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Volumetric Fluorescence Microscopy For High-Throughput And High-Sensitivity Imaging: From Single Molecules To Tissues, Le-Mei Wang

Graduate Studies Theses and Dissertations 2026

Fluorescence microscopy is an indispensable tool in the biological sciences, enabling researchers to investigate intricate subcellular structures, particularly for volumetric studies. However, conventional optical microscopy for volumetric imaging remains fundamentally constrained by imaging speed and throughput. To bypass traditional serial z-scanning, we introduce an axially scan-free method using a phase layer cake to modulate the system's point spread function. This approach projects volumetric information onto a 2D plane in a single shot, offering high flexibility in tuning axial depth alongside simultaneous multicolor imaging with high spatial resolution and sensitivity. This dissertation divides these technical advancements into cellular and tissue imaging …

Generalization Bounds For Semi‑Supervised Matrix Completion With Distributional Side Information, Antoine Ledent, Mun Chong Soo, Minh Hieu Nong

Research Collection School Of Computing and Information Systems

We study a matrix completion problem where both the ground truth R matrix and the unknown sampling distribution P over observed entries are low-rank matrices, and share a common subspace. We assume that a large amount M of unlabeled data drawn from the sampling distribution P is available, together with a small amount N of labeled data drawn from the same distribution and noisy estimates of the corresponding ground truth entries. This setting is inspired by recommender systems scenarios where the unlabeled data corresponds to ‘implicit feedback’ (consisting in interactions such as purchase, click, etc. ) and the labeled data …

A Systematic Review And Characterization Of Privacy Noncompliance In Real-World Applications, Alexander E. Charkiewicz

Graduate Studies Theses and Dissertations 2026

Software applications increasingly rely on user data to provide their functionality, but improper handling of such data can lead to serious privacy noncompliance with applicable regulations and policies. A prominent example is the Facebook–Cambridge Analytica scandal, in which a third-party application collected the personal data of approximately 87 million Facebook users without users' consent. Despite growing attention to privacy compliance, two key challenges hinder the systematic understanding and analysis of privacy noncompliance. First, unlike security vulnerabilities, which have been systematically categorized through taxonomies such as the Common Weakness Enumeration (CWE), privacy noncompliance lacks a technical taxonomy describing how it manifests …

Dimension Reduction Involving Exogenous Variables With Applications In Manufacturing And Healthcare, Linxi Li

Theses and Dissertations

High-dimensional data analysis presents diverse challenges, including the curse of dimensionality, the complexities of working with datasets that combine large feature spaces with limited sample sizes, and difficulties in identifying meaningful relationships among variables. As datasets grow in size and complexity across different fields, it is increasingly important to develop practical approaches for extracting essential information from such data. Dimension reduction methods address these challenges by alleviating the effects of high dimensionality, enhancing the ability to reveal hidden patterns, and uncovering latent structures within the data to support further analysis. Some methods reduce dimensionality while preserving all relevant information, offering …

Towards Sample-Efficient Deep Reinforcement Learning, Guang Yang

Theses and Dissertations

Deep reinforcement learning (DRL), combining reinforcement learning and high-performance function approximations such as deep neural networks (DNN), is a powerful approach to solving complex sequential decision-making problems. However, due to the complex solution space of the sequential decision-making problems and the inefficient design of the DRL algorithms, DRL algorithms usually require a prohibitively large number of data samples to train effective strategies. Consequently, it is difficult to apply these DRL algorithms to complex real-world problems that require high costs to collect a large volume of data samples. This dissertation proposes new mechanisms to address this sample inefficiency issue, realizing sample-efficient …

A Mathematical Frameworks For Singular, Nonlinear Phenomena: Applications To Nematocyst Firing And Inhomogeneous Nls With Coulomb Potential, Abdulrahman Alharbi

Theses and Dissertations

Nematocysts are specialized cellular organelles found in all cnidarians, including corals and jellyfish, as well as in some single-celled protists such as dinoflagellates. These organelles display remarkable diversity in morphology and function, playing roles in prey capture and defense. The firing of a nematocyst is one of the fastest accelerations in nature, yet the underlying physical mechanisms remain not fully understood. In this work, we address key questions: how sufficient force is generated to overcome the fluid boundary layer, whether fluid–structure interaction models can reproduce observed dynamics, and what mechanisms trigger discharge.

