真人线上娱乐sis/Project Final Defense Schedule
Join us as the School of STEM master’s degree candidates present their culminating thesis and project work. 真人线上娱乐 schedule is updated throughout the quarter, check back for new defenses.
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Master of Science in Computer Science & Software Engineering
SPRING 2025
Monday, May 19
SAVANNA (sAV) WHEELER
Chair: Dr. Marc Dupuis
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; UW2 (Commons Hall) 327
真人线上娱乐sis: Investigating 真人线上娱乐 Relationships Between SNS Usage, Personal Information Disclosure, And Cybersecurity
As social networking sites (SNSs) become ubiquitous for daily activities, users and regulators have raised concerns with the state of digital privacy and security. Cyberattacks on SNSs have exposed private data of millions of users, and cybersecurity threats propagate through social engineering over SNSs. To determine whether frequent SNS users who disclose personal information are at greater risk of cyberattacks, I conducted a hybrid survey-interview study measuring the correlations between personal information disclosure, SNS usage, cybersecurity practices, and past experiences with cybersecurity threats. 真人线上娱乐 survey findings (n = 276) suggest that SNS usage frequency and usage for MNPS (meeting new people and socializing) or MEPO (make, express, or present more popular oneself) purposes have positive correlations to personal information disclosure. SNS usage frequency and personal information disclosure also had positive correlations with experienced cybersecurity threats, but little correlation with cybersecurity behavior. Interview responses highlighted how subjects experienced cybersecurity threats within SNSs.
Thursday, May 22
ELIAS MARTIN
Chair: Dr. Afra Mashhadi
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Elias Martin’s Online Defense
Project: AuditAI – An Interactive Platform to Audit Question-Answer (QA) Pairs from AI and LLM Agents
AuditAI is an interactive platform to help users audit a question-answer pair that they received from conversations with an artificial intelligence (AI) agent.
This tool is especially relevant to current happenings in the world, as large language models (LLMs) and other AI agents become more prevalent and used in many contexts globally. In my capstone, I built out a framework for users to be able to gain insight into whether their question-answer pair is accurate and appropriate, taking into account machine learning (ML) models combined with other sources of content from the internet as a ground truth (using Google Search to fact check answer).
真人线上娱乐 goal of AuditAI is not to definitively say whether a question-answer pair being audited is correct or incorrect, but to give the user unprecedented access to information at their fingertips to come to their own conclusion about the information they are trying to audit.
PRATHAMESH PRAKASH BHALANGE
Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Prathamesh Prakash Bhalange’s Online Defense
Project: Peer-to-Peer Experience Synchronization System: Enhancing Communication and Control through Chat, Video Streaming, and Admin Features
As the demand for real-time collaboration and media sharing tools has increased in the past decade, the development of real-time innovative communication platforms capable of delivering a seamless communication experience has risen rapidly. Traditional systems rely heavily on centralized infrastructures to maintain coordination across distributed clients. While centralized architecture is effective for smaller-scale deployments, it poses significant challenges in scalability, performance overhead, higher operational costs, and infrastructure complexity.
This project presents a Peer-to-Peer Experience Synchronization System, implemented as a browser extension, that leverages decentralized communication using WebRTC to enable scalable, real-time synchronization without depending on persistent server connections. This project introduces session creation and connection establishment through a lightweight signaling server, which minimizes asset exchange cycles and reduces the server communication burden.
Beyond synchronization, the system integrates a robust communication framework designed to improve user interaction and control. Core features include real-time communication chat functionality, a two-person video conferencing enabling face-to-face video with synchronized audio collaboration, and admin access control mechanisms embedded within the browser extension UI.
By extending the communication mechanisms leveraging WebRTC and unifying these features within a single browser extension, this project demonstrates a holistic approach to decentralized streaming and communication. 真人线上娱乐 system not only reduces infrastructure dependency but also enriches user experience through integrated tools for interaction and control.
VIVEKANANDA REDDY LENKALA
Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
5:00 P.M.; Join Vivekananda Reddy Lenkala’s Online Defense
Project: BuzzMagnet: A Bidding Ecosystem for Democratizing Access Between Social Media Promoters and Marketing Opportunities
In today’s digital landscape, social media usage has grown exponentially, creating opportunities for influencers to reach massive audiences through innovative approaches. However, many influencers struggle to establish sustainable partnerships with businesses that would allow them to monetize their content creation efforts. Simultaneously, businesses face challenges identifying appropriate influencers whose audience and style align with their brand values and marketing objectives. BuzzMagnet addresses this market inefficiency by creating a transparent bidding marketplace where businesses post advertising opportunities and influencers can bid to secure these campaigns. This reverse-auction approach empowers businesses to evaluate multiple potential influencers based on their proposals, while giving influencers of all sizes equal opportunity to showcase their value beyond mere follower counts. 真人线上娱乐 platform provides comprehensive analytics to business owners, enabling data-driven decisions when selecting influencers based on relevant metrics such as engagement rates, and previous campaign performance. By democratizing access to advertising opportunities, BuzzMagnet particularly benefits emerging influencers who might otherwise be overlooked in traditional influencer marketing approaches. For businesses, the platform offers the advantage of discovering high-potential content creators at earlier stages of their careers, potentially securing more authentic promotion at competitive rates. Through this innovative bidding system, BuzzMagnet aims to transform the influencer marketing ecosystem by facilitating more transparent, merit-based connections between businesses seeking promotion and the diverse community of digital content creators.
Friday, May 23
KEVIN JOSEPH GRAFF
Chair: Dr. Wooyoung Kim
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Kevin Joseph Graff’s Online Defense
Project: Dual-Modality Alzheimer’s Detection with Interpretable Models
Early diagnosis of Alzheimer’s disease (AD) is critical for enabling timely intervention and improving patient outcomes. However, traditional diagnostic methods often face limitations in accessibility, accuracy, efficiency, and specifically interpretability. This capstone project explores the application of both Machine Learning (ML) models and Deep Learning (DL) techniques by addressing the limitations but focusing on interpretability for detecting Alzheimer’s disease using demographic tabular data and 3D structural brain MRI scans from the OASIS-2 (Open Access Series of Imaging Studies) dataset. It investigates classical iML models including Decision Trees, Random Forests, and Logistic Regression, applied on demographic tabular data, with an emphasis on interpretability and transparency in addition to predictive performance. Evaluation metrics such as accuracy, precision, recall, and F1-score are used to assess model quality. To further interpret model behavior, global and local agnostic explanation techniques are applied, including SHAP (SHapley Additive exPlanations), Permutation Feature Importance (PFI), and Partial Dependence Plots (PDPs). In parallel, a 3D Convolutional Neural Network (CNN), as one of DL models, is trained to classify patients as either “Demented” or “Non-Demented” based on medical imaging of entire brain volumes. Explainability techniques such as Grad-CAM and saliency maps are employed to generate spatial visualizations that highlight key regions of the brain contributing to each prediction. 真人线上娱乐se visual explanations help bridge the gap between model output and clinical insight, potentially increasing trust in ML-assisted diagnostics. This dual approach provides both structured insights from tabular data and spatial insights from medical imaging, illustrating how iML and DL can complement one another in the context of Alzheimer’s detection.
