Project:<\/strong> Multimodal EEG-EMG decoding for Neurorehabilitation Therapy-Specific Upper Limb Motor Execution Exercises<\/p>\n\n\n\nUpper limb motor impairments arising from conditions such as stroke, peripheral nerve damage, and compressive neuropathies like carpal tunnel syndrome often require structured, repetitive fine-motor rehabilitation. Despite growing interest in neural and muscular signal monitoring for neurorehabilitation, tools for tracking neural and muscular engagement and benchmarks for classifying clinically prescribed movements rather than generic gestures remain scarce. This project investigates whether EEG, EMG, or their multimodal fusion can be reliably decoded for a structured set of rehabilitation-relevant upper limb exercises spanning wrist and finger movements, and rest and focuses on building a processing pipeline and automating multiple common steps for current and future experiments.<\/p>\n\n\n\n
EEG and EMG signals were recorded from healthy subjects performing these exercises. A data collection tool called EEG Task Marker was developed to timestamp and cue subjects through exercises to enable more accurate EEG and EMG synchronization. In addition, a framework was devised to process collected signals in a modular, reusable pipeline covering automated data trimming, synchronization, preprocessing, and feature extraction designed to eliminate need for manual labelling and marking of tasks and support future experiments with different exercise sets. Preliminary results indicate that EMG can provide stronger discriminative signal across evaluation conditions, with muscular activation patterns stable enough to generalize for binary movement detection across our experimental sample. EEG captures cortical motor intent and contributes complementary neural information that muscular signals alone cannot provide alone. Multimodal EEG-EMG fusion improves robustness and classification accuracy. Cross subject, kinematically similar movements like finger actions remain the hardest to distinguish. These findings establish an initial benchmark for rehabilitation-specific motor decoding and demonstrate the potential of integrated bio-signal frameworks to support future adaptive, data-driven neurorehabilitation systems.<\/p>\n\n\n\n
Jayalakshmi Sasidharan<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Annuska Zolyomi
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> StoryPath: Designing a Human-Centered AI-Assisted Reminiscence Therapy Platform for Dementia Care<\/p>\n\n\n\nDementia affects 57 million individuals globally, with limited pharmaceutical treatments available. Reminiscence therapy, the intentional exploration of meaningful memories, is evidence-supported for improving mood, reducing agitation, and helping people maintain their sense of identity. However, traditional reminiscence therapy demands significant time and preparation. Caregivers must gather and organize photos, document the stories behind them, and guide meaningful conversations. Digital reminiscence therapy tools exist but often fall short in two ways: they lack grounding in established therapeutic principles, or they don’t adequately support the caregiver’s role in the process.<\/p>\n\n\n\n
StoryPath was developed through a human-centered design approach that centers caregiver agency and therapeutic expertise. The system supports two interconnected experiences: one for people with dementia and their caregivers to explore memory albums together, and one for caregivers to actively shape the therapeutic experience by managing patient information, organizing memories, and reviewing engagement patterns. The system uses artificial intelligence to generate personalized prompts that invite storytelling and reflection. These prompts are designed around validation therapy principles, meaning they focus on sensory details, avoid testing memory, and affirm emotional truth rather than factual accuracy. Importantly, caregivers maintain control throughout. They can review every AI-generated prompt and edit it to better fit their loved one’s personality and history. This ensures the therapy remains deeply personal while reducing the preparation burden that traditionally falls on caregivers.<\/p>\n\n\n\n
This project demonstrates how thoughtful design and artificial intelligence can make evidence-based therapy more accessible and less burdensome for families and caregivers. Findings will inform a larger research study and contribute to the growing field of human-AI collaboration in digital health interventions for older adults.<\/p>\n\n\n\n
Thursday, June 4<\/h4>\n\n\n\nMaria Ixchel Arias Cruz<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Geethapriya Thamilarasu
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Discovery Hall (DISC) 464
Project:<\/strong> A Two-Level Firewall for Preventing Direct Prompt Injection and PII Exfiltration in Hybrid Mobile-Cloud Clinical RAG Systems<\/p>\n\n\n\nLarge Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) are increasingly used in healthcare chatbots to provide evidence-based answers grounded in clinical documents, but this integration also exposes sensitive data to direct prompt-injection attacks that aim to exfiltrate data in hybrid mobile-cloud deployments. This capstone project addresses the lack of lightweight, deployment-ready defenses for such systems by designing and evaluating a two-level firewall for a hybrid mobile-cloud healthcare RAG chatbot. The architecture places a first-level input firewall on the mobile device to screen user queries and a second-level output firewall in the cloud to validate and sanitize model responses before they are returned to the phone, aligning security enforcement with mobile performance constraints. Using a synthetic healthcare knowledge base and a structured set of direct prompt-injection queries, the system is evaluated on PII and metadata leakage, a composite compound score that combines detection quality and latency, and standard classification metrics (accuracy, precision, recall, F1, and latency) relative to a baseline mobile RAG system without defenses. Results show that the proposed two-level firewall substantially reduces prompt-injection-driven leakage, with the on-device input firewall emerging as the main contributor and improving the compound score by up to 13% while preserving high accuracy and recall on benign clinical questions. At the same time, the on-device firewall introduces no significant performance overhead, maintaining interactive response times and modest CPU and memory usage on the mobile device.<\/p>\n\n\n\n
\n\n\n\nSonal Singh<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Annuska Zolyomi
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Project:<\/strong> SafeSpace AI: A Human-Centered Guardrailed Retrieval-Augmented Conversational System for Anxiety and Stress Support<\/p>\n\n\n\nSafeSpace AI is a human-centered, guardrailed conversational platform designed to provide accessible emotional support for individuals experiencing anxiety, stress, and emotional overwhelm. Existing mental wellness applications often rely on static self-help content, limited personalization, or costly professional support models that may not be readily accessible to students, young professionals, and underserved communities. Furthermore, many AI-powered conversational systems lack emotional safety mechanisms, contextual grounding, and responsible response generation, making them unsuitable for sensitive mental wellness interactions. Unlike conventional wellness applications, SafeSpace AI combines retrieval-augmented generation (RAG), prompt-orchestrated large language models, and multimodal calming interventions to create an empathetic, context-aware, and supportive user experience.<\/p>\n\n\n\n
