MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems

This thesis presents an integrated framework for mechanical-guided, label-free cell sorting that synergizes high-speed localization with precise classification based on complementary feature modalities. The research addresses fundamental challenges in biomedical cell analysis through interconnected methodological innovations across detection, classification, and real-time processing domains.The cornerstone of this work is the development of Multiplex Image Machine Learning (MIML), a novel architecture that integrates bright-field microscopy images with cellular mechanical properties extracted during microfluidic transit. This hybrid approach achieves 98.3\% classification accuracy in distinguishing human colorectal carcinoma cells from white blood cells—representing an 8\% improvement over image-only methods. MIML demonstrates exceptional transfer learning capabilities, enabling effective classification of visually similar but mechanically distinct cells even with limited training datasets.To enable real-time applications, this research employs knowledge distillation techniques to compress a ResNet50-based teacher model into a student network with merely 0.02\% of the original parameters while maintaining robust classification performance. The subsequent FPGA implementation achieves unprecedented 14.5μs inference latency, establishing a new state-of-the-art benchmark that represents a 12-fold improvement over previous methods. Complementing this classification pipeline, a custom YOLO-based architecture optimized for high-speed microfluidic videos provides real-time cell detection and tracking. This detection framework integrates with Kalman filtering for robust trajectory analysis and extracts on-the-fly mechanical descriptors including deformation indices, velocity profiles, and transition times.By unifying these approaches, the thesis demonstrates a comprehensive system that advances label-free cell classification while maintaining high specificity, minimizing computational requirements, and operating at sub-millisecond latencies suitable for real-time applications. The integrated platform has significant implications for clinical diagnostics, cancer detection, and personalized therapies where label-free, high-throughput cell analysis is essential. This work establishes a cohesive narrative bridging high-speed cell handling with multi-modal data analysis, addressing critical challenges in microscopy-based cell classification and setting the foundation for future breakthroughs in biomedical engineering and cellular diagnostics.</p

The Effects of School-Level Achievement Goals and Student-Level Characteristics on Academic Self-Efficacy

Academic self-efficacy is essential to students\u27 academic success, engagement, and behavioral and emotional well-being. Higher levels of self-efficacy are associated with positive outcomes, while lower levels correlate with poor performance, disengagement, and increased behavioral and emotional risk (BER) symptoms. This issue is particularly critical for African American high school students, who often face unique challenges within the educational system. While previous research highlights the importance of academic self-efficacy, limited attention has been given to school-level contextual factors, such as mastery goal orientation. This study initially sought to examine the influence of context-level mastery goals; however, preliminary analyses revealed minimal variance in academic self-efficacy at the school level, leading to a focus on personal mastery goals as a student-level predictor. Using a quantitative design, this study analyzed data from 4,891 high school students across seven schools. Results revealed that personal mastery goals significantly predicted academic self-efficacy and were protective against the adverse effects of BER for all students. While mastery goals partially mitigated the effects of BER, this protective relationship weakened as BER levels increased. As hypothesized, African American students reported lower self-efficacy than White students. Additionally, students categorized as "Other" reported lower self-efficacy than both African American and White students. These findings provide insight into the relationships between academic self-efficacy, mastery goal orientation, BER, and race/ethnicity, contributing to a deeper understanding of student-level factors that shape academic self-efficacy outcomes.</p

A Comparison of Traditional and Bayesian Methods for Estimating Rates of Change on Direct Behavior Rating-Single Item Scales