WonWhoo (Andrew) nah
Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Wonwhoo (Andrew) Nah’s Online Defense
Project: Understanding Software Engineering Principles: Developing a PowerPoint Builder Framework for Luna mHealth’s Authoring System
Luna mHealth has evolved over time, but its architecture required refactoring to improve extensibility and maintainability. This project explores how software engineering principles can transform PowerPoint based educational content into a structured format in the Luna authoring system. At its core, PptxTreeBuilder is a modular framework built upon test-driven development that extracts and organizes essential elements from .pptx files into Luna content modules, ensuring efficiency, readability, maintainability, and scalability. Through this project, I learned how to build a clean architecture, and use lean development principles. Beyond its technical contributions, this system establishes a sustainable, extensible authoring platform, empowering future developers to refine and expand its capabilities.
Wednesday, May 28
PRAGATI AMOL DODE
Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; UW1 (Founders Hall) 370
真人线上娱乐sis: Exploring Classification Methods for Motor Imagery and Execution EEG Signal Fluctuations
Brain-Computer Interfaces (BCIs) offer promising applications in neurological rehabilitation through motor imagery (MI)-based training, which is the “intent” of performing an action. This research addresses the challenge of accurately classifying MI and motor execution (ME) based on Electroencephalography (EEG) signals. This kind of data is often limited by subject variability, non-stationarity, environmental noise during data collection, EEG device quality, and small dataset sizes. For our study, we propose to make use of an external large dataset, including data from 103 subjects (compared to the 9–12 subject datasets used in prior work). One of the main goals of this research is to integrate multiple feature extraction techniques spanning time, frequency, and spatial domains. Effective EEG channel selection was guided by fMRI studies identifying MI- and ME-relevant Brodmann areas, combined with EEG-based statistical analysis, resulting in a refined set of 12 informative electrodes. Several machine learning models (SVM, RF, KNN, XGBoost, MLP) are evaluated, achieving up to 80\% accuracy with improved robustness across subjects. 真人线上娱乐se findings demonstrate enhanced generalizability and support the development of more reliable BCI applications for real-world rehabilitation scenarios.
YUZE WANG
Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Yuze Wang’s Online Defense
Project: 真人线上娱乐 QuickCheck Vision Screening Application: Multi-platform Mobile Development (iOS and Android) and Preparation for Clinical Trials
According to the World Health Organization, roughly 2.2 billion people worldwide have some visual impairment. 真人线上娱乐 key challenge in treating many forms of visual impairment is detection. Visual impairment is often gradual, meaning that people with it may not notice the changes in their perceptions. Also, children who do not initially develop normal visual abilities can lack a baseline of “good” vision to compare their experiences against. And because of that, they may not realize they have vision problems. 真人线上娱乐se impairments can affect individuals’ independence, educational achievements, and mobility, and may also lead to physical injuries and mental health challenges. Traditionally, these vision conditions are expected to be detected in school eye exams. However, eye exams only take place a few times throughout the school year.
QuickCheck is a quick and easy-to-use vision screening tool for mobile devices that can perform vision tests to determine if a patient is likely to have any specific visual conditions as the first step toward diagnosis and treatment.
Throughout the project, functions were restored in QuickCheck. By adding functions for generating and sending result reports, QuickCheck became a standalone application. Also, support for IOS devices was added to QuickCheck during this project, bringing it to a wider user group.
Keywords: Vision Screening, Mobile Application Development, Unity, Xcode
MICHAEL LEE
Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Michael Lee’s Online Defense
Project: Learning Software Engineering Principles: Developing a Navigation System for Luna mHealth
Luna mHealth is a mobile health education platform designed to deliver culturally relevant, offline-accessible content to underserved and low-connectivity communities. Luna transforms PowerPoint-based health materials into structured, interactive learning modules. 真人线上娱乐 system supports both guided and exploratory navigation through sequences of content, using a lightweight mobile interface optimized for usability in resource-constrained settings.
This project focuses specifically on the design and implementation of Luna’s navigation system. Navigation is a core component of the user experience, as it determines how users interact with and progress through health education materials. Foundational software engineering principles were applied throughout the development process, including test-driven development (TDD), the SOLID principles, and the KISS (Keep It Simple, Stupid) and YAGNI (You Aren’t Gonna Need It) philosophies. 真人线上娱乐se practices helped ensure a clean, maintainable architecture focused on simplicity, scalability, and long-term extensibility. 真人线上娱乐 goal was to design a streamlined, intuitive application that enhances the user experience while providing a strong architectural foundation for future development.
JASWANTH SRIVAN LAGADAPATI
Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
5:45 P.M.; Join Jaswanth Srivan Lagadapati’s Online Defense
Project: Understanding Software Engineering Principles: Building and Evolving the Architecture of the Luna mHealth Project
Luna mHealth is a mobile health education platform designed to work offline in underserved communities. As the project grew, it became clear that its architecture needed to evolve. What began as a fast-moving prototype required a more deliberate structure, one that future contributors could understand, maintain, and build upon. In this project, I applied core software engineering principles to rework key areas of the Luna system: the authoring pipeline, the domain model, and the mobile rendering layer. I redesigned the Luna domain model to introduce clearer abstractions like LineComponent, Dimension, and SequenceOfPages, reflecting how real content is structured and consumed. I replaced a monolithic module generator with a modular builder-based framework, allowing content to be constructed in clean, testable steps. On the mobile side, I implemented logic to render line components directly from PowerPoint data, ensuring that visuals appeared consistently and accurately. Through practices like test-driven development, I gained firsthand experience with principles like Single Responsibility and Open/Closed. More than just improving code, this work laid the foundation for a maintainable and extensible system, one that can support Luna’s mission for years to come.
