suited for informing public health policies. In this thesis, we address this problem by developing a methodology to create and calibrate an agent decision-making model for a large multi-agent simulation, in a data driven way. Our method optimizes a cost vector associated with the various behaviors to match the behavior distributions observed in a detailed survey of human behaviors during influenza outbreaks. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations. / Master of Science / In the real world, individuals can decide to adopt certain behaviors that reduce their chances of contracting a disease. For example, using hand sanitizers can reduce an individual‘s chances of getting infected by influenza. These behavioral decisions, when taken by many individuals in the population, can completely change the course of the disease. Such behavioral decision-making is generally not considered during in-silico simulations of infectious diseases. In this thesis, we address this problem by developing a methodology to create and calibrate a decision making model that can be used by agents (i.e., synthetic representations of humans in simulations) in a data driven way. Our method also finds a cost associated with such behaviors and matches the distribution of behavior observed in the real world with that observed in a survey. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.

Motivated by challenges and opportunities in nutritional epidemiology and food journaling, ubiquitous computing researchers have proposed numerous techniques for automated dietary monitoring (ADM) over the years. Although progress has been made, a truly practical system that can automatically recognize what people eat in real-world settings remains elusive. This dissertation addresses the problem of ADM by focusing on practical eating moment detection. Eating detection is a foundational element of ADM since automatically recognizing when a person is eating is required before identifying what and how much is being consumed. Additionally, eating detection can serve as the basis for new types of dietary self-monitoring practices such as semi-automated food journaling. In this thesis, I show that everyday eating moments such as breakfast, lunch, and dinner can be automatically detected in real-world settings by opportunistically leveraging sensors in practical, off-the-shelf wearable devices. I refer to this instrumentation approach as "commodity sensing". The work covered by this thesis encompasses a series of experiments I conducted with a total of 106 participants where I explored a variety of sensing modalities for automatic eating moment detection. The modalities studied include first-person images taken with wearable cameras, ambient sounds, and on-body inertial sensors. I discuss the extent to which first-person images reflecting everyday experiences can be used to identify eating moments using two approaches: human computation, and by employing a combination of state-of-the-art machine learning and computer vision techniques. Furthermore, I also describe privacy challenges that arise with first-person photographs. Next, I present results showing how certain sounds associated with eating can be recognized and used to infer eating activities. Finally, I elaborate on findings from three studies focused on the use of on-body inertial sensors (head and wrists) to recognize eating moments both in a semi-controlled laboratory setting and in real-world conditions. I conclude by relating findings and insights to practical applications, and highlighting opportunities for future work.

<p dir="ltr">Cyber-physical-human systems (CPHSs) integrate human cognitive capabilities into the decision and control processes of complex dynamical systems. While artificial intelligence (AI) has shown promise in controlling such systems, it often encounters challenges such as conflict with human behavior and brittleness. Moreover, even successful AI implementations may lead to negative impacts on humans, such as the degradation of manual skills and diminished situation awareness, thereby weakening humans' ability to effectively monitor and intervene in off-nominal conditions as the final decision-makers of the systems. To address these unique challenges within CPHSs, this dissertation proposes three key approaches. First, human behavior modeling approaches are proposed to enhance understanding and prediction of human behavior from the perspective of AI. Accurate modeling enables better calibration of AI's expectations regarding human teammates' intentions and skill-levels. Second, a novel shared control approach is developed to expedite human training for complex dynamic control tasks. An assistant agent supports human novices in emulating human experts by leveraging human behavior models to gauge the human's skill-levels and provide tailored assistance to help improve one's skill. Lastly, human-autonomy teaming (HAT) design is addressed from a resource allocation perspective. A systematic computational simulation approach is proposed to optimize function and attention allocation to manage trade-offs in performance, situation awareness, workload, and other considerations. The proposed frameworks are demonstrated via examples in drone applications. Numerical and experimental results, utilizing simulation platforms and human subjects, validate the efficacy of the proposed approaches. This dissertation presents significant progress in the design and implementation of CPHSs in that it offers insights and methodologies to enhance collaborative interactions between humans and autonomous systems in complex environments.</p>