Theory and Algorithms Commons^™

Evops: Evolutionary Patch Selection In The Embedding Space Of Whole Slide Images, Saya Hashemian

Theses and Dissertations (Comprehensive)

Computational pathology increasingly relies on the analysis of Whole-Slide Images (WSIs), which capture tissue specimens at gigapixel resolution. Because a single slide is far too large to process directly, the

prevailing paradigm decomposes each WSI into thousands of small patches and encodes them as high- dimensional feature embeddings using deep learning backbones. While effective, this paradigm carries a

substantial cost: the resulting collections of patch embeddings are computationally expensive to store and process, and they are frequently dominated by redundant, homogeneous, or otherwise uninformative tissue regions that dilute the diagnostic signal. Existing patch selection methods largely depend on heuristic or …

Geovig And Purevig: Geometry-Aware Architectures For Efficient Computer Vision, Omar Ismail

Theses and Dissertations (Comprehensive)

Deploying deep learning models for medical image analysis on mobile devices requires a balance between inference latency, memory footprint, and delineating anatomical boundaries with high accuracy. While Convolutional Neural Networks (CNNs) and mobile Vision Transformers (ViTs) offer efficiency, they often struggle to model the irregular, non-local geometric structures inherent in biological tissues without incurring prohibitive computational costs. In this thesis, we introduce GeoViG (Geometric Vision Graph), an architecture that bridges the gap between efficient grid-based processing and explicit Geometric Deep Learning. GeoViG introduces a novel transition from high-resolution pixel grids to low-resolution dynamic graphs via a SpreadEdgePool operator, a geometry-aware …

Error-Driven Density Control For Compact Gaussian Splatting Under Sparse Supervision, Abdelrhman Elrawy

Theses and Dissertations (Comprehensive)

This thesis studies efficiency and stability challenges in Gaussian-splatting-based reconstruction under sparse supervision. In few-shot novel view synthesis, standard 3D Gaussian Splatting (3DGS) can overfit the limited training views and grow an unnecessarily large number of primitives due to limitations in its Adaptive Density Control (ADC) mechanism. This thesis introduces an error-driven reformulation of ADC that triggers densification using opacity gradients as a lightweight proxy for rendering error, and shows that such aggressive densification must be paired with delayed and conservative pruning to prevent destructive create--destroy cycles. When combined with depth-based geometric regularization, the resulting framework produces substantially more compact …

Consensus And Controversies Of International Guidelines For The Diagnosis, Surveillance, And Management Of Fetal Growth Restriction: An Updated Comparison, Daniele Diane Mascio, Suneet P. Chauhan, Tullio Ghi, Asma Khalil, Juliana G. Martins, Sara Sorrenti, Tamara Stampalija, Fabrizio Zullo, Francesc Figueras

Department of Obstetrics & Gynecology Faculty Publications

OBJECTIVE: To compare areas of consensus and disagreements across contemporary international and national guidelines on the diagnosis, surveillance, and management of fetal growth restriction (FGR).

DATA SOURCES: Electronic searches of MEDLINE from database inception up to March 2026 using MeSH terms and keywords related to FGR and guidelines. STUDY ELIGIBILITY CRITERIA: Critical, structured comparison of national or international guidelines on FGR published since 2010. Final inclusion required unanimous agreement from all authors.

STUDY APPRAISAL AND SYNTHESIS METHODS: Pre-specified extraction across domains: definition; prediction/prevention; surveillance tools and frequency; delivery timing and mode; and labor induction methods. Dual data …

Ai-Driven Penetration Testing For Arm Systems: A Comprehensive Framework With Experimental Validation, Matthew Ragsdale

College of Graduate Studies: Theses & Dissertations

The convergence of artificial intelligence and cybersecurity presents new opportunities for automated penetration testing capable of discovering, prioritizing, and remediating vulnerabilities at machine speed. However, deployment on resource-constrained ARM platforms remains unexplored despite ARM’s dominance in mobile, IoT, and edge computing with over 280 billion chips deployed globally. This thesis presents systematic experimental evaluation of AI-driven penetration testing across four paradigms—traditional machine learning, deep learning, large language models, and reinforcement learning—on three ARM platform tiers: Raspberry Pi 5 (8GB, Cortex-A76), Radxa ROCK 5B Plus (16GB LPDDR5 with NPU), and NVIDIA Jetson Nano (4GB with Maxwell GPU). The experimental framework generates …

Early Icu Physiological Subtyping From Time-Series Data: A Comparative Study Of Feature Complexity, Predictive Performance, And Model Interpretability, Rojeena Khadka