Thursday, May 29
STEPHANIE ANNE MURRAY
Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Stephanie Anne Murray’s Online Defense
真人线上娱乐sis: Exploring Quantum Machine Learning-Enhanced Models for EEG Data Classification
Electroencephalography (EEG) records brain activity linked to both executed and imagined movements, but separating real hand movement from the surrounding noise in high-dimensional EEG remains difficult. Reliable classifiers are therefore vital for accurately tracking patient progress over time. This work is part of a larger initiative, dubbed Smart NeuroRehab Ecosystem, which has two main goals: 1) to propose innovative physical-rehabilitation strategies for certain neurologic conditions (e.g., stroke) using cutting-edge technologies, making therapy more accessible, and 2) collecting EEG data for analysis and building machine-learning (ML) models to classify brain signals.
EEG data are complex and difficult to analyze and classify. In this research we explore a quantum-machine-learning (QML) strategy for that purpose. Compared with traditional ML approaches, qubits and quantum circuits offer a different way to represent and process features, which could lead to more efficient classification.
We implement and analyze a ten-qubit Variational Quantum Classifier (VQC) and compare its performance against a “traditional” classification method—a tuned Random Forest baseline. An existing publicly available data set is used for the experiments, which involve discriminating between specific motor tasks based on signals collected from a 64-channel EEG device.
Across 40 preliminary runs, the VQC achieves macro-F1 ≈ 0.75, accuracy ≈ 0.76, and AUROC ≈ 0.83, outperforming the Random Forest (macro-F1 ≈ 0.71, AUROC ≈ 0.79). 真人线上娱乐 majority of experiments were performed on a quantum simulator, with a subset of jobs submitted to a cloud-based quantum computer for testing.
真人线上娱乐se findings show that hybrid quantum-classical models can match and occasionally surpass strong classical pipelines without expanding the computational footprint, offering a practical path toward longitudinal EEG monitoring within the scope of the Smart NeuroRehab project. Moreover, because quantum computing is still emerging, this work may open avenues for new advancements in the field.
MANU HEGDE
Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Manu Hegde’s Online Defense
Project: Project TLDR: Standalone desktop application for question answering and summarization using resource-efficient LLMs
This project presents the design and development of a standalone desktop application for offline question answering and summarization over a user-provided document corpus, using resource-efficient large language models (LLMs). Targeted for Apple’s M1/M2 hardware, the application leverages on-device computation via the Apple Neural Engine (ANE) and Metal shaders, exploring the use of the NPU beyond traditional CoreML applications.
真人线上娱乐 application addresses key concerns around data privacy, resource efficiency, and accessibility. Unlike cloud-based services that require constant internet access and raise privacy risks, this application offers a secure, local alternative optimized for researchers and students. It features a graphical interface and supports retrieval-augmented generation (RAG) over the user’s corpus, all while utilizing only a fraction of system resources to support seamless multitasking.
Evaluation is conducted using both functional metrics (e.g., BERTScore against ChatGPT outputs) and non-functional metrics (e.g., memory and CPU usage). 真人线上娱乐 result is a practical, efficient application that enables interaction with large academic corpora while preserving system responsiveness and data confidentiality.
Friday, May 30
VEDANG PARASNIS
Chair: Dr. Geethapriya Thamilarasu
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Discovery Hall 464
Project: Linux Kernel Integrated Security Framework for Real-Time Prevention of DNS Data Exfiltration in Distributed Environments
Data exfiltration remains one of the most persistent and sophisticated threats in cybersecurity, with DNS frequently exploited as a covert channel for tunneling and command-and-control (C2) operations. Advanced persistent threat (APT) malware leverages DNS not only for stealthy data exfiltration but also for remote exploitation across multiple compromised nodes using techniques such as remote code execution, port forwarding, reverse tunneling, and process side-channel attacks. 真人线上娱乐se threats are especially critical for hyperscalers managing large-scale server data planes running on Linux, where data sovereignty and integrity are paramount. DNS’s ubiquity, inherent security flaws, and mandated unblocked availability on firewalls make it a high-risk vector for stealthy breaches and C2 attacks—particularly difficult to prevent in real time across distributed environments.
Existing solutions for DNS exfiltration security primarily rely on passive traffic analysis from centralized locations, using anomaly detection based on statistical heuristics and machine learning models trained on DNS traffic anomalies. Others depend on domain reputation scoring and static blacklists—all operating in userspace. However, these approaches are inherently reactive, slow to respond, increase latency, lack security enforcement at the endpoint, and are often ineffective against stealthy, adaptive C2 implants that utilize domain generation algorithms (DGAs) to obfuscate attacker infrastructure. As a result, they offer no guarantees of preventing data loss before detection and are inefficient at combating C2 attacks across distributed environments. By the time an alert is triggered, malicious commands may have already been executed and significant damage inflicted—especially in the case of advanced C2 botnet vectors.
This project develops a novel, scalable framework for real-time prevention of DNS-based data exfiltration across distributed environments. It uses an agent-based, endpoint-centric approach, embedding security code throughout the Linux kernel network stack, the mandatory access control layer, and other core kernel subsystems. 真人线上娱乐 framework performs deep packet inspection for advanced DNS payload analysis by parsing the DNS protocol inside the Linux kernel via eBPF, supported by real-time inference from a deep learning model trained on diverse data obfuscation techniques to detect malicious exfiltrated DNS payloads. Malicious C2 implants are terminated at the source on the endpoint, with detailed observability metrics exported to characterize implant behavior—minimizing dwell time, accelerating incident response, and containing threats immediately. Additionally, the framework supports event streaming, dynamic domain blacklisting on DNS servers, in-kernel network policy enforcement, and cross-endpoint security policies for scalability. Experimental results demonstrate horizontal scalability, deep learning model accuracy near 99%, and sub-second response and containment of sophisticated DNS C2 attacks—all with negligible or no data loss. This approach enhances real-time visibility, accelerates threat containment, and fortifies endpoint defense against emerging threats across distributed environments.
BRENDA SUGEY VEGA CONTRERAS
Chair: Dr. Geethapriya Thamilarasu
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Discovery Hall 464
Project: SAFE-MD: A Secure Architecture Framework for Enhanced Mobile Development
真人线上娱乐 use of mobile applications has increased over the years since the introduction of smartphones and the creation of centralized repositories like Google Play and App Store, where mobile health or mHealth apps have gained traction over. 真人线上娱乐ir user base is expected to continue to grow, making them a target for attackers as they can carry over critical tasks and contain sensitive patient information. Different vulnerabilities have been identified, with multiple contributing factors. 真人线上娱乐se often arise from a combination of insufficient knowledge of security best practices, limited expertise in implementation, and practical challenges, such as the need to meet development deadlines and manage financial limitations.