Direct behavior ratings (DBR) are a series of assessments that measure behavior change using a combination of systematic direct observation and behavior ratings. To assess student responsiveness to behavioral interventions, educators might consider estimating rates of change on DBR within specified time periods. The primary purpose of this study was to compare the performance of three estimators of DBR score change (ordinary least squares regression [OLSR]; uninformed Bayesian regression [UBR]; informed Bayesian regression [IBR]) in academic engagement and disruptive behavior. Of substantive interest was the extent to which specifying prior knowledge (e.g., trend magnitude and observation variability) affected the precision of rates of change estimated for hypothetical progress monitoring cases at different intervals of data collection (e.g., with four weeks of data, with eight weeks of data, etc.). Priors informed by descriptive analyses improved the precision of slope estimates in both progress monitoring domains. The effects of incorporating informed priors appeared most significant at short durations (e.g., with four weeks of data) and among cases exhibiting relatively high observation variability. The reliability of all estimates improved primarily as a function of duration and magnitude of the simulated true trend rather than estimation method. OLSR and UBR estimates were relatively unbiased. However, minor bias that was initially attributable to IBR decreased as duration increased. Results lend preliminary evidence for developing weakly-to-moderately informative prior distributions before estimating rates of change on DBR in a Bayesian framework. Researchers and practitioners might consider the potential effects of data availability and stability before estimating and interpreting OLSR-derived rates of change in academic engagement and disruptive behavior. The advantages and limitations of each estimator for evaluating intervention effects in prevention-intervention frameworks are discussed.</p

Compression-Aided Privacy and Inferential Separation in Machine Learning

The rapid proliferation of Internet of Things (IoT) devices and the demand for real-time data processing have raised significant concerns about data privacy in machine learning applications. This dissertation addresses these challenges through two key approaches: inferential separation and compression-aided privacy.In inferential separation, we develop methodologies to protect sensitive inferences drawn from high-rate data streams, without compromising data utility. This includes a theoretically grounded framework for protecting sensitive inferences in IoT systems, as well as Decoct-Net, a deep learning-based model designed to sanitize sensitive attributes without compromising non-sensitive information. In the domain of compression-aided privacy, we explore techniques that remove sensitive information from computational models while maintaining their utility. This includes Spectral-DP, a spectral domain perturbation method that enhances the utility of differentially private learning through spectral filtering, and two theoretically rigorous approaches-Randomized Quantization with SGD (RQP-SGD) and Gaussian Sampling Quantization for Federated Learning (GSQ-FL)—which focus on achieving privacy and communication efficiency in resource-limited environments. By combining theoretical insights with empirical validation, this dissertation demonstrates how sensitive information can be effectively removed from data and models. The proposed techniques provide significant advancements in privacy-preserving machine learning, particularly in IoT and edge computing environments, without sacrificing model performance.</p

Understanding Electrochemical Doping in Carboxylated Mixed Ionic-Electronic Conductors: From Ion Uptake to Functional Performance

Organic mixed ionic-electronic conductors (OMIECs) are emerging as key materials for energy storage, bioelectronics, and sensing due to their dual ionic and electronic conductivity. However, the complex interplay between chemical structure, environment, and ion dynamics remains poorly understood. This thesis advances the understanding of electrochemical doping mechanisms in carboxylated polythiophenes by probing ion uptake, transport, and swelling behavior in real time. Chapter 2 investigates the influence of COOH functionality and alkyl spacer length on polymer performance, revealing materials that exhibit low swelling, high aqueous processability, and strong ionic-electronic coupling. Benchmarking studies demonstrated record-setting OECT performance, supported by in situ spectroelectrochemical analysis. Chapter 3 explores the impact of side-chain chemistry by comparing carboxylic acid and ester-functionalized analogs. Operando characterization showed that COOH groups facilitate cation expulsion and deswelling during doping, while ester groups enable cation-free, anion-driven swelling—underscoring the critical role of side-chain polarity in ion transport dynamics. Chapter 4 focuses on pH-regulated doping behavior. The degree of COOH dissociation, governed by its pKa, was shown to modulate both ion uptake and swelling: neutral pH promotes deswelling through stronger cation-polymer interactions, while acidic pH limits dissociation and leads to increased swelling. This work introduces the concept of an electrochemical dissociative balance, where ion flux and volume changes can be tuned without requiring swelling. Collectively, these findings establish key design principles for carboxylated OMIECs with enhanced performance, long-term stability, and minimized swelling across diverse electrochemical environments.</p

Orchestrating Coding and Learning for Reliable and Secure Neural Network Processing