Our research investigates models based on osmotic pressure …

De Novo Protein Binding To Zinc Oxide Through Biomineralization Pathways, Jean-Mark A. Francis

Theses and Dissertations

De novo proteins are structurally distinct from proteins found in nature and thus capable of having their amino acid sequence modified to accomplish tasks such as increasing protein-nanoparticle binding without unfolding or decomposing. Zinc Oxide nanoparticles are functionally distinct from their bulk counterparts and are widely used as semiconductors in a variety of fields such as medicine and agriculture. With demand for these nanoparticles increasing, environmentally sustainable methods of Zinc Oxide nanoparticle synthesis are being investigated as an eco-friendly alternative to currently utilized but environmentally hazardous chemical and physical techniques. This research investigates the binding characteristics between Zinc Oxide nanoparticles …

Visual Cues Of Human-Likeness, Not Salience, Impact Trust-Related Human-Computer Interaction, Jordan Schotz

Graduate Studies Theses and Dissertations 2026

As interactions with digital agents become increasingly integrated into daily life, understanding how visual representations influence social decision-making is critical. Previous research in human-computer interaction has frequently confounded the psychological effects of an agent's perceived human-likeness with the underlying visual salience of the stimuli. To address these persistent gaps, the present study systematically isolated the effects of human-likeness and visual cue trustworthiness on trust behavior while controlling for objective image properties. The present study expanded on and normed the Virtual Avatar Facial Stimuli Set (VAFSS), a comprehensive database comprising hundreds of identity-matched photographs and computer-generated avatars varying across a spectrum …

Machine Learning-Based Intrusion Detection System For Iot Networks Using The Rt-Iot 2022 Dataset, Bukunmi Ebenezer Afolabi

Theses, Dissertations and Capstones

The rapid expansion of the Internet of Things (IoT) has transformed modern computing by enabling seamless connectivity among heterogeneous devices across diverse application domains. However, this increased interconnectivity has significantly enlarged the attack surface of IoT networks, exposing them to a wide range of sophisticated cyber threats. Conventional security mechanisms often lack the capability to detect emerging attacks in real time, thereby necessitating the development of intelligent Intrusion Detection Systems (IDS) capable of accurately identifying malicious network activities. This study developed and evaluated a machine learning-based intrusion detection framework for multiclass IoT attack detection using the RT-IoT2022 dataset. The dataset …

Elucidating The Impacts Of Non-Covalent Interactions In Organic Materials Through A Multiscale Computational Approach, Sashen A. Ruhunage

Theses and Dissertations--Chemistry

Noncovalent interactions (NCIs) in π-conjugated organic materials serve as tunable levers that influence molecular structure and intermolecular interactions in the condensed phase and, in turn, impact the electronic, optical, and mechanical properties of these materials. NCIs include attractive dispersion, electrostatic, and induction interactions, as well as repulsive exchange interactions. However, how to design materials with NCI considerations remains an open question across many fields. Here, we seek to provide an electronic and atomistic perspective on these interactions through multiscale simulations to aid materials design, processing, and performance optimization. In this study, we investigate NCIs and their effects across various systems …

Student Familiarity With The Periodic Table Of The Elements: Results From Cued-Recall And Eye-Tracking Assessments On Memory, Victor A. Okuo

Theses and Dissertations--Chemistry

Learning element symbol–name associations and the spatial organization of elements on the periodic table is a foundational step in learning chemistry, supporting later understanding of chemical formulas, equations, bonding, and stoichiometry. Although students often rely on memorization strategies to learn periodic table content, this task is challenging due to the large number of elements and the apparent ambiguity in matching some element symbols to their names.