Research has proposed conceptual and prototypic frameworks to mitigate these security risks. However, most implementations focus exclusively on a single platform, primarily Android. 真人线上娱乐refore, it is crucial to develop solutions that enable mHealth app developers to reduce the likelihood of user data breaches across multiple mobile platforms.
This project presents SAFE-MD, a comprehensive security framework with a platform-agnostic approach that facilitates the integration of security mechanisms into mHealth applications at any time during the app development lifecycle. To ensure multiplatform compatibility, the security framework is built using Kotlin Multiplatform, with an initial focus on Android and iOS devices. Different platform-agnostic and platform-specific security mechanisms were integrated to protect users, such as password strength validation and biometric authentication.
To validate the effectiveness and portability of the security framework across different mobile development environments, two mock mHealth apps were built using Kotlin Multiplatform and Flutter, running on two of the most attractive platforms for attackers, Android and iOS. Preliminary findings during the implementation phase indicate that the incorporation of the platform-agnostic security mechanisms of the SAFE-MD in Android and iOS mock apps for both Kotlin Multiplatform and Flutter, can achieve smooth integration across all the instances of the mock app. However, the availability of platform-specific security mechanisms in the security framework may vary across platforms due to architectural or implementation differences.
SAFE-MD simplifies the integration of security mechanisms in mHealth apps for multiple platforms and lays the foundation for future enhancements, including broader platform support, the improvement of current security mechanisms, and the integration of new ones to protect users.
Monday, June 2
ATUL AHIRE
Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Join Atul Ahire’s Online Defense
Project: MASS Java Library Towards it’s Use for a Graph Database System
Distributed graph databases are foundational to modern applications such as social networks, recommendation systems, and transportation platforms. 真人线上娱乐 MASS (Multi-Agent Spatial Simulation) framework—an agent-based parallel computing library for distributed systems—designed to simulate spatial computations by deploying mobile agents over distributed data structures such as graphs. MASS’s agent-based graph computing model enables intuitive programming and parallel traversal, making it well-suited for scalable graph database applications. This research intends to address two core limitations in its graph database model: inefficient vertex placement and high communication overhead during agent migration. To improve data locality and reduce inter-node communication during graph traversal, we explored and integrated graph partitioning strategy which minimizes edge cuts and better aligns with graph topology. This approach significantly reduced inter node communication for graph benchmarks like Triangle Counting and Articulation Points by localizing vertex adjacency across fewer computing nodes.
真人线上娱乐 second challenge targeted the messaging inefficiencies caused by TCP-based agent migration. We redesigned the MASS communication infrastructure using Aeron, a high-performance UDP messaging library, to support low-latency, high-throughput data exchange. This refactoring introduced a dual communication pattern: a structured master-worker protocol for control synchronization and a peer-to-peer mesh for direct agent exchanges. By combining optimized graph partitioning with Aeron-based messaging, the enhanced MASS system reduced the communication latency and increased agent migration efficiency across large, distributed graphs.
VANESSA ARNDORFER
Chair: Dr. Michael Stiber
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Vanessa Arndorfer’s Online Defense
真人线上娱乐sis: Network Behavior Analysis of Spike Timing Dependent Plasticity (STDP) in Simulated Neural Networks
真人线上娱乐 machine learning landscape is rapidly evolving with researchers often turning toward
nature for inspiration. Understanding the development of neural networks in vivo contributes
significant transferrable insight for advancing both neuroscience and computational research.
This project applies a multiplicative Spike Timing Dependent Plasticity (STDP) model to
the weighted graph output from neural growth simulations and analyzes the resulting spike
and weight changes over time. This preliminary investigation establishes a baseline process
for understanding the effects of STDP on a neural network and provides a framework for
defining the resulting network behavior. Through rigorous data analysis, we qualify bursting
behavior during the refinement phase, analyze the progressive effects of STDP on synapse
weights, and compare how the network behavior changes between the growth and refinement
phases of neural development.
Tuesday, June 3
AVIKANT WADHWA
Chair: Dr. Michael Stiber
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Join Avikant Wadhwa’s Online Defense
真人线上娱乐sis: Abstractions for Code Migration from CPU to GPU in Simulation Domain
Large-scale simulations are crucial in science, enabling the modeling of complex phenomena that are difficult to study empirically. As they scale, they demand greater performance and efficiency. To meet this need, computing has shifted toward heterogeneous architectures that combine CPUs and GPUs. While effective, this shift introduces software engineering challenges, making abstraction an increasingly important solution. Abstractions hide low-level implementation details behind clean interfaces, improving clarity and reducing complexity.
This thesis reviews existing abstractions, analyzing their integration effort, performance trade-offs, and limitations. It presents the design and implementation of DeviceVector, a lightweight abstraction that unifies host and device memory management in Graphitti, a high-performance graph-based simulation platform. DeviceVector reduces code duplication, introduces CPU-GPU data relationship, and abstracts CUDA boilerplate through an interface that closely mirrors a standard C++ container. 真人线上娱乐 thesis also discusses design approaches for extending support in the future to object hierarchies and general function-level abstractions, further minimizing logic duplication between host and device code. Overall, this work highlights how thoughtful abstraction design can bridge the usability-performance gap in heterogeneous computing systems.
WILLIAM D. SELKE
Chair: Dr. Wooyoung Kim
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join William D. Selke’s Online Defense
真人线上娱乐sis: Pairwise Normalization Reveals Predictive Immunosignatures in CAR-T Cell 真人线上娱乐rapy
Immunoprecipitation (IP) assays are widely used to quantify protein-protein interactions (PPIs), yet their susceptibility to variability in protein concentration and experimental conditions often obscures meaningful biological signals. This variability poses a significant challenge for downstream analyses, particularly machine learning (ML) models that require well-separated feature spaces. To address this, we introduce Pair Vector Centralization (PVC), a normalization strategy that eliminates concentration-dependent noise while preserving biologically relevant variation. PVC operates by pairing each altered-state sample (e.g., Cytokine Release Syndrome, Neurotoxicity) with a corresponding baseline (Optimal Response) under similar experimental conditions, then transforming these pairs into vectors of change. A midpoint centering step further removes global shifts unrelated to the underlying phenotype.