College of Graduate Studies: Theses & Dissertations

Intensive Care Unit (ICU) patients do not follow a single uniform physiological pattern. Patients admitted with the same diagnosis show different clinical trajectories over time making standardized classification and treatment approaches insufficient. The increasing availability of large-scale electronic health records in MIMIC-IV makes it possible to investigate such heterogeneity through data-driven approach that captures how physiology evolves during the early phase of ICU admission. This thesis compares two analytical pipelines designed to identify physiological subtypes from the first 48 hours of ICU time series data. This study then assesses how well these subtypes predict in-hospital mortality. The first approach, referred …

Cogram: A Computational Pipeline For Genome Assembly And Reconstruction Using Graph Neural Networks, William Coggins

College of Graduate Studies: Theses & Dissertations

Genome assembly — the reconstruction of a complete DNA sequence from short, overlapping reads — remains a fundamental challenge in computational biology. A central difficulty is distinguishing true genomic overlaps from spurious connections arising from repetitive sequences, a task that traditional assemblers address through hand-tuned heuristic rules applied to de Bruijn or overlap graphs. This thesis introduces COGRAM (Coggins–Ramasamy Assembly Method), a genome assembly pipeline that reframes sequence reconstruction as an edge classification task on a k-mer overlap graph, replacing heuristic graph cleaning with a learned model.

COGRAM constructs a directed overlap graph from raw sequencing reads using a k-mer …

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 …

A General Algorithm For Assortment Optimization Under Random Utility Choice Models, Tien Mai, Andrea Lodi

Research Collection School Of Computing and Information Systems

This work concerns the assortment optimization problem that refers to selecting a subset of items that maximizes the expected revenue in the presence of the substitution behavior of consumers specified by a random utility choice model. The key challenge lies in the computational difficulty of finding the best subset solution, which often requires exhaustive search. The literature on constrained assortment optimization lacks a practically efficient method that is general to deal with different types of customer choice models (e.g., the multinomial logit, mixed logit or general multivariate extreme value models). In this work, we propose a new approach that allows …

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 …

A New Parallel-In-Time Direct Inverse Method For Nonlinear Differential Equations, Nail K. Yamaleev, Subhash Paudel

Mathematics & Statistics Faculty Publications

We propose a new method for parallelization of the first-order backward difference discretization (BDF1) of the first-order time derivative in nonlinear partial differential equations, such as conservation law equations. The time derivative term is discretized by using the method of lines based on the implicit BDF1 scheme, while the inviscid and viscous terms are approximated by conventional 2nd-order central discretizations of the 1st- and 2nd-order derivatives in each spatial direction. The global system of nonlinear discrete equations in the space-time domain is solved by the Newton method for all time levels simultaneously. For the BDF1 discretization, this all-at-once system at …

Enlem: Ensemble Learning-Based Model To Detect Phishing Websites, Most Nilufa Yeasmin, Md Abu Rumman Refat, Bikash Chandra Singh, Zulfikar Alom, Zeyar Aung, Mohammad Azim

School of Cybersecurity Faculty Publications

Phishing involves manipulating individuals into revealing private data, e.g., user IDs, bank details, and passwords. The observed surge in fraud is related to increased deception, impersonation, and advanced online attacks. Thus, effective phishing detection methods are required to mitigate escalating global phishing threats. Existing methods (e.g., heuristics-based, signature-based, and visual similarity-based methods) attempt to detect phishing sites, and machine learning (ML) and deep learning (DL) methods are effective in the cybersecurity context in terms of learning from data, offering insights, and forecasting. However, independent ML algorithms are limited when handling complex data, and DL techniques surpass traditional ML methods in …

Framework For Task Offloading In O-Ran Architecture For Heterogeneous Computing Applications, Samira Taheri, Neda Moghim, Naser Movahhedinia, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Computational offloading transfers tasks from resource-constrained devices to more capable servers or cloud platforms, improving processing speed and user experience. Open radio access networks (O-RAN's) disaggregated architecture and open interfaces make it suitable for offloading delay-sensitive tasks, enhancing real-time application performance. This study focuses on task offloading in O-RAN, a reference network architecture. Although research on O-RAN is limited, existing work lacks a comprehensive approach to offloading, including offloading layer determination, node selection, and resource allocation based on task types and their latency needs. We propose a delay-aware task offloading framework within O-RAN to support diverse delay requirements, improving offloading …