Error correcting output codes (ECOCs) have been proposed to improve the robustness of deep neural networks (DNNs) against hardware defects of DNN hardware accelerators.Unfortunately, existing efforts suffer from drawbacks that would greatly impact their practicality: 1) limited effectiveness due to error propagation and accumulation when DNNs are deep; 2) robust accuracy (with defects) improvement at the cost of degraded clean accuracy (without defects); and 3) absence of theoretical foundations that can elucidate the relationship between codeword design, weight-error magnitude, and network characteristics, so as to provide robustness guarantees. In this dissertation, we bridge the gap and tackle the aformentioned problems in three works. The first work aims to denoise in the early layers of DNNs to diminish the significance of error propagation and accumulation on memristive DNN accelerators. Specifically, we propose a minimum mean square error (MMSE) based method to compensate the weight variations at each layer without extra hardware costs. What\u27s more, we propose a weights-to-crossbar mapping scheme by inverting bits to mitigate the impact of stuck-at-faults (SAFs). Additionally, we propose to use L1 regularization to increase the network sparsity, as a greater sparsity not only further enhances the effectiveness of the proposed bit inversion scheme, but also facilitates other early denoising mechanisms. Experimental results show that our schemes can achieve 40%--78% accuracy improvement under different tasks and DNNs. In the second work of this dissertation, we first identify the root cause of ECOCs\u27 degraded clean accuracy is error correlation, and then propose a novel comprehensive error decorrelation framework, namely COLA. Specifically, we propose to reduce inner layer feature error correlation by adopting a separated architecture, where the last portions of the paths to all output nodes are separated, and orthogonalizing weights in common DNN layers so that the intermediate features are orthogonal with each other. We also propose a regularization technique based on total correlation to mitigate overall error correlation at the outputs. The effectiveness of COLA is analyzed theoretically, and evaluated experimentally, e.g., up to 6.7% clean accuracy improvement compared with the original DNNs and up to 40% robust accuracy improvement compared to the state-of-the-art ECOC-enhanced DNNs. The third work of this dissertation is a fundamental analysis of ECOC through the lens of neural tangent kernels (NTKs). We found that utilizing one-hot code and non-one-hot ECOC is akin to altering decoding metrics from l_2 distance to Mahalanobis distance in clean models, which are defined as those free of weight errors. A distance threshold exists between clean models and non-clean models such that if the distance between a clean output and its nearest codewords is smaller than this threshold, then the DNN can make predictions as if it is free of weight-errors. The threshold is determined by the normalized distance among codewords, the DNN architecture, and the scale of weight-errors. Based on these findings, we further demonstrate how to practically use them to identify optimal ECOCs for simple tasks, which have small number of classes, and complex tasks, which have large number of classes, by balancing the code orthogonality and code distance. Extensive experimental results across four datasets and four DNN models validate the superior performance of constructed codes, guided by our findings, compared to existing ECOCs. The drawbacks of ECOCs mentioned in the first paragraph are addressed through the techniques proposed in this dissertation. Specifically, the early denoising techniques introduced in the first study effectively correct errors in the initial layers, significantly mitigating error propagation and accumulation. By reducing error accumulation, the robust accuracy of ECOCs is substantially improved. To address the second drawback, we identify the root cause of clean accuracy degradation—error correlation—and propose the COLA framework to decorrelate errors. This approach enhances both clean and robust accuracy. Furthermore, the third contribution provides a theoretical foundation that offers guidelines to the research community for further improving ECOC. Together, the three works proposed in this dissertation synergistically enhance each other to develop a practical and robust DNN with ECOC on memristive devices.</p