We applied PVC to a high-dimensional, high-variability dataset comprising 121 PPIs from 98 CAR-T therapy patients across 27 independent experiments. Compared to standard normalization techniques. including Z-score, quantile normalization, and PCA; PVC substantially improved both clustering quality and predictive performance. Notably, the F1 score of a baseline Random Forest classifier improved from 75.71% to 95.24% post-normalization. Heatmap comparisons of multiple normalization methods and ML classifiers further demonstrate PVC’s consistent superiority across classification and unsupervised clustering metrics.
真人线上娱乐se results underscore PVC’s potential as a robust preprocessing tool for biological datasets affected by batch effects and concentration variability. Its applicability extends to other domains involving biological state transitions, including drug perturbation studies and disease progression modeling. Future work will explore its integration with broader computational pipelines and validation across additional high-throughput datasets.
NEERAJ BAIPUREDDY
Chair: Dr. Arkady Retik
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Neeraj Baipureddy’s Online Defense
Project: Smart Umpire: AI-Powered Decision Support and Analytics for Grassroots Cricket
While professional cricket benefits from advanced technologies like DRS, local and amateur-level matches often lack reliable tools for decision-making. 真人线上娱乐 few existing solutions for grassroots cricket are typically limited to LBW detection and do not support other important decisions such as run-outs and no-balls. 真人线上娱乐y are also costly and not accessible to most local teams.
Smart Umpire aims to provide a more complete and affordable alternative by using computer vision and machine learning to bring real-time decision support to local matches. 真人线上娱乐 system analyzes video footage to detect LBWs, run-outs, and no-balls, as well as delivery patterns such as bounce location, deviation after pitching, and length classification. All models are developed in Python using custom datasets trained and tested on the Roboflow platform.
This project establishes the foundation for a mobile application that will offer decision support, player analytics, and a subscription-based access model—making professional-level insights available to the everyday game.
Wednesday, June 4
PHAT TIEN TRAN
Chair: Dr. Wooyoung Kim
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Phat Tien Tran’s Online Defense
真人线上娱乐sis: StackBERT-Enhancer: A Dual-Layer BERT-Based Framework for Enhancer Identification and Strength Classification in Genomic Data
Enhancers are crucial regulatory DNA sequences that control gene expression and play essential roles in cellular differentiation, development, and disease. Despite their significance, accurately identifying and classifying enhancers remains challenging due to their context-dependent activity, complex sequence features, and lack of consistent structural patterns. Traditional computational approaches, such as sequence heuristics and shallow machine learning models, often fail to capture the long-range dependencies and multi-scale contextual information embedded in genomic sequences. Moreover, they typically lack interpretability, making it difficult to extract relevant biological understanding.
This thesis introduces StackBERT-Enhancer, a novel deep learning framework designed to overcome these limitations. It addresses two core tasks: distinguishing enhancer sequences from non-enhancer sequences and classifying identified enhancers based on their functional strength. 真人线上娱乐 framework leverages multiple transformer-based language models, each trained independently on DNA sequences tokenized with different k-mer sizes. This multi-k-mer strategy enables the capture of sequence dependencies and context across various scales. 真人线上娱乐se individual models then serve as base learners within a stacking ensemble architecture. This ensemble approach significantly enhances classification accuracy, robustness, and generalization performance across both tasks, achieving state-of-the-art results.
To handle the computational demands of large genomic datasets, the framework employs distributed multi-GPU systems for efficient model training and hyperparameter optimization. In addition, to bridge the gap between model predictions and biological insight, interpretability techniques are incorporated. SHapley Additive exPlanations (SHAP) are used to evaluate feature importance, while attention score analysis supports sequence motif discovery.
Overall, StackBERT-Enhancer combines advanced machine learning with biological insight to create a robust and interpretable framework for enhancer identification and strength classification. By uncovering complex sequence patterns, it holds strong potential for applications in disease modeling and broader biomedical research.
HOLT OGDEN
Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Holt Ogden’s Online Defense
Project: Multi-GPU Parallelized Agent Based Modeling
Agent based modeling (ABM) is a method for simulating emergent behaviors in complex systems by modeling the interactions of individual agents. 真人线上娱乐se simulations often require substantial computational resources, necessitating increased parallelization and spatial scalability to produce usable results. One approach is to use a computer’s Graphics Processing Unit (GPU) to allow increased parallelization by utilizing the greater number of threads available on the GPU compared to the Central Processing Unit (CPU). 真人线上娱乐 Multi-Agent Spatial Simulation (MASS) library for NVIDIA’s Compute Unified Device Architecture (CUDA) provides a platform that allows users to write ABM programs to run on the GPU. However, these programs are limited in their spatial scalability by the memory available on a single graphics card. In this project, we improved the MASS CUDA library’s Place object implementation by extending it to function over multiple GPUs connected via NVIDIA NVLink, increasing the potential simulation size of MASS CUDA programs. This required splitting ABM data between multiple graphics cards, ensuring memory synchronization between the cards, and recombining result data at the end of the simulation. 真人线上娱乐se changes improved the runtime of ABM simulations by 35% and increased the maximum simulation size by 75%. In addition, these changes were designed to be abstracted from the user so that minimal changes are required by ABM programs written for previous versions of MASS CUDA. Overall, this project significantly expands the computational resources available to the MASS CUDA library, allowing the running of larger and more complex ABM programs.
AZMEEN KAUSAR MOHAMMAD
Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Azmeen Kausar Mohammad’s Online Defense
真人线上娱乐sis: Enhancing Coach Bot Features through Adaptive Learning using NLP and Fine Tuning
Artificial Intelligence (AI) is revolutionizing mental health care by providing innovative, scalable, and adaptive training solutions for caregivers. Caregivers often face challenges in adapting to diverse mental health scenarios and require tools that support personalized and effective training. To address this, Coach Bot was developed to train caregivers in empathetic communication through scenario-based learning. However, to meet the diverse needs of caregivers and make learning more effective, it is essential for the chatbot to adapt dynamically to individual user preferences and provide real-time, actionable feedback. This project focuses on developing an advanced AI-powered Coach Bot that incorporates adaptive learning paths and personalized feedback mechanisms. A detailed review of existing literature on conversational agents and applications of NLP in mental health was conducted, along with an analysis of data classification to difficulty levels and caregiver response evaluation. 真人线上娱乐 overarching goal of this work is to enhance the design and functionality of adaptive chatbots to improve caregiver training and ultimately support better mental health outcomes and include detailed feedback to the user along with the EPITOME framework scores.