Quantum Machine Learning Models: Principles, Frameworks, And Computational Challenges, K. A. Jayabalaji, S. Venkata Anand, Dineshkumar Rajendran, Prasanta Chatterjee Biswas, Sardor Omonov, Rubaid Ashfaq

Computer Science Faculty Publications

Quantum machine learning (QML) has become an optimistic avenue of harnessing quantum computation in data-driven modeling, especially of issues with high dimensionality and complicated correlations. Current methods are generally based on fixed or over-parameterized quantum circuits, and hence restricted to scalability as well as unproductive optimization in real-world hardware. This chapter introduces a hybrid quantum-classical learning system that is adaptive and provides principled quantum data encoding, architecture-conscious variational circuit design and resource-optimal optimization. The technique is based on the concepts of quantum architecture search and subspace-preserving transformations to trade expressiveness with trainability, and discretize the quantum model into a classical …

Maxgrnet: A Multi-Axis Vision Transformer With Improved Generalization For Eye Disease Classification Using Explainable Ai With Insertion-Deletion Operations On Fundus Images, Md Mehedi Hasan Santo, Fuyad Hasan Bhoyan, Fuad Ibne Jashim Farhad, Fahmid Al Farid, Sovon Chakraborty, Md Humaion Kabir Mehedi, Jia Uddin, Hezerul Bin Abdul Karim

Computer Science Faculty Publications

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification …

Variational Autoencoder Inverse Mapper For Extraction Of Compton Form Factors: Benchmarks And Conditional Learning, Douglas Adams, Md Fayaz Bin Hossen, Joshua Bautista, Gia-Wei Chern, Simonetta Liuti, Marie Boër, Marija Čuić, Michael Engelhardt, Gary R. Goldstein, Huey-Wen Lin, Yaohang Li

Computer Science Faculty Publications

Deeply virtual exclusive scattering processes (DVES) serve as precise probes of nucleon quark and gluon distributions in coordinate space. These distributions are derived from generalized parton distributions (GPDs) via Fourier transform relative to proton momentum transfer. QCD factorization theorems enable DVES to be parameterized by Compton form factors (CFFs), which are convolutions of GPDs with perturbatively calculable kernels. Accurate extraction of CFFs from DVCS, benefiting from interference with the Bethe–Heitler (BH) process and a simpler final state structure, is essential for inferring GPDs. This paper focuses on extracting CFFs from DVCS data using a variational autoencoder inverse mapper (VAIM) and …

Toward An Event-Level Analysis Of Hadron Structure Using Differential Programming, Kevin Braga, Markus Diefenthaler, Steven Goldenberg, Daniel Lersch, Yaohang Li, Jian-Wei Qiu, Kishansingh Rajput, Felix Ringer, Nobuo Sato, Malachi Schram

Computer Science Faculty Publications

Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental quantum correlation functions (QCFs) that characterize the microscopic structure of hadronic systems within the theory of QCD. Recent advances in scientific computing and machine learning have opened new avenues for addressing this challenge using …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Design And Analysis Of Modern Quantum Neural Network Architectures For Intelligent Systems, Lakshmi Chandrakanth Kasireddy, Prabhakara Rao Kapula, Dineshkumar Rajendran, Neha Bharani, Srikanth Pulipeti, Islombek Khushvaktov

Computer Science Faculty Publications

Quantum neural networks (QNNs) offer a principled pathway for integrating quantum computation with machine learning through superposition- and entanglement-based representations. This chapter proposes an architecture-aware design and evaluation framework for modern QNNs, emphasizing robustness and system feasibility alongside predictive performance. Multiple architectures variational QNNs, quantum convolutional neural networks, tensor-network hybrids, and fully quantum models—are assessed under a unified protocol. Experimental analysis shows that the proposed architecture-search–guided QNN achieves 91.8% classification accuracy and an F1-score of 0.914, outperforming fixed-template variational QNNs by approximately 5.6 percentage points. Under depolarizing noise with probability p = 0.10, the proposed model retains 85.3% accuracy, whereas …

Deep Incomplete Multi-View Clustering Via Hierarchical Imputation And Alignment, Yiming Du, Ziyu Wang, Jian Li, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) aims to discover shared cluster structures from multi-view data with partial observations. The core challenges lie in accurately imputing missing views without introducing bias, while maintaining semantic consistency across views and compactness within clusters. To address these challenges, we propose DIMVC-HIA, a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components: (1) view-specific autoencoders for latent feature extraction, coupled with a view-shared clustering predictor to produce soft cluster assignments; (2) a hierarchical imputation module that first estimates missing cluster assignments based on cross-view contrastive similarity, and then reconstructs missing features using …