SafeSpace AI employs a retrieval-augmented architecture that grounds conversational responses in curated cognitive behavioral therapy (CBT)-inspired wellness resources, reducing hallucinations and improving contextual relevance during emotionally sensitive interactions. The system integrates guardrailed prompt orchestration, response structuring, and ethical conversational constraints to encourage supportive guidance rather than harmful dependency or unsafe advice generation. In addition, the platform incorporates multimodal wellness tools such as guided breathing exercises, calming sensory interactions, and curated soothing media experiences to provide users with real-time emotional regulation support beyond text-based conversation.<\/p>\n\n\n\n
The application further emphasizes accessibility, emotional safety, and human-centered design through a calming interface tailored to reduce cognitive overload and anxiety during use. By combining retrieval-augmented reasoning, conversational AI, multimodal interventions, and ethical guardrails, SafeSpace AI transforms digital mental wellness support from a passive chatbot interaction into an adaptive, supportive, and accessible emotional assistance platform.<\/p>\n\n\n\n
Friday, June 5<\/h4>\n\n\n\nEthan Davis<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Erika Parsons
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Discovery Hall (DISC) 464
Thesis:<\/strong> Benefits of Being Bayesian: Motor Imagery Electroencephalogram Classification<\/p>\n\n\n\nBrain-computer interfaces (BCI) based on electroencephalogram (EEG) pattern recognition have demonstrated clinical value in motor rehabilitation and neuroprosthetics, yet accurate classification of EEG signals remains challenging due to poor signal-to-noise ratio and intrinsic nonstationarity. Bayesian machine learning offers a principled framework for uncertainty quantification that may address these limitations, though prior comparisons with frequentist methods have been limited to informal evaluations on single datasets. This thesis presents a large-scale meta-analysis of Bayesian versus frequentist machine learning for motor imagery EEG classification across 20 benchmark datasets. Six top-performing frequentist pipelines spanning spatial filtering, Riemannian geometry, and deep learning approaches were benchmarked against pairwise Bayesian variants. Pooled effects were estimated using a three-level meta-analytic model accounting for heterogeneity both within and between datasets. Results yielded statistically significant improvements in calibration metrics \u2014 expected calibration error and maximum calibration error \u2014 for Bayesian models, while discrimination metrics including AUROC and MCC showed no significant difference. Computational cost analysis further suggests that Bayesian methods are economically viable for future BCI research. These findings motivate a hypothesis that the primary advantage of Bayesian learning lies in long-term adaptability through sequential model updating and transferability across subjects, with probabilistic resolution and reliability proposed as key indicators for sustaining high-performing BCI systems.<\/p>\n\n\n\n
\n\n\n\nAhmed Bera Pay<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Munehiro Fukuda
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Commons Hall (UW2) 327
Thesis:<\/strong> A Benchmark of C++ Cluster-Computing Libraries: MASS C++, HPX, and PM2<\/p>\n\n\n\nThe selection of a distributed C++ runtime for high-performance cluster applications entails significant performance and programmability trade-offs, yet existing evaluations of higher-level runtimes remain siloed within each runtime\u2019s own application domain, precluding workload-diverse, controlled cross-runtime comparison. This thesis presents a benchmark suite of five computationally distinct patterns\u2014a 3D stencil kernel (Heat3D), dense matrix multiplication (DGEMM), graph motif search, a phased financial graph simulation (Bail-In\/Bail-Out), and an agent-based model (Reproductive SugarScape)\u2014implemented equivalently across MASS C++, HPX, and PM2 from runtime-neutral algorithmic specifications. Runtime behavior is characterized along two dimensions: performance, measured through strong and weak scaling experiments, and programmability, assessed through a unified frame-work of quantitative and qualitative metrics. This work constitutes the first algorithmically equivalent cross-runtime characterization of MASS C++, HPX, and PM2, yielding empirically grounded, workload-specific guidance for distributed C++ runtime selection.<\/p>\n\n\n\n
WINTER 2026<\/h3>\n\n\n\nWednesday, February 25<\/h4>\n\n\n\nDEBOSHRI DAS<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Min Chen
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Project:<\/strong> Blackfoot – Play and Learn: Designing a Game-Based Language Learning Application<\/p>\n\n\n\nBlackfoot (Niits\u00ed\u02bcpowahsin) is a culturally and historically significant Indigenous language of the Blackfoot Confederacy and a primary medium through which community knowledge, identity, and oral traditions are sustained. As with many Indigenous languages, Blackfoot is experiencing ongoing endangerment, highlighting the need for accessible learning tools and culturally grounded resources that support intergenerational transmission and revitalization.<\/p>\n\n\n\n
This paper presents the design and implementation of Blackfoot – Play and Learn, a web-based, game-driven language-learning application that integrates cultural context with structured, practice-oriented activities. The application is organized as a suite of modular learning experiences, including a Blackfoot History module and several interactive activities: Flashcards for incremental vocabulary acquisition, Blackfoot Builder for scaffolded construction of meaning through guided word selection, Voices of the Blackfoot for listening-centered engagement with narrative content and dialect identification, and a Quiz module for rapid recall and self-assessment. A persistent leaderboard provides lightweight gamification to encourage continued engagement and make progress visible across activities. Because Blackfoot is spoken across multiple dialects, the system incorporates dialect awareness throughout its modules rather than assuming a single standardized variety. The system is implemented as a React-based web application using client-side routing and reusable, modular UI components.<\/p>\n\n\n\n
The project follows Agile development practices with iterative feature delivery and periodic review feedback. Emphasis was placed on code quality, modular design, reusable utilities, and backend best practices such as normalized answer checking, debounced availability validation, provider-aware reauthentication, and guarded asynchronous data access. The resulting system demonstrates a scalable, maintainable architecture for culturally informed, game-based language learning, and provides a foundation for future adaptive and community-driven extensions.<\/p>\n\n\n\n
Friday, March 6<\/h4>\n\n\n\nBENJAMIN V. SCHIPUNOV<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Clark Olson
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
1:15 P.M.