AMALAYE OYAKE
Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering
5:45 P.M.; Discovery Hall 464
真人线上娱乐sis: Resilient State Convergence in Interplanetary and Edge Networks Using Conflict-Free Replicated Data Types (CRDTs)
Embedded devices operating in harsh environments, such as outer space or remote locations, present significant challenges for system design, mainly due to their functionality under conditions that disrupt traditional assumptions. Advancements in computing power and the miniaturization of modern electronics have enabled the implementation of distributed computing systems in isolated terrestrial locations as well as in space. 真人线上娱乐 most extreme environments, including polar regions, outer space, and places characterized by resource limitations such as power or network connectivity, stand to benefit from these advancements in networking, computing, and Artificial Intelligence (AI).
Space presents particular challenges, including high communication latency, intermittent connectivity, obscured signals, and bodies in motion. Deploying enterprise software or conventional IT approaches in these extreme conditions is ineffective. Conventional enterprise software solutions adhere to design principles that are tested to their limits, often leading to fragile and unreliable systems.
Disruption-tolerant networking (DTN) is a relatively new set of Internet standards designed to address the challenges networked systems face in highly remote locations. It enables communication despite delays, disruptions, and disconnections. One limitation of the DTN standards is the lack of a standardized mechanism for exchanging participant contact information and validating the updated contact information. This exchange is crucial for maintaining a coherent global state across a DTN network, an essential capability for real-world applications such as Edge Networks and satellite communications.
This research explores theoretical methods for exchanging contact information among participants and applies these theories to DTN. Specifically, it examines Eventual Consistency 真人线上娱乐ory in conjunction with established Distributed Snapshot algorithms such as Chandy-Lamport, Lai-Yang, Spezialetti-Kearns, and Epidemic Model Gossip Protocols, assessing how prior research in distributed systems can be applied to extremely remote systems. 真人线上娱乐 study proposes suitable algorithms and combined approaches, evaluates their advantages and disadvantages, and presents quantitative results demonstrating system performance.
Thursday, June 5
SUMIT HOTCHANDANI
Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Join Sumit Hotchandani’s Online Defense
Project: Link Prediction in Agent-Based Graph Database System
This work introduces a comprehensive link prediction framework within an agent-based graph database system built on the Multi-Agent Spatial Simulation (MASS) library. Extending prior enhancements to the MASS framework—including support for property graphs and distributed shared graph infrastructure—the system integrates both topological and embedding-based approaches to infer potential links in complex networks. Classical topological methods such as Adamic-Adar, Resource Allocation, and Preferential Attachment are implemented alongside Fast Random Projection (FastRP), a dimensionality reduction technique used to generate node embeddings. 真人线上娱乐se embeddings are subsequently leveraged in a k-Nearest Neighbors (kNN) classifier to identify probable future connections. 真人线上娱乐 FastRP algorithm is realized using a novel agent-based propagation mechanism that exploits MASS’s parallel and distributed architecture.
By embedding these capabilities into the core MASS library, the proposed framework enables scalable, interpretable, and extensible link prediction for applications in domains such as social networks, biological systems, and recommendation engines.
SURBHI GUPTA
Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Surbhi Gupta’s Online Defense
Project: Developing Culturally Aware and Multilingual AI for Mental Health Support
真人线上娱乐 development of multilingual and culturally adaptive AI-based chatbots to facilitate mental health intervention is focused on and explored in this project. This involves the chatbot providing its users with faith-based reassurance, emotional support, and practical guidance while taking cognizance of cultural nuances through the fine-tuning of its natural language processing models. It finally achieves culturally relevant, semantically accurate responses by applying techniques such as prompt engineering, translation evaluation, and answer strategy alignment. To validate the statements as mentioned earlier, the following metrics were used for evaluation purposes: BLEU scores to analyze translation consistency, recall-based accuracy for strategy containment, and Cultural Alignment Score (CAS) as a measure of faith, family, and affective sensitivity within the chatbot construct. Human evaluation, to ensure the response’s relevance, empathy, and cultural appropriateness, was also conducted. 真人线上娱乐 system aligns well with therapeutic strategies. Currently, the chatbot is serving four cultural groups, and future developments will enable the coverage of more diverse communities. Furthermore, emphasis will be placed on user personalization and increased human feedback input to help grow the system’s adaptability and effectiveness.
SHARAN VAITHEESWARAN
Chair: Dr. Kelvin Sung
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Sharan Vaitheeswaran’s Online Defense
Project: Data-Driven Washington College Grant Prediction
真人线上娱乐 Washington State Grant (WSG) is one of the state’s largest sources of need-based financial aid, yet students often struggle to anticipate how much they will receive. Existing tools offer limited personalization and often overlook the complexity of eligibility rules, leaving students with uncertainty during college planning. This project addresses that gap by developing a machine learning–based system to predict individualized WSG award amounts using a combination of institutional and macroeconomic data.
We built on the College Affordability Model (CAM), a curated dataset of approximately 5,000 student-year records from Washington’s public universities spanning 2008 to 2022. 真人线上娱乐 dataset includes features such as household income range, dependency status, institution group, and actual WSG awards. To provide broader context, we enriched this data with external variables including GDP per capita, Consumer Price Index (CPI), graduation rates, and state education appropriations. 真人线上娱乐se additions were informed by expert input and policy review, and aimed to capture systemic factors influencing annual funding patterns.
After experimenting with several modeling approaches, we selected gradient-boosted decision trees (GBDTs) for their strong performance on tabular data. We compared CatBoost, LightGBM, and XGBoost, training each model using a consistent pipeline that included 10-fold cross-validation, hyperparameter tuning, and lag feature integration. CatBoost emerged as the top-performing model, offering high accuracy with minimal preprocessing.
真人线上娱乐 final CatBoost model achieved an R? of 0.94 and Root Mean Square Error (RMSE) of $670 for a mean value of $4490, demonstrating strong predictive performance. A Shapley Additive Explanations (SHAP) analysis confirmed the model’s interpretability, highlighting key features such as income range, dependency status, institution group, and prior-year funding metrics.
This work shows that combining student-level and macroeconomic data can produce reliable, individualized predictions of financial aid outcomes. 真人线上娱乐 resulting tool has the potential to help Washington students make more informed decisions about college affordability with greater confidence and clarity.