This study explores students’ recall of element names when given element symbols as cues and their knowledge of element locations on the periodic table when given element names as cues. The study also examines …

Longitudinal Dechirping Of Electron Beams Using Negative Identity Drifts In A Transverse Deflecting Cavity-Based Chirper, Alex Desimone

Graduate Research Theses & Dissertations

Electron beam chirping for bunch compression is commonly achieved through off-crest acceleration in radio-frequency accelerating cavities. While it is effective, this method reduces average beam energy and introduces nonlinearities in the beam's longitudinal phase space, which can degrade beam quality in applications such as X-ray free electron lasers. An alternative chirping scheme has been proposed, in which accelerating cavities operate on-crest while transverse deflecting cavities generate the required longitudinal chirp. However, the efficiency of this approach and its impact on beam quality have not been systematically investigated, and the previously proposed dechirping scheme for this system is experimentally impractical. This …

Application Of Paper Spray Mass Spectrometry For Screening Acetylcholinesterase Inhibitors, Diksha Gautam

Graduate Research Theses & Dissertations

Paper spray mass spectrometry (PS-MS) is a novel ambient ionization method that allows chemical and biological samples to be rapidly analyzed with little preparation being required. In this paper, PS-MS was set up and streamlined as an acetylcholinesterase (AChE)-monitoring platform in addition to screening an existing and newly produced AChE-inhibitors. The detection of the substrate, acetylcholine (m/z 146), and the AChE catalysis product, choline (m/z 104) was performed without applying chromatographic separation or derivatization to the dried paper substrates. Assay conditions were optimized in a systematic way, to develop a robust MS-compatible enzymatic workflow.

Calibration curves for both, acetylcholine and …

Evaluating Planting Methods For Native Seed Production And Exploring Granivory In The Northern Great Plains, Allison Dollen

Electronic Theses and Dissertations

Temperate grasslands have faced immense losses due to land use change in the last 150 years and less than half of the overall grasslands that once covered North America remain today. A portion of historic and remaining U.S. grasslands are located within the northern Great Plains (NGP) region which is home to over 1,600 species of native plants and provides many ecosystem services and economic benefits. Initiatives to restore grasslands and their ecosystem services are currently underway, but barriers such as limited native seed supply and granivory hinder restoration efforts. My first chapter addresses knowledge gaps in planting methods for …

Fifty Years Of Research In The Commonwealth Of The Bahamas: A Quantitative Thematic Analysis Of Scholarly Output, Amina Moss, Krista D. Sherman, Williamson Gustave, Kathleen Sullivan Sealey, Charlotte Dunn, Nicola Simone Smith, Lindy Knowles, Christy Gibson

Gulf and Caribbean Research

This study presents the first longitudinal bibliometric mapping of peer—reviewed research on The Bahamas, analyzing 12,113 Web of Science records (1975–2025) using CiteSpace v.6.4.R2. We identified 36 co—citation clusters (modularity Q = 0.7967; silhouette = 0.9231) and 28 keyword clusters (Q = 0.7523; silhouette = 0.8695), revealing 8 thematic trajectories over 5 decades. Early research (prior to 1990) focused on carbonate geology and sea—level dynamics across the northwestern Bahama Platform and Blake—Bahama Basin (Cluster#0, silhouette = 0.939). From the 1990s–mid—2000s, attention shifted to marine fisheries, Aliger gigas, coral reefs, and ecological resilience (Cluster #1, n = 203). In the …

A Comparative Analysis Of The Ecophysiology Between The Invasive Macroalgae Starry Stonewort And The Native Macroalgae Chara To Predict Invasion Potential, Thomas J. Zellmer

All Graduate Theses, Dissertations, and Other Capstone Projects

Starry stonewort (Nitellopsis obtusa) is an invasive macroalga from Eurasia. Currently, starry stonewort’s invasive range consists of the upper Midwest and Great Lakes region, including the Mississippi River. Starry stonewort poses recreational and ecological problems by forming dense canopies that extend through the water column. Starry stonewort occupies a late-season niche space with dense biomass occurring in late July and persisting well into autumn. There is a lack of information on the ecophysiology of starry stonewort. In this study, two populations of starry stonewort, Minnesota and New York, were analyzed to compare regional differences in ecophysiology, along with chara (native …

Algorithmic Medicine And The Duty To Disclose: Informed Consent Through The Lens Of Radiology, Lee Rodriguez