Project:<\/strong> AROfficeNav: A Scalable Indoor Mapping and Augmented Reality Navigation System<\/p>\n\n\n\nIndoor navigation has emerged as a growing challenge in large, complex buildings such as university campuses, hospitals, shopping malls, and corporate offices, where traditional signage is not always sufficient to provide consistent, accessible guidance. Outdoor Global Navigation Satellite Systems (GNSS) are unreliable indoors due to signal attenuation and lack of line-of-sight to satellites, while existing positioning systems such as QR-code markers and dedicated infrastructure introduce installation and maintenance overhead that scale poorly over time. These limitations motivate the development of scalable indoor positioning systems that leverage advances in computer vision, sensor fusion, and modern mobile computing. This project presents the design, implementation, and evaluation of an augmented reality-based (AR) indoor building navigation system that integrates 3D spatial modeling to enable scalable, user-friendly indoor guidance.<\/p>\n\n\n\n
The system uses a server-backed architecture and a modular reconstruction pipeline to create building-scale 3D maps from phone-captured scans, organized into workspace-based reconstructions to support iterative refinement and long-term data management. For mapping, the pipeline uses learned features and robust pose estimation to produce a sparse 3D reconstruction. For navigation, the system performs real-time visual localization with rolling-window multi-frame matching against a pre-built 2D-3D descriptor database, estimating camera pose using Perspective-n-Point (PnP) with Random Sampling Consensus (RANSAC). A coordinate alignment transform maps reconstructed building coordinates into the user\u2019s live AR session coordinates, enabling stable AR labeling and route visualization. The overall design prioritizes minimal installation and maintenance compared to infrastructure-heavy alternatives such as fiducials and beacons, while supporting multi-floor buildings and scalable deployment.<\/p>\n\n\n\n
The project is evaluated in UW Bothell\u2019s Innovation Hall, which involves scanning hallways, open spaces, and staircases. After the scanning process, all rooms, exits, elevators, restrooms, and scan connections are labeled using a label projection system that operates directly on the captured scan images. The scans are then merged into a unified building model, from which a navigation graph is generated and prepared for publication and consumer use. The resulting platform demonstrates the feasibility of scalable, infrastructure-light indoor navigation while highlighting practical challenges in visual positioning under descriptor ambiguity and perceptual aliasing.<\/p>\n\n\n\n
Wednesday, March 11<\/h4>\n\n\n\nSAMMAN TYATA<\/h5>\n\n\n\n
Chair<\/strong>: Dr. David Socha
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Project:<\/strong> Design Decisions and Architectural Rationale in the Luna mHealth Mobile Application: UI Enhancements and Module Page Navigation<\/p>\n\n\n\nThis paper presents a critical analysis of design decisions and technical contributions made during the development of the Luna mHealth Mobile application, a Flutter-based mobile health education platform. The work encompasses four primary areas of the user interface: the module homepage, the module reading experience, peer-to-peer module sharing, and a breadcrumb-based navigation system with stack-based history. Each area is evaluated against foundational software engineering principles such as SOLID design, DRY, and Clean Code practices, illustrating how these principles shape architectural decisions in real-world Flutter development. Rather than cataloging all activities, this paper focuses on the reasoning behind significant architectural choices, the iterative thought processes that led to final implementations, and the broader lessons learned about mobile application development in a production environment.<\/p>\n\n\n\n
Friday, March 13<\/h4>\n\n\n\nPAUL OLADELE<\/h5>\n\n\n\n
Chair<\/strong>: Dr. David Socha
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
8:45 A.M.
Project:<\/strong> Offline Health Module Sharing in Luna mHealth: A Flutter-Based Peer-to-Peer Approach with Google Nearby Connections<\/p>\n\n\n\nThis capstone project presents the design and implementation of a peer-to-peer content sharing system for the Luna mHealth mobile application, an offline-first platform that delivers health education in low-connectivity environments. Luna distributes structured, audio-visual health modules on low-cost smartphones, where internet access cannot be assumed. To support local content dissemination, this work implements a device-to-device transfer system using Google Nearby Connections within a Flutter architecture. The project evaluates prior Bluetooth Low Energy approaches and motivates the selection of a higher-level framework to reduce protocol complexity and improve reliability. The system is modeled using finite state machines to provide predictable connection lifecycles, failure recovery, and multi-peer coordination. A module transfer pipeline with metadata exchange, file streaming, and integrity validation ensures safe ingestion and resharing. The result is a module sharing system that strengthens Luna\u2019s offline distribution model and supports community-driven propagation of health education content.<\/p>\n\n\n\n
AUTUMN 2025<\/h3>\n\n\n\nThursday, November 20<\/h4>\n\n\n\nJAMES TRUONG<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Kelvin Sung
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Project:<\/strong> Realistic Illumination in AR<\/p>\n\n\n\nAugmented Reality (AR) creates a unique user experience by superimposing digital content onto real-world environments. With the addition of applying realistic visual cues, AR objects can be further involved and properly integrated into a user\u2019s real-world environment to achieve higher immersion and believability. Unfortunately, accurately applying realistic illumination and spatial conditions remains a technically challenging task. Diverse environments and exposure to real-world phenomena often vary in complexity and visual acuity.<\/p>\n\n\n\n
Two primary approaches exist to address this challenge: machine learning and image-based methods. Machine learning based methods can simulate real-world illumination on AR objects by recognizing illumination patterns and features in images, but are reliant on the quality, quantity, and diversity of their training data. In contrast, image-based approaches offer advantages in flexibility and usability where a balance in image quality and hardware performance can still provide a satisfactory result. Unfortunately, image-based approaches remain sensitive to hardware limitations and image quality which can significantly affect the performance and outcome.<\/p>\n\n\n\n