JACOB (JAKE) KEMPLE
Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Jacob (Jake) Kemple’s Online Defense
真人线上娱乐sis: Evaluating Vision-Language-Action Models in Robotic Manipulation: Performance, Implementation, and Comparison with Deterministic Systems
Robotic manipulation systems typically use deterministic policies for perception, decision-making, and task planning, which achieve millimeter-level precision but require extensive
specialized development and cannot easily generalize to new tasks. Emerging vision-language-action (VLA) foundation models promise to reduce this specialized effort and inflexibility through learned multimodal reasoning. However, their practicality in the real world and associated development costs remain largely unknown.
This thesis presents a real-world comparison of a strong open-source VLA foundation model (OpenVLA-7B) against a fine-tuned deterministic control system. Both systems are evaluated on identical hardware consisting of a WidowX 250 6-DoF robotic arm, an Intel RealSense D415 camera, an NVIDIA Jetson AGX Orin edge computer, and the ROS 2 (Robot Operating System 2) framework. Each system repeatedly executes a pick-and-place robotic task under randomized initial conditions. Performance is measured using goal-oriented, object-centric metrics of accuracy, repeatability, and cycle time, adapted from ISO 9283 standards. Additionally, a qualitative analysis examines the installation effort and configuration challenges associated with each system.
真人线上娱乐 primary contributions of this research include: (i) a comparative evaluation of performance and setup complexity between robotic systems utilizing a VLA-based control policy and conventional deterministic control logic; (ii) documentation of hardware, software, and configuration challenges encountered during VLA system implementation; and (iii) qualitative insights from real-world deployment, emphasizing usability and adaptability.
Results indicate that current VLA foundation models underperform compared to deterministic control systems in terms of accuracy, repeatability, and cycle time, limiting their immediate viability for production-level robotic tasks. However, the inherent flexibility of VLA models suggests strong potential as future replacements for deterministic approaches, contingent upon improvements through fine-tuning, future optimizations, enhanced integration frameworks, and better overall performance metrics. 真人线上娱乐se findings offer practical insights and set realistic expectations for developers considering transitioning from deterministic robotics systems to VLA-based implementations.
Friday, June 6
PRATIK NARSING JADHAV
Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Join Pratik Narsing Jadhav’s Online Defense
Project: Multilingual Carebot and Coachbot
This capstone enhances the iCare project by developing Multilingual Carebot and Coachbot—two conversational AI agents designed to provide mental health support and caregiver training in both English and Spanish. To address disparities in access for non-English speakers, the project fine-tunes advanced LLaMA-based large language models using domain-specific datasets and introduces a multilingual framework that dynamically adapts responses based on user-selected language while preserving therapeutic alignment. Carebot delivers empathetic support using psychologically informed dialogue strategies, while Coachbot generates and evaluates caregiving scenarios across affective and interpretive dimensions. 真人线上娱乐 system is evaluated through traditional NLP metrics (BLEU, ROUGE, Perplexity), human feedback, and LLM-as-a-Judge assessments, along with techniques like Active Listening and Socratic questioning. Results show that the LLaMA 3.1 8B Instruct model consistently outperforms others in empathy and coherence, demonstrating that instruction-tuned LLMs can be effectively adapted for high-quality, multilingual mental health support.
JEREMY GAO
Chair: Dr. Kelvin Sung
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Jeremy Gao’s Online Defense
Project: Real Time Snow Deformation
In modern interactive 3D applications, realistic snow deformation plays a vital role in enhancing environmental immersion and visual fidelity. This project introduces a real-time solution for simulating dynamic snow surface deformation in response to object interaction. By combining orthographic depth capture, GPU tessellation, and heightmap-based vertex displacement in a unified rendering approach, the system delivers high visual fidelity and remains efficient across different object types and interaction scenarios.
真人线上娱乐 solution consists of a pipeline with three core stages: pre-processing, geometric processing, and illumination. In the pre-processing stage, an orthographic camera positioned beneath the snow surface captures depth information from deforming objects and updates a global heightmap in real time. 真人线上娱乐 geometric processing stage adaptively tessellates and dynamically refines mesh resolution in regions requiring high detail, enabling smooth and continuous deformations. 真人线上娱乐 final illumination stage recalculates surface normals based on the deformed geometry and enhances them using reoriented normal mapping. This allows lighting to reflect both the physical contours of the displaced surface and subtle variations introduced by the deformation, resulting in a more cohesive and dynamic visual effect. Together, these three stages form a coherent pipeline that enables real-time snow deformation with high visual fidelity and computational efficiency, supporting a broad range of artistic and gameplay requirements.
Extensive testing demonstrates that the system is capable of handling a diverse set of object shapes—from sharp-edged cubes to organically irregular models—with high fidelity. 真人线上娱乐 delivered solution achieves a balance between realism and performance, operating efficiently under interactive conditions while supporting detailed visual effects such as footprint trails and rim lifting. Although current limitations include terrain flatness and lack of force-based deformation, the framework provides a solid foundation for future extensions toward physically grounded and terrain-adaptive snow simulation.
AMULYA HOLENARASIPURA NARAYANA
Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Amulya Holenarasipura Narayana’s Online Defense
Project: Enhancing a Content Management System for Language Learning: Performance, Usability, and Engagement (MeTILDA)
真人线上娱乐 extinction of languages is a global concern, with over 1,500 languages at risk of disappearing by the end of this century. Melodic Transcription in Language Documentation and Analysis (MeTILDA) is a web-based toolset designed to support the documentation, preservation, and education of endangered languages. As part of this initiative, the project focuses on enhancing the Content Management System (CMS) of MeTILDA, an e-learning platform specifically developed to facilitate language preservation.
真人线上娱乐 platform is designed to engage indigenous communities, linguists, and educators specializing in language preservation by providing an interactive and user-centric learning experience. To improve the usability and effectiveness of the system, several key features are introduced for enhancing teaching and learning experience. 真人线上娱乐se include a teacher dashboard for visualizing student performance, grading assignments, creating announcements, and sending notifications. Additionally, students can access their test and assignment grades, utilize the Play and Learn feature for an engaging language learning experience.
真人线上娱乐 platform leverages a modern web technology stack to ensure scalability, responsiveness, and maintainability. 真人线上娱乐 frontend is developed using React.js, providing a dynamic and intuitive user interface that enhances the overall learning experience. 真人线上娱乐 backend services are powered by Firebase, offering real-time database capabilities and authentication for seamless user interactions. PostgreSQL serves as the primary relational database, ensuring robust data management and efficient querying for storing and retrieving linguistic and user-related data.
To enhance system performance and minimize latency, API call optimization techniques such as connection pooling and data caching were implemented, effectively reducing redundant database queries and improving response times. 真人线上娱乐 middleware, built using Python Flask, acts as an intermediary between the frontend and backend, handling data processing, request validation, and business logic execution. By integrating these technologies, the system effectively supports endangered language education, making learning more accessible and impactful.
Master of Science in Cybersecurity Engineering
SPRING 2025
Friday, May 30
CHRISTIAN BERGH
Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering
3:30 P.M.; Join Christian Bergh’s Online Defense
真人线上娱乐sis: Cybersecurity is Stressful: 真人线上娱乐 Impact of Stress on Identifying Phishing Attacks
Stress is an emotion that impacts everyone; it can have profound physical impacts on health and cognitive functions. 真人线上娱乐 responsibilities placed on average workers in many fields can increase workplace stress. As technology continues to be integrated into all facets of life and work, many industries have become de facto technology companies with large or valuable technical datasets. Many of these industries are facing an onslaught of cyber-attacks in an attempt to gain access to those large or valuable datasets. One of the most common cyber-attacks, often found to be the entryway for many data breaches, remains phishing attacks. This combination of factors places a large amount of responsibility for a business’ digital security on the shoulders of every employee. In contrast, security is often not the primary responsibility of this employee. Malicious actors know that humans are the most vulnerable portion of any security network and understand that employees who may not understand how their company’s digital network functions and do not understand the value of access credentials to a malicious actor can be the easiest to target. Phishing attacks are designed and created to elicit stress, emotions, and a sense of urgency from the target to trick that target into clicking a link, downloading a file, or entering their credentials somewhere they should not. This study uses the framework and core concepts developed in the Trier Social Stress Test (TSST) in a novel application to reveal the potential impacts of acute stress on common cybersecurity attacks. 真人线上娱乐 TSST creates an acute instance of increased stress on participants. Participants are then shown several screenshots of emails and asked to determine if the email shown is a phishing attack. By analyzing the participant’s stress levels before and after the phishing attack identification test, along with their performance on the test, we can determine if there Is a significant impact on a participant’s ability to identify phishing attacks under increased stress.
Monday, June 2
NIMISH SRIVASTAV
Chair: Dr. Brent Lagesse
Candidate: Master of Science in Cybersecurity Engineering
1:15 P.M.; Join Nimish Srivastav’s Online Defense
Project: Privacy-Preserving Malware Detection Framework
Malware attacks are becoming increasingly sophisticated, exploiting the centralized architectures of traditional detection systems to compromise both security and user privacy. To address these challenges, we propose a privacy-preserving malware detection framework that combines federated learning with additive secret sharing, enabling multiple clients to collaboratively train neural models—Dense, Deep, and Residual—implemented in TensorFlow and PyTorch via the Flower platform, without exposing raw data. Our lightweight secret-sharing protocol masks client updates to achieve a differential privacy guarantee of ? = 0.5, while allowing noise cancellation in aggregate and avoiding the computational overhead of homomorphic encryption. Evaluated on the BODMAS dataset, our approach achieves over 99.2%accuracy and F1-score within five communication rounds, and under adversarial Projected Gradient Descent (PGD) attacks retain up to 17% more accuracy than standard federated learning. In contrast, centralized baselines such as Logistic Regression, Random Forest, and Gradient Boosting suffer catastrophic performance drops under adversarial conditions, highlighting the necessity of privacy-aware decentralization. Privacy analyses confirm strong protection with moderate reconstruction risk, illustrating a practical balance between robustness and privacy for sensitive domains like healthcare, finance, and critical infrastructure.
Tuesday, June 3
JUI ANIKET BANGALI
Chair: Dr. Brent Lagesse
Candidate: Master of Science in Cybersecurity Engineering
11:00 A.M.; Join Jui Aniket Bangali’s Online Defense
真人线上娱乐sis: Evaluating the Impact of Responsible AI as a Security Control in Machine Learning
Responsible Artificial Intelligence (RAI) is an approach to developing and deploying AI systems in a manner that is ethical, trustworthy, and safe. While RAI is often framed in terms of fairness, transparency, and social responsibility, its potential role in improving the security and robustness of machine learning (ML) models remains underexplored. This research proposes that integrating RAI principles during the development lifecycle of ML models can serve not only as a foundation for ethical AI but also as a proactive security control. Using Microsoft’s Responsible AI framework, this study examines whether models built with responsible development practices are more resilient to adversarial attacks compared to those developed without them. 真人线上娱乐 findings confirm that RAI practices significantly improve model robustness, with the improved model reducing the average accuracy drop due to adversarial attacks by 46.23% compared to the baseline model. Notably, this result was achieved without applying any additional security-specific defenses, demonstrating that RAI alone can serve as an effective and independent layer of protection. This positions RAI not only as an ethical imperative but also as a practical, adaptable defense mechanism that can complement existing security techniques, offering valuable guidance for AI practitioners building trustworthy and secure systems.
Master of Science in Electrical & Computer Engineering
SPRING 2025
Thursday, June 5
AUSTEN CLAIR
Chair: Dr. Walter Charczenko
Candidate: Master of Science in Electrical Engineering
5:45 P.M.; Discovery Hall 464
真人线上娱乐sis: Signal Integrity of Coplanar Waveguides versus Microstrip Interconnects for Clock Distribution
This dissertation investigates the use of Grounded Coplanar Waveguide (GCPW) interconnects, commonly employed in monolithic millimeter-wave circuits, versus microstrip interconnects for distributing digital clock signals on printed circuit boards (PCBs) with the goal of improving electromagnetic compatibility (EMC) and signal integrity (SI). Two prototype PCBs, one using microstrip traces and the other using GCPW traces, were designed and fabricated using identical materials and layout constraints. Both designs were simulated and measured in the frequency and time domain to evaluate their performance in clock signal splitting and isolation from adjacent transmission lines. Results show that the GCPW-based PCB outperforms the microstrip design in the frequency range from 300kHz to at least 6GHz. 真人线上娱乐 GCPW circuit demonstrated a superior suppression of electromagnetic coupling and crosstalk to adjacent nets, reduced clock skew, lower reflection, and minimized signal attenuation. After propagating through the clock distribution network, clock signals at the output ports of the GCPW PCB displayed greater signal integrity and a higher isolation between output ports than the microstrip PCB throughout the measured spectrum. Crosstalk measurements taken on a net adjacent to the clock distribution circuit, while clearly quantifiable on the microstrip PCB, were nearly undetectable on the GCPW PCB. This study supports the use of GCPW structures over microstrip for high-performance digital clock distribution to improve EMC and SI.