Physical Sciences and Mathematics Commons^™

Speeding Up The Quantification Of Contrast Sensitivity Functions Using Multidimensional Bayesian Active Learning, Shohaib Shaffiey

McKelvey School of Engineering Theses & Dissertations

No abstract provided.

Design And Analysis Of Strategic Behavior In Networks, Sixie Yu

McKelvey School of Engineering Theses & Dissertations

Networks permeate every aspect of our social and professional life.A networked system with strategic individuals can represent a variety of real-world scenarios with socioeconomic origins. In such a system, the individuals' utilities are interdependent---one individual's decision influences the decisions of others and vice versa. In order to gain insights into the system, the highly complicated interactions necessitate some level of abstraction. To capture the otherwise complex interactions, I use a game theoretic model called Networked Public Goods (NPG) game. I develop a computational framework based on NPGs to understand strategic individuals' behavior in networked systems. The framework consists of three …

Smart Sensing And Clinical Predictions With Wearables: From Physiological Signals To Mental Health, Ruixuan Dai

McKelvey School of Engineering Theses & Dissertations

Wearable devices such as smartwatches and wristbands are gaining adoption. Recent advances in technology in wearables enable remote health monitoring. However, there are challenges in exploiting wearables in healthcare applications. First, sensor readings from wearables are vulnerable to motion and noise artifacts. A robust pipeline is needed to extract reliable measurements from noisy signals. Second, while wearables support an increasing number of sensing modalities, there is a significant need to generate more clinically meaningful measurements with wearables. Finally, to incorporate wearables into clinical practice, we need to establish the link between wearable measurements and clinical outcomes, thus supporting clinical decisions. …

Dynamic Continuous Distributed Constraint Optimization Problems, Khoi Hoang

McKelvey School of Engineering Theses & Dissertations

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model multi-agent coordination problems that are distributed by nature. The formulation is suitable for problems where the environment does not change over time and where agents seek their value assignment from a discrete domain. However, in many real-world applications, agents often interact in a more dynamic environment and their variables usually require a more complex domain. Thus, the DCOP formulation lacks the capabilities to model the problems in such dynamic and complex environments. To address these limitations, researchers have proposed Dynamic DCOPs (D-DCOPs) to model how DCOPs dynamically …

Tfa Inference: Using Mathematical Modeling Of Gene Expression Data To Infer The Activity Of Transcription Factors, Cynthia Ma

McKelvey School of Engineering Theses & Dissertations

Transcription factors (TFs) are a set of proteins that play a key role in the information processing system that enables a cell to respond to changes in internal and external state. By binding near a gene in a cell’s DNA, a TF can influence that gene’s expression level, triggering the appropriate increase or decrease in production levels of proteins that are needed to handle stressors like a change in nutrient availability or damage to the cell’s internal structures. Transcription factor activity (TFA) is a measure of how much effect a TF has on its target genes in a given sample …

Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu

McKelvey School of Engineering Theses & Dissertations

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and …

Model-Based Deep Learning For Computational Imaging, Xiaojian Xu

McKelvey School of Engineering Theses & Dissertations

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.

The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …

Machine Learning And Scalable Informatics Methods To Predict Disease Status From Multimodal Biomedical Data, Hossein Mohammadian Foroushani

McKelvey School of Engineering Theses & Dissertations

Biological understanding of complex diseases such as stroke and obesity is critical for the advancement of medicine. Further knowledge discovery can provide effective biomarkers to improve disease diagnosis and prognosis, identify driver mutations, predict individual genetic susceptibility for early prevention and effective disease management, and facilitate development of personalized drugs. Stroke is the second leading cause of death and long-term disability in the world. Thus, stroke management is a time-sensitive emergency. The initial hours after stroke onset map the trajectory of subsequent neurologic complications. Cerebral edema develops hours to days after acute ischemic stroke and may result in midline shift …

Integrating Physical Models And Deep Priors For Computational Imaging, Yu Sun

McKelvey School of Engineering Theses & Dissertations

This dissertation addresses integrating physical models and learning priors for computational imaging. The motivation of our work is driven by the recent discussion of learning-based methods that solve the imaging inverse problem by directly learning a measurement-to-image mapping from the existing data: they achieve superior performance over the traditional model-based methods but lack the physical model to impose sufficient interpretation and guarantee of the final image. We adopt the classic statistical inference as the underlying formulation and integrate learning models as implicit image priors, such that our framework is able to simultaneously leverage physical models and learning priors. Additionally, the …

Human-Centered Machine Learning: Algorithm Design And Human Behavior, Wei Tang

McKelvey School of Engineering Theses & Dissertations

Machine learning is increasingly engaged in a large number of important daily decisions and has great potential to reshape various sectors of our modern society. To fully realize this potential, it is important to understand the role that humans play in the design of machine learning algorithms and investigate the impacts of the algorithm on humans.

Towards the understanding of such interactions between humans and algorithms, this dissertation takes a human-centric perspective and focuses on investigating the interplay between human behavior and algorithm design. Accounting for the roles of humans in algorithm design creates unique challenges. For example, humans might …

Scheduling For High Throughput And Small Latency In Parallel And Distributed Systems, Zhe Wang

McKelvey School of Engineering Theses & Dissertations

Parallel and distributed systems are pervasive, such as web services, clouds, and cyber-physical systems. We often desire high throughput and small latency in the parallel and distributed system. However, since the system is distributed and the input is online, scheduling for high throughput while keeping the latency small is often challenging. In this dissertation, we developed scheduling algorithms, policies, and mechanisms to approach high throughput with small latency in various parallel and distributed applications. First, we developed AMCilk runtime system for running multi-programmed parallel jobs on many-processor machines. When running parallel jobs, the allocation of processors to the parallel jobs …

Geometric Algorithms For Modeling Plant Roots From Images, Dan Zeng

McKelvey School of Engineering Theses & Dissertations

Roots, considered as the ”hidden half of the plant”, are essential to a plant’s health and pro- ductivity. Understanding root architecture has the potential to enhance efforts towards im- proving crop yield. In this dissertation we develop geometric approaches to non-destructively characterize the full architecture of the root system from 3D imaging while making com- putational advances in topological optimization. First, we develop a global optimization algorithm to remove topological noise, with applications in both root imaging and com- puter graphics. Second, we use our topology simplification algorithm, other methods from computer graphics, and customized algorithms to develop a high-throughput …