This project focuses on the latter, emphasizing the development of a practical and accessible solution for realistic AR lighting on common consumer devices. The implemented system utilizes two mobile devices: one device gathers real-world environmental data, and the other establishes and renders the AR object. This configuration allows realistic illumination of AR objects in near real-time under dynamic indoor lighting conditions. The resulting system provides a practical and accessible solution for integrating digital objects into real indoor environments allowing them to appear consistent with real physical objects in terms of lighting, shadow, and reflection. However, the performance is limited in complex or rapidly changing environments where noise is more prominent.<\/p>\n\n\n\n
Friday, November 21<\/h4>\n\n\n\nSIQIAO YE<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Michael Stiber
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> Incident Classification of Emergency Services Communication System (ESCS)<\/p>\n\n\n\nIn the context of next-generation 911 (NG911), Emergency Service Communications Systems (ESCS) are becoming increasingly complex, requiring operators to answer calls and then categorize events based on the content of the conversation. This study explores metadata-based event classification methods and how their performance depends on the spatiotemporal structure of incoming calls, providing operators with an initial pre-classification before answering. We synthesized 911-like datasets using a graph-based clustering point process simulator, built a dual-input Long Short-Term Memory (LSTM) network classifier, and compared it with classic non-sequential baseline models. We generated three spatiotemporal separation modes: temporally overlapping but spatially separable; spatially overlapping but temporally separable; and simultaneously spatiotemporally overlapping. The results reveal clear patterns, especially when spatial cues are ambiguous, where temporal memory becomes crucial, and the LSTM model performs better. On a representative test set, the overall accuracy and macro F1 score typically exceed 85%, demonstrating robust performance and highlighting the necessity and option of sequence modeling in metadata-only ESCS classification.<\/p>\n\n\n\n
Tuesday, November 25<\/h4>\n\n\n\nPOULAMI DAS GHOSH<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Min Chen
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Project:<\/strong> Visual Question Answering System Using Gated Bidirectional Cross-Attention<\/p>\n\n\n\nThis capstone project presents a cost-effective and reliable Visual Question Answering (VQA) system designed to improve accessibility for visually impaired users. VQA is a multimodal AI task that requires joint reasoning over image and text inputs to answer open-ended questions. The delivered solution leverages a novel gated bidirectional Cross-Attention mechanism that integrates BERT-based text embeddings with spatial and global image representations from ResNet-50 to generate answers. In bidirectional Cross-Attention, the AI model dynamically fosters combined learning by allowing the text to attend to visual features and vice versa, enabling a richer multimodal understanding. Meanwhile, the gating algorithm enables the model to filter out irrelevant information by regulating the flow of data between attended and original features, thereby reducing the overall computational overhead of the VQA system. Using this approach, we achieved an accuracy of 58% on the VQA v2 validation set, a 10% improvement over the hierarchical co-attention-based baseline work. Furthermore, the system is deployed as a user-friendly and intuitive web application that supports both voice and text input\/output for enhanced usability.<\/p>\n\n\n\n
Wednesday, November 26<\/h4>\n\n\n\nDHURKA ROHINI MADASAMY<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Wooyoung Kim
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Thesis:<\/strong> Predictive Robustness and Biomarker Discovery in Rare Cancers: An Interpretable Machine Learning Approach<\/p>\n\n\n\nRare cancers such as Glioblastoma Multiforme (GBM, a brain rare cancer) pose persistent challenges in computational oncology due to severe data scarcity, biological noise, and the difficulty of identifying disease specific molecular signatures. This thesis develops an interpretable machine learning framework to evaluate the predictive robustness of GBM gene expression signals and to isolate biologically meaningful biomarkers under extreme class imbalance. Across four classification settings and multiple resampling strategies, the models achieve consistently strong performance (F1 and MCC values often above 0.95), demonstrating that the GBM transcriptomic signature remains highly separable even under challenging experimental conditions. To refine this signal, a novel Cascade Learning approach systematically removes common cancer pathways and reveals biomarkers specific to the rare cancer. SHAP based interpretability identifies a stable set of genes aligned with known glioma biology, and a complementary Tab2Image analysis offers visual validation of class separability through structured image based embeddings. Alongside these methodological results, the thesis also addresses ethical considerations inherent to rare disease research by emphasizing fairness, representation, and accountability when working with structurally imbalanced datasets. Overall, the study provides a robust, transparent, and ethically grounded pathway for biomarker discovery in rare cancers.<\/p>\n\n\n\n
\n\n\n\nARAVIND TALLAPRAGADA<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Erika Parsons
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> Fish Species Identification System for Enhanced Fishing Experience<\/p>\n\n\n\nThe goal of this project is to research and set the foundations to develop an ML-based fish species identification system to assist recreational anglers while supporting marine research and species preservation in Washington State. The system employs a ResNet-50 convolutional neural network fine-tuned on 2291 images of eight fish species commonly found in Washington\u2019s coastal waters. The project explores several experimental setups and configurations, leading to a 94.25% classification accuracy.<\/p>\n\n\n\n
The model is integrated into a full-stack web application that aims to enable users to upload fish images and receive prompt identification (under 5 seconds). The system also captures and stores environmental metadata including location, timestamp, and historical weather conditions via the OpenWeatherMap API. Species-specific information is retrieved through the iNaturalist API, while conservation status is provided via the IUCN Red List API.<\/p>\n\n\n\n
By providing a platform for accessible species identification along with taxonomic and environmental context, this system aims to deliver a practical tool recreational fishers and an infrastructure for citizen science contributions for ecological monitoring. This project demonstrates practical application of machine learning and cloud technologies to create a functional application that helps with ecological conservation research while serving as a tool for more conscientious recreational fishing.<\/p>\n\n\n\n
Monday, December 1<\/h4>\n\n\n\nNICOLAS POSEY<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Michael Stiber
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
8:45 A.M.