Michigan Law Review

Informed consent is the law’s mechanism for protecting patient autonomy by requiring disclosure of facts that bear on the decision to accept or refuse care. Artificial intelligence now helps decide what is medically true for patients, yet informed consent law still assumes that diagnostic judgment is rendered by a human mind whose reasoning is at least in principle communicable. Radiology has become the leading setting for this tension. AI systems triage worklists, flag suspected abnormalities, and anchor first-pass impressions in ways that guide radiologists’ attention and, in practice, can coauthor diagnostic conclusions while remaining invisible to patients. When patients are …

Essays On Accelerated Failure Time Models For Recurrent Event Data, Emmanuel Masavo Djegou

Doctoral Dissertations

Recurrent event data arise in many fields such as medicine, reliability, insurance, and economics, where the same event may occur repeatedly for a subject. Accelerated Failure Time (AFT) models provide an intuitive framework for relating covariates to event times and offer a useful alternative to proportional hazards models, allowing direct prediction of event timing under right censoring. However, existing AFT extensions for recurrent events, such as accelerated gap time (AGT) models, often fail to account for interventions between events and may not capture complex temporal patterns.

In this work, we first propose a class of semiparametric AGT models incorporating an …

Swimming In Uncertainty: Filling Data Gaps And Providing An Educational Platform For Beach Water Quality At Tybee Island, Georgia, Lukas Roberson

College of Graduate Studies: Theses & Dissertations

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Swimming in beaches water contaminated with high levels of bacteria can make you sick. Current monitoring at the public beaches on Tybee Island consists of weekly monitoring and enumeration of fecal indicator bacteria that takes 24 hours for results. If the number of bacteria exceed regulatory limits, a public health advisory is issued, and affected waters are retested until …

Flexible Membrane Electrodes For Use In High-Capacity Lithium-Ion Batteries, David A. Denemark

College of Graduate Studies: Theses & Dissertations

Wearable electronic devices require high-capacity flexible batteries to improve user comfort and increase usage time per charge. In this thesis, flexible lithium-ion battery (LIB) electrodes are prepared using a novel and scalable phase inversion method, embedding one-dimensional SnO₂ nanowires and double-walled carbon nanotubes within a porous polyacrylonitrile asymmetric membrane. The optimized electrode delivered an initial specific discharge capacity of 915.93 mAh g⁻¹, approximately 2.5 times greater than conventional graphite anodes, with 61.99% capacity retention after 45 cycles. The incorporation of a carbon mesh substrate further enhanced both electrochemical and mechanical performance, achieving 89.46% capacity retention after 45 cycles and a …

Escher: Efficient And Scalable Hypergraph Evolution Representation With Application To Triad Counting, S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das

Computer Science Faculty Research & Creative Works

Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as triad counts is essential, as hypergraphs can reveal intricate group interaction patterns that conventional graphs fail to capture. In realworld scenarios, these networks are often large and dynamic, introducing significant computational challenges. Due to the absence of specialized software packages and data structures, the analysis of large dynamic hypergraphs remains largely unexplored. Motivated by this gap, we propose ESCHER, a GPU-centric parallel data structure for Efficient and Scalable Hypergraph Evolution Representation, designed to manage largescale hypergraph dynamics efficiently. We also design …

High-Resolution Mapping Of Soil Moisture Variation Using Uas Thermal And Multispectral Imagery, Jackline Amma Timah

Theses and Dissertations

In agricultural landscapes, soil moisture regulates hydrologic partitioning, nutrient transport and water quality, land-atmosphere energy exchange that shapes local climate, and ecosystem resilience. However, traditional monitoring approaches, such as in-situ sensors and satellite imagery, often lack the spatial resolution required to capture fine-scale soil moisture variability. This study evaluated whether unmanned aerial system (UAS)-derived thermal, multispectral, and terrain variables can capture fine-scale spatial variability in volumetric water content (VWC) within an SRB in central Illinois.

High-resolution imagery was collected and paired with 50 field-measured VWC observations. Land surface temperature (LST), vegetation indices (NDVI and NDRE), spectral bands, and slope were …

Harnessing Activated Sludge For The Treatment Of Hydrothermal Liquefaction Wastewater: A Proof-Of-Concept Study, Cyrus Li, Jiefu Wang, Meicen Liu, Yi Zheng, Sandeep Kumar, Isamu Umeda, Chandan Mahata, John Norton

Civil & Environmental Engineering Faculty Publications

Although hydrothermal liquefaction (HTL) is the leading technology in converting wet biomass into bioenergy, the treatment of its toxic-laden aqueous phase wastewater presents a major challenge on its path toward commercial viability. This study presents the first-ever assessment of sewage sludge-fed HTL wastewater (SS-HTLWW) treatment and toxic compound removal using municipal activated sludge (AS) by optimizing its cultivation condition. It was found that AS with optimized pretreatment can remove up to 91.2% of the soluble chemical oxygen demand (sCOD) in SS-HTLWW, of which up to 82% can be attributed to biological mineralization and adsorption of sCOD by AS. Conventional bioprocess …

Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

Electrical & Computer Engineering Faculty Publications

This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacoustic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential …

Mtl_Tx: A Multi-Task Transformer Model For Improved Radiation Time-Series Estimation, Hongfang Zhang, Adam Stavola, Hal Ferguson, Bence Budavari, Hongyi Wu, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed …

An Explainable Cs-Mitigation Triangular (Ecsmt) Framework To Secure Graph Neural Networks, Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Predicting Oil Contamination In Water Using Machine Learning On Microbial Compositions, Tong Gao, Isaac Bigcraft, Stephen Techtmann, Issei Nakamura

Michigan Tech Publications

We present a compact and generative machine-learning framework that predicts oil contamination based on microbial community compositions from experimental samples. Our method combines dimensionality reduction with data augmentation and generative modeling to address high-dimensional, non-linear, and sparse microbial data. To reduce the 503-dimensional bacterial composition dataset, we compared three dimensionality reduction techniques: feature importance from random forest, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE). Feature importance outperformed PCA and t-SNE, improving predictive performance and identifying microbial species most strongly correlated with oil contamination. To mitigate data scarcity, we augmented the training data using an augmented data neural …

Optimal Control Analysis Of Malaria Transmission In The Presence Of Insecticide Resistance And Climate Variability In Kenya, Lorna Chepkemoi, Titus Okello Orwa, Samuel Mwalili, Rachel Waema Mbogo, Steeven Belvinos Affognon, Daisy Salifu, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Malaria remains a major public health concern in Kenya, where changing climatic conditions, insecticide resistance, and mosquito behavioral adaptations continue to challenge control efforts. This study develops and analyzes a climate-sensitive malaria transmission model that incorporates mosquito behavior, insecticide resistance, and vector–human ecological dynamics to identify optimal control strategies for the Kenyan context. The model’s well-posedness was established, the basic reproduction number (R0) computed using the next-generation matrix method, and the stability of equilibrium points assessed. Spatial analysis of R0 was performed using temperature and rainfall raster data to map transmission risk across Kenya under varying insecticide use scenarios. Results …

Taxonomy, Distribution And Ecology, Biology, Nutritional Composition, And Conservation Status Of Critically Endangered Lake Chala Tilapia Oreochromis Hunteri (Günther, 1889): Implications For Aquaculture Development, Jonathan Munguti, Mavindu Muthoka, Domitila Kyule, Esther Magondu, Jacob O. Iteba, Kevin Obiero, Paul Orina, Menaga Meenakshisundaram, Tanga M. Chrysantus

All Peer-Reviewed Publications

Oreochromis hunteri Günther, 1889, commonly known as Lake Chala tilapia, is an endemic cichlid restricted to the small crater lake, Lake Chala, on the border of Kenya and Tanzania. This review synthesizes existing knowledge on the taxonomy, and phylogenetic placement, distribution and ecology, and biology including nutritional composition, and conservation status of O. hunteri and identifies knowledge gaps to assess its potential for aquaculture development. The knowledge gaps include its reproductive biology, population structure, and adaptive capacity to environmental and climatic variability. As the type species of the genus Oreochromis, O. hunteri is a unique, genetically isolated lineage within a …