On Heterogeneous Systems and Data Repositories

Data repositories are software systems that store, retrieve, and analyze data. They are the backbone of computing infrastructure and rely on various core components, including concurrency controls and data structures. Improving their performance is essential to supporting ever-increasing computational demand.With recent trends in computer architecture, it is becoming increasingly important to consider specialized processors and how they are interconnected and can cooperate. The design of these heterogeneous systems for data repositories and their algorithmic components is the primary focus of this dissertation. More specifically, we consider utilizing central processing units (CPUs) with graphics processing units (GPUs) as co-processors and designing our systems and components with these processors in mind. Our methodology is to approach data repositories through the lens of instruction set architecture affinity (ISA affinity), or how well our algorithms and tasks map to specific processor architectures. We further consider the interconnection between processors and the additional latency and performance of moving data between co-processors. The contributions of this dissertation include the design of two systems: a cooperative CPU-GPU key-value store and a transactional system with support for heterogeneous workloads, including hybrid transactional-analytical processing through first-class support for heterogeneous architectures. We also provide an approach to semantic transactional processing through cooperative CPU-GPU processing, an architecture-agnostic framework for coalescing memory accesses in data structures for high performance, and a mapping data structure supporting linearizable point operations and range queries.</p

Latiné Students\u27 Perceptions of Dual Enrollment Programs

The purpose of this study was to understand the characteristics or factors that influence Latiné students\u27 perceptions of access to dual enrollment (DE) programs. Utilizing a modified version of Perna\u27s (2006) socioecological model of college choice, the study sought to answer the following questions: How do student, district and state-level characteristics influence Latiné students\u27 perceptions of access to DE programs? How do perceptions of access differ across DE participation status? Results from within-case and cross-case analyses and analyses from a critical realist perspective found two predominant themes influencing Latiné students’ perceptions of DE accessibility: the DE awareness gap and a sense of belonging. Results also indicate that school districts can significantly influence Latiné students\u27 perceptions of DE access depending on how they market and communicate about DE programs and foster an inclusive school climate.The DE awareness gap, or a lack of knowledge, awareness, or misconceptions about DE programs contributing to perceptions of DE inaccessibility, was evident across student and district-level characteristics. At the student level, results indicated that parents and friends had limited knowledge of DE programs which narrowed their influence on Latiné students’ perceptions. At the district level, the DE awareness gap contributed to Latiné students\u27 lack of access to financial information and misperceptions about who should participate in DE programs. Results also revealed that DE participants had a higher sense of belonging than non-DE participants, suggesting that Latiné students who feel they belong in their schools are more likely to pursue advanced coursework.To increase Latiné students\u27 awareness of DE programs and improve their perceptions of DE accessibility, district leaders should articulate an inclusive, equitable vision for these programs and align their practice to that vision. They should also provide professional development to broaden the understanding of who should have access. Additionally, at the school level, educators need to evaluate how they market and communicate about DE programs, dispel misconceptions, and ensure that all students have an opportunity to participate. Overall, this study underscored the importance of advancing equitable access for Latiné students in DE programs.</p

Additive Manufacturing of Polymer-Metal Systems

{"value":"Modern additive polymer deposition is rapidly advancing across multiple industries. As manufacturing processes evolve, Additive Manufacturing (AM)—particularly AM using polymer direct deposition—has emerged as an affordable, precise, and efficient method for producing complex geometries and customized designs at high production rates [2][3]. Historically, AM has been limited to individual material classes such as metals, ceramics, or polymers, restricting the development of hybrid structures that integrate multiple material types [2].With the advancement of high-performance metals, the demand for stronger and more durable AM parts in applications such as weapons systems, automotive components, and fracture-resistant structures has increased [3]. The electronics industry has further expanded the potential of hybrid materials by introducing polymer deposition onto metal substrates, enabling novel metal-polymer hybrid designs [2]. At a fundamental level, chemical bonds at the polymer-metal interface are weak and generally ineffective for long-term adhesion [3]. This study explores methods to improve the “bonding” of inherently incompatible polymer and metal interfaces, seeking a mechanically robust alternative to traditional adhesive-based joining techniques [2]. While metals and polymers each exhibit distinct mechanical properties, conventional adhesives such as epoxy often result in weak interfacial bonds, susceptible to shear and normal forces, which limit the structural reliability of hybrid components [3]. To address this challenge, this research investigates mechanical interlocking as an alternative joining mechanism [2]. This method involves modifying the metal substrate interface to incorporate irregular surface textures or micro-sized extruded features, which physically interlock with the deposited polymer [3]. In this study, hourglass-shaped arrays, consisting of approximately 400 microstructures per 30 × 20 mm metal substrate, were examined as the primary interlocking geometry [2]. During polymer deposition using the Prusa i3 MK3S+ 3D printer, the warm polymer infiltrates the textured metal surface, effectively forming an interconnected mechanical bond [3]. The resulting structure mimics a Velcro-like effect, where the polymer is physically locked into place by the metal\u27s textured features, significantly enhancing adhesion strength compared to a smooth epoxy-metal interface [2]. This research provides insight into optimized polymer-metal bonding strategies by combining additive manufacturing, surface engineering, and mechanical interlocking [3]. The findings contribute to the development of high-performance hybrid structures with enhanced durability, load-bearing capacity, and resistance to mechanical stresses, paving the way for next-generation polymer-metal applications in aerospace, defense, and automotive engineering [2][3]. ","attr0":"abstract"

Overcoming bacterial antibiotic resistance mechanism using Escherichia coli outer membrane vesicles for novel drug delivery

{"value":"Novel methods to combat resistance mechanism of Gram-negative bacteria are limited by their inhibition or removal through the use of enzymes and active transport pumps in innate or acquired resistance. Antibiotic resistance has become a leading health care concern globally with domestic death reported by the United States Center for Disease Control and Prevention analyzed that approximately 35,000 deaths annually are caused by resistant bacterial infections. Since the discovery of the primary antibiotic classes, no recent discoveries have been made leading development to focus solely on derivatives of current solutions. Isolated Gram-negative bacteria have demonstrated the ability to enhance their resistance mechanism and acquire extensive antibiotic resistance over the entire therapeutic spectrum. Gram-negative bacteria such as Pseudomonas aeruginosa (P. aeruginosa) possess a dual membrane structure moderating transport across the cell wall and is capable of downregulating specific transport proteins to further limit antibiotic entry. Without entry, antibiotics are ineffective against bacteria therefore a new delivery method is required to improve antibiotic activity. Gram-negative bacteria release lipid nanoparticles, outer membrane vesicles (OMVs), during growth to transport material, release toxins, and communicate between species. The cross-membrane transporter OMVs, sourced from Escherichia coli (E. coli) strain JC8031, possess antibiotic loading capabilities facilitated by an active sonication loading technique. Imipenem encapsulated in OMVs, IMI-OMVs, demonstrated potential for a novel antibacterial agent to combat bacterial resistance as an innovative delivery method. E. coli strain JC8031 is a hypervesiculating mutant strain which produced high quantities of OMVs but has demonstrated toxicity in the presence of mammalian cells. E. coli Nissle 1917 (EcN) possesses the same capacity to produce OMVs while having a GRAS (generally regarded as safe) designation from the United States Food and Drug Administration (FDA) indicating its accepted biocompatibility. We propose the use of EcN OMVs as nanocarriers for antibiotics to traverse the Gram-negative bacteria membrane and increase the therapeutic effect against bacteria. In this study we focused on the culturing and characterization of EcN OMVs through purification procedures. The encapsulation efficiency of the active sonication loading technique was optimized and compared to E. coli JC8031 to determine imipenem, a transport protein dependent antibiotic, content. The bactericidal effectiveness of the imipenem loaded OMVs (IMI-OMVs) were studied by treating P. aeruginosa strain PAO1 with encapsulated imipenem, free imipenem, empty OMVs, and unencapsulated imipenem with empty OMVs. We observed that IMI-OMVs sourced from both E. coli strains effectively inhibited the growth of Gram-negative P. aeruginosa. IMI-OMVs demonstrated increased antibiotic activity compared to free antibiotics at the same concentration. EcN IMI-OMVs matched and improved upon the bactericidal effect of E. coli JC8031 while possessing non-toxic characteristics. These results demonstrated the potential of EcN OMVs as novel biocompatible drug delivery carriers to overcome antibiotic resistance. ","attr0":"abstract"