Project:<\/strong> Implementing a GPU Model for Simulating Emergency Services Communication Systems<\/p>\n\n\n\nGraphitti is a general-purpose, high-performance, graph-based simulator designed to significantly speed up the simulation of network event models, such as biological neural simulations, using NVIDIA GPUs. Graphitti also provides an architecture designed to allow for the validation of GPU simulation results against single-threaded CPU simulation results. This enables researchers and other users of Graphitti to be confident in the correctness of their parallel implementations. Previous work at the University of Washington, Bothell\u2019s Intelligent Networks Laboratory (INL) has succeeded in creating and validating a single-threaded CPU implementation of a Next Generation 911 (NG911) Emergency Services Communication System (ESCS) model. This Master\u2019s capstone project builds on this work, specifically, by creating a NG911 GPU implementation to support the simulation of larger networks. Two kernels, advance911VerticesDevice and maybeTakeCallFromEdge, were implemented using the CUDA programming language to parallelize the most computationally intensive components of the NG911 model. The runtimes of these kernels and of a simulation consisting of 1932 vertices, 34,119 calls, and 288,000 timesteps were measured to assess the performance of the implementation. Runtimes of the GPU implementation were slower than those of the CPU implementation, for advance911VerticesDevice (2.63x); for maybeTakeCallFromEdge (1.84x); and for the total simulation time (2.08x). Future work will be needed to achieve performance improvements for the GPU implementation by examining possible causes such as the complexity of the advance911VerticesDevice kernel and the inability to get the advance911VerticesDevice kernel sufficiently busy during the simulation. Because this is the first instance in the INL of creating a GPU implementation from an existing CPU implementation, the difficulty of the creation process using the existing architecture is analyzed to understand if architectural changes should be made to improve this process for future models. A normalized functional code change (NFCC) metric is computed to measure the difficulty. A NFCC value for all files changed during the GPU implementation was found to be 0.1359, demonstrating that the process for creating a GPU implementation from an existing CPU implementation is not difficult.<\/p>\n\n\n\n
\n\n\n\nJEREMY NEWTON<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Wooyoung Kim
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.; UW2 (Commons Hall) Room 327
Thesis:<\/strong> Interpretable Machine Learning for Biomarker Identification in RNA Seq Cancer Data<\/p>\n\n\n\nExisting research on RNA Seq gene expression biomarkers has provided various methods to select a small list of genes as cancer biomarkers from a large number of gene expression data. Previous methods for identifying potential gene expression cancer biomarkers have focused on statistical analysis, but other methods have incorporated machine learning, often including Interpretable Machine Learning (iML) techniques. On 16 cancer type cohorts of TCGA data, we used inherently interpretable machine learning models: Logistic Regression, Random Forest, and Linear Support Vector Machine to narrow down lists of potential genes as biomarkers using the trained model’s feature importance rankings. We subsequently applied the model-agnostic iML techniques Shapley Additive Explanations (SHAP) and Permutation Importance to narrow down the subsets even further. We compared classification performance between machine learning models trained on selected features from these methods with features selected by statistical methods, and biomarkers from external research. We cross-checked the highly scored potential biomarkers with biomedical annotations and gene pathway analysis. We found that these biomarker selection methods lead to comparable or better classification performance on these datasets than the biomarkers from outside research, or from statistical analysis alone. Mutual Information estimation was a surprisingly useful technique for initial feature selection, and iML techniques improved the selection of short lists of biomarkers for classification.<\/p>\n\n\n\n
Tuesday, December 2<\/h4>\n\n\n\nAKBARBEK RAKHMATULLAEV<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Munehiro Fukuda
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.; UW2 (Commons Hall) Room 327
Thesis:<\/strong> IVF Semantical: Agent-Based Implementation of Vector Search on GPU<\/p>\n\n\n\nVector search plays a crucial role in large-scale similarity search applications, with IVF (Inverted File Index) being a widely used indexing method due to its balance between accuracy and efficiency. However, traditional vector search algorithms that used IVF as an indexing method, such as IVF Flat and IVFPQ, yield results by brute force searching within each cluster\/list. This paper introduces a new IVF-based vector search algorithm, called IVF Semantical, which does the search within each cluster\/list through a different arrangement of data and traversal using the Binary Search. In order to accelerate the development phase, the author used MASS CUDA to handle the searching part, which allowed to leverage the abstraction level of the code. We evaluated our IVF Semantical, implemented for GPUs using MASS CUDA, against two other algorithms, IVF Flat and IVFPQ, demonstrating the significant speed efficiency of the approach. The findings suggest IVF Semantical can make vector search more efficient and robust on infrastructure that requires immediate response, such as navigation systems or robots.<\/p>\n\n\n\n
\n\n\n\nSARAH MARTEL<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Annuska Zolyomi
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> Sail the Ship, Steer the Self: Using Social Accountability and Gamification for Goal Attainment in Users with Attention Difficulties<\/p>\n\n\n\nPeople with attention difficulties, such as those with Attention-Deficit\/Hyperactivity Disorder (ADHD), tend to face challenges achieving their goals due to struggling with breaking goals into tasks, procrastination, and planning and remembering important deadlines.<\/p>\n\n\n\n
This project explores a novel approach for motivating people with attention difficulties to set and achieve goals using two motivating factors identified in literature: social accountability and gamification. Social accountability occurs in the form of a body doubling simulator, while a visual journey map of the user\u2019s goals and milestones helps make their progress more meaningful with gamification.<\/p>\n\n\n\n
In the first stage of a user-centered design process, a prototype of the app was designed using the design tool, Figma. Usability testing and interviews with four users who self-identified as having attention difficulties revealed key findings regarding engagement and ease of use. The findings revealed the importance of gamification and novelty in users with attention difficulties, the strength of peer accountability, and the importance of a clear, distraction-free UI. Building on these insights, an Android app, “”Sail the Ship””, was developed. This second stage, where six participants used the app, showed that peer accountability through body doubling increased task initiation and feelings of social pressure for most users. Additionally, the journey map supported engagement through milestone completion and gamification, with participants requesting even more gamified features. This work contributes a novel app design that helps people with attention difficulties venture forward and achieve their goals.<\/p>\n\n\n\n
Wednesday, December 3<\/h4>\n\n\n\nMARINA ROSENWALD<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Michael Stiber
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
8:45 A.M.
Thesis:<\/strong> Application of Graph Neural Networks in Spike-Time-Dependent-Plasticity Graphitti Simulations<\/p>\n\n\n\nSpike-timing-dependent plasticity (STDP) is a core mechanism that drives how biological neural networks’ connectivity changes over time, but understanding the structural changes for large networks is still difficult. Graphitti, the simulator used in the Intelligent Networks Laboratory, can generate large, densely-interconnected STDP networks, yet interpreting what emerges from those simulations is not straightforward. This thesis uses Graph Neural Networks (GNNs) to analyze these networks and to uncover patterns that are hard to see through traditional tools.<\/p>\n\n\n\n
The main goal of this work is to apply different GNN architectures, most notably Graph Attention Networks (GATs), to Graphitti-generated networks in order to identify key structural and behavioral changes. The focus of this thesis is to identify key neurons as STDP unfolds. By treating each simulation as a series of graphs over time, we can apply Graph Neural Networks to explore how connectivity shifts over time, which nodes receive higher attention weights in a Graph Neural Network, and whether these \u201ckey neurons\u201d lead to the expected graph structure. Alongside this analysis, an equally important objective is to improve our understanding and implementation of GNNs for large, fully connected neural graphs. We document the full workflow, challenges, and model choices, and contribute these findings for future work.<\/p>\n\n\n\n
Overall, this thesis links large scale STDP simulation with modern graph learning methods, using attention mechanisms to highlight influential nodes and offering new tools and insights for analyzing complex neural systems.<\/p>\n\n\n\n
\n\n\n\nCOLLEEN LEMAK<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Geethapriya Thamilarasu
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.; DISC (Discovery Hall) Room 464
Thesis:<\/strong> Hybrid Static-Dynamic Feature-Weighted Analysis for IoT Botnet Malware Detection<\/p>\n\n\n\nAs the Internet of Things (IoT) domain continues to evolve, IoT devices face escalating security challenges. Recent waves of IoT botnets have exploited device vulnerabilities to launch dangerous large-scale Distributed Denial of Service (DDoS) attacks from compromised, resource-constrained devices. These networks of infected devices pose a unique threat to modern infrastructure, homes, schools, medical facilities, and transportation systems at heightened risk of malicious exploitation.<\/p>\n\n\n\n
This paper proposes a novel hybrid framework that combines static and dynamic analysis techniques for IoT botnet malware detection without relying on complex Machine Learning (ML) models. By extracting and weighing the importance of key features from malware binaries based on their relevance to DDoS behavior, the framework maintains statistical adaptability to observed data while avoiding large memory usage and opaque black-box decision processes common in ML. Designed for interpretability and efficiency, this malware detection framework bridges code-level structure and runtime behavior, offering a transparent and practical botnet detection strategy for diverse resource-constrained IoT ecosystems.<\/p>\n\n\n\n
\n\n\n\nLOGAN CHOI<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Wooyoung Kim
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
1:15 P.M.
Thesis:<\/strong> Benchmarking TenSEAL\u2019s Homomorphic Encryption Through Predicting Encrypted RNA Sequencing Data<\/p>\n\n\n\nThis study addresses the growing need to protect sensitive healthcare data as digital technologies and cloud-based analytics become integral to modern medical research and care delivery. Healthcare data, such as clinical or genomic information, holds immense potential to enhance disease understanding and improve diagnostics through machine learning models; however, adopting third-party cloud technologies increases the risks of data breaches and noncompliance with regulations such as the Health Insurance Portability and Accountability Act (HIPAA). To address these concerns, this research investigates homomorphic encryption, a cryptographic method that allows computations on encrypted data without exposing sensitive information. The study benchmarks the TenSEAL library to evaluate its performance in encrypting healthcare test datasets and executing predictions through a pre-trained machine learning model, while also evaluating memory utilization and encryption time. The findings show that TenSEAL\u2019s CKKS encryption scheme effectively enables data encryption and secure machine learning inference on genomic datasets for breast, lung, and prostate cancers, achieving an average accuracy of 90% across all datasets. On the other hand, our results also highlight a key trade-off: as encryption strength and dataset size increase, computational overhead rises sharply. Thus, medical professionals and data scientists must carefully balance the need for security with the practical deployment in real-world healthcare systems.<\/p>\n\n\n\n
\n\n\n\nKAMERON VUONG<\/h5>\n\n\n\n
Chair<\/strong>: Dr. David Socha
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> Understanding Software Engineering Principles by Evolving Luna mHealth\u2019s Support for PowerPoint<\/p>\n\n\n\nLuna mHealth is a mobile health education platform designed to deliver culturally relevant, offline-accessible health education content to resource-constrained communities. Luna mHealth is composed of two parts: Luna authoring system and Luna mobile. Luna authoring system is a desktop application that converts PowerPoint-based health content into structured, interactive learning modules called Luna content modules. Luna mobile is a mobile application that readers use to view Luna content modules on their mobile phone without the need for an active Internet connection.<\/p>\n\n\n\n
In this project, I evolved Luna mHealth\u2019s support for the core PowerPoint features needed for Luna, such as connection shapes (aka lines), textbox shapes, picture shapes, hyperlinks, and placeholder shapes from slide layout templates. For Luna mobile, I evolved Luna mobile\u2019s rendering logic to render the extracted text and images, ensuring that visuals appear consistently and accurately. I also evolved Luna mobile\u2019s navigation system to support inter-page hyperlinks, allowing readers to navigate to different pages of a Luna content module. I also did enough work to demonstrate that we could extend this system to support LibreOffice Impress presentation files.<\/p>\n\n\n\n
Through practices like Test-Driven Development and principles like Single Responsibility and You Aren\u2019t Gonna Need It, I ensured the code I contributed to the Luna mHealth\u2019s codebase was clean, simple, and maintainable.<\/p>\n\n\n\n
\n\n\n\nWENTAO GAO<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Erika Parsons
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
5:45 P.M.
Project:<\/strong> Virtual Reality and sEMG Integration for Phantom Limb Pain Therapy<\/p>\n\n\n\nPhantom limb pain (PLP) affects a substantial proportion of amputees and remains challenging to treat despite decades of research. Virtual reality (VR) has emerged as a promising non-pharmacological intervention, offering immersive visual feedback and controlled movement experiences. However, existing VR systems face significant barriers to widespread adoption to treat PLP: high costs, limited adaptability to individual differences due to reliance on pre-trained models and fixed control schemes, and restriction to specific upper or lower limb applications. Systematic reviews highlight the need for more affordable, customizable, and versatile rehabilitation technologies.<\/p>\n\n\n\n
This project presents the design and preliminary evaluation of a novel, low-cost VR system capable of supporting both below-elbow and below-knee amputation scenarios through a self-calibrating machine learning pipeline. The system integrates consumer-grade VR hardware (Meta Quest 2), surface electromyography (sEMG) armband (8 channels total), and AprilTag visual markers to provide real-time, personalized limb control in immersive game environments. For limb control, the system classifies three discrete gestures (clench, extension, rest) combined with positional tracking and rotation state, which drive a rigged virtual limb model through animation blending to achieve naturalistic movement suitable for amputee rehabilitation. The system features an automated guided data collection and training workflow that allows users to record EMG signatures and train custom Random Forest gesture recognition models in approximately 5 minutes, without requiring technical expertise or pre-trained models with great accuracy.<\/p>\n\n\n\n
System feasibility is validated through a pilot study with able-bodied participants who complete both upper and lower limb training sessions and interact with purpose-designed VR games. Preliminary results demonstrate successful gesture classification (mean accuracy >85%), acceptable system latency (<50ms end-to-end), and positive user experience metrics. The system addresses a critical gap in accessible rehabilitation technology by providing a single platform adaptable to multiple amputation types while maintaining low implementation costs.<\/p>\n\n\n\n
Thursday, December 4<\/h4>\n\n\n\nYU WEN<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Annuska Zolyomi
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Project:<\/strong> ConverSwim: A Visual and Emotion-Aware Platform to Support Reflective Conversation for Neurodivergent Users<\/p>\n\n\n\nNeurodivergent individuals\u2014including those with autism and ADHD\u2014often face challenges in conversations, such as interpreting social cues, recognizing emotional signals, and maintaining mutual understanding. These difficulties can hinder effective communication and self-reflection. To address these issues, we developed ConverSwim, a web-based platform that supports users in reflecting on conversational dynamics through an integrated and interactive canvas. This canvas combines real-time chat, NLP-driven emotion detection, and a structured swimlane diagram that chronologically maps each user\u2019s internal emotional state and external verbal expression. Swimlanes within the canvas visualize the conversation across five columns: each participant has two parallel lanes\u2014Inside (representing emotions and internal states) and Outside (representing spoken or written expressions)\u2014while the central Common Ground lane captures moments of shared understanding as they emerge. By unifying interaction, emotional analysis, and visual structure in a single space, ConverSwim is designed to enable users to explore communication patterns and observe emotional shifts as conversations unfold. The platform includes a step-by-step tutorial to guide users through its features and reduce cognitive load. Through iterative, user-centered design, ConverSwim promotes emotional awareness, improves conversational reflection, and facilitates more empathetic, collaborative dialogue. Preliminary user sessions with neurodivergent participants evaluate usability, emotional awareness, and collaborative reflection. Early feedback indicates that participants found the system intuitive to use, helpful for noticing emotional cues, and supportive of reflecting on conversational flow.<\/p>\n\n\n\n
\n\n\n\nBREANNA POWELL<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Afra Mashhadi
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
1:15 P.M.; DISC (Discovery Hall) Room 464
Project:<\/strong> Accelerating Climate Forecasts: Vision Transformer Models for Arctic Sea Ice Concentration Prediction with Novel Tokenization Techniques<\/p>\n\n\n\nReliable forecasting of Arctic Sea Ice Concentration (SIC) is critical for safe shipping, sustainable settlement planning for indigenous communities, and effective wildlife conservation. Yet, accurate SIC prediction remains difficult: observational data are sparse, models often fail at extremes, and climate science depends heavily on computationally expensive physics-based simulations.<\/p>\n\n\n\n
This work introduces IceForecastTransformer, a Vision Transformer\u2013based architecture trained on synthetic data from the U.S. Department of Energy\u2019s Energy Exascale Earth System Model (E3SM) Version 3. By incorporating Freeboard\u2014a satellite-observable metric derived from ice and snow thickness\u2014alongside flexible tokenization and positional encoding strategies, the model provides a possible bridge between simulation outputs and real-world observations. IceForecastTransformer is trained on 163 years of monthly SIC and Freeboard data, achieving 0.16 RMSE and 0.32 JSD for 12-month SIC forecasts, and 0.84 F1 Score with 0.98 AUC for Sea Ice Extent (SIE), all with training times under 15 minutes.<\/p>\n\n\n\n
These results demonstrate that transformer-based models can efficiently scale down complex Earth system data while maintaining high accuracy, offering a practical complement to physics-based simulations. The approach provides a pathway toward operational Arctic forecasting, with potential for fine-tuning on satellite data and broader applications in climate research and planning.<\/p>\n\n\n\n
Friday, December 5<\/h4>\n\n\n\nJONATHAN CHO<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Wooyoung Kim
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Project:<\/strong> Interpretable Machine Learning Studio Platform Web Application<\/p>\n\n\n\nMachine learning models are often treated as black boxes, creating challenges in domains such as healthcare where interpretability is critical. This project enhanced a Machine Learning Studio Platform (MLSP) originally developed at the University of Washington by improving reliability, expanding supervised and unsupervised model support, and redesigning the interface for usability. Interpretability methods including SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plot (PDP), Permutation Feature Importance (PFI), and Exploratory Data Analysis (EDA) were integrated and tested on benchmark datasets such as the Breast Cancer Wisconsin dataset and others of varying size and complexity. The enhanced system produced consistent metrics and visual explanations, enabling users to evaluate models more effectively and approach results with greater trust and understanding. These contributions demonstrate how interpretability features can make machine learning workflows more transparent and accessible for students, researchers, and healthcare professionals.<\/p>\n\n\n\n
\n\n\n\nXIU ZHOU<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Geethapriya Thamilarasu
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
1:15 P.M.; DISC (Discovery Hall) Room 464
Project:<\/strong> Privacy Information Detection Using Imagined Speech EEG-to-Text Analysis<\/p>\n\n\n\nBrain\u2013Computer Interface (BCI) devices are rapidly entering consumer markets and offer several benefits, such as hands-free interaction and improved accessibility. However, their passive recording capability introduces a severe privacy threat, as malicious actors could intercept neural signals and apply machine learning techniques to infer sensitive information. Among neural modalities, imagined speech\u2014the internal process of thinking words or characters without vocalization\u2014is especially concerning, as it may reveal private content such as passwords or Personal Identification Numbers (PINs).<\/p>\n\n\n\n
To assess this risk, we investigate whether passwords can be decoded purely from imagined-speech EEG without prior knowledge of candidates. Moving beyond prior work limited to classifying among known passwords, we propose a novel hierarchical Binary-Digit-Letter (BDL) system for character-level reconstruction. Using a commercial headset, our method achieves an unprecedented end-to-end accuracy exceeding 60\\% on the full alphanumeric set. This performance markedly reduces the effective entropy of real-world passwords (e.g., from $62^{8}$ to roughly $2^{8}$ candidates for an 8-character password).<\/p>\n\n\n\n
We further present a systematic comparison of two deep neural architectures (CNN+BiLSTM and CNN+Transformer) across four experimental conditions to evaluate their password decoding performance and robustness. The results offer strong evidence that sensitive information can be extracted from passively collected EEG and establish key architectural baselines for future security analyses and privacy-preserving BCI design.<\/p>\n\n\n\n
\n\n\n\nNEELIMA CHOWDHURY<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Annuska Zolyomi
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> HealthMate: Designing a Senior Centered Digital Twin App for Health Monitoring and Predictive Insights<\/p>\n\n\n\nThis project introduces HealthMate, a senior centered mobile health app that uses the concept of a digital twin to present personal health information and future health predictions in a simple format. Current commercially available fitness apps often depict numbers in complex data dashboards designed for mainstream users, not older adults. HealthMate is designed from the beginning to be accessible for older adults and to provide meaningful data visualization and health predictions for health areas that are a priority for them. Due to the popularity of the Apple ecosystem with older adults, HealthMate was chosen to be developed as an iOS app that is compatible with the Apple Watch.<\/p>\n\n\n\n
The key features of HealthMate are to provide seniors with a Digital Twin interface to access their daily health data by tapping on body parts of the Digital Twin or avatar, provide predictive insights based on past week statistics, enable customized notifications for daily health alerts, and provide an intuitive and clear interface with minimal visual clutter and focused views. This is achieved by the app reading user data from wearable devices like Apple Watch, analyzing the past week\u2019s data and statistics, and displaying likely trends or potential issues in areas the user wants to focus on. Currently the data shown reflects a user\u2019s most consistent heart rate, step count consistency, and sleep habit consistency. Future enhancements aim to show anxiety or high stress propensity, fall detection, cardiovascular anomaly, dizziness propensity, and irregular heart rhythm. The predictive insights module analyzes last week\u2019s data and forecasts the current week\u2019s activity, heart rate, and sleep patterns, and may indicate possible incidents such as anxiety, falls, or heart palpitations linked to inadequate sleep. The prework for this module uses generated synthetic datasets that are similar to typical wearable device data. This supports model development and safe investigation of uncommon problems without exposing actual users. The objective is to create a trustworthy and understandable companion that assists senior citizens in identifying changes and helps inspire small improvements to daily routines for better and healthier living.<\/p>\n\n\n\n
SUMMER 2025<\/h3>\n\n\n\nWednesday, June 25<\/h4>\n\n\n\nARJUN TANEJA<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Michael Stiber
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> Spatiotemporal Analysis of Neuronal Avalanches in Self-Organized Criticality<\/p>\n\n\n\nNeural spike activity forms the fundamental basis of information processing and communication in the brain, exhibiting complex spatiotemporal dynamics. Recent advances in computational neuroscience have enabled high-resolution numerical simulations of large-scale neural networks, generating spike-time and location data that capture emergent phenomena such as neuronal avalanches and whole-network bursting. In the case of neuronal avalanches, prior analysis has almost exclusively focused on temporal information, ignoring the spatial information associated with spike data. This project presents an efficient algorithm that incorporates both spatial and temporal constraints for avalanche classification, moving beyond conventional spike-train analyses. Through systematic comparison of temporal-only and spatiotemporal methods, we investigate how the inclusion of spatial information affects avalanche identification and characterization. Finally, we examine how varying spatiotemporal constraints influence the detection and properties of whole-network bursting events, providing insight into the large-scale organization of neural activity.<\/p>\n\n\n\n
Thursday, August 7<\/h4>\n\n\n\nPANKTI BHASKAR SHAH<\/h5>\n\n\n\n
Chair<\/strong>: Professor Mark Kochanski
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> Design and Implementation of a Unified Appointment Scheduling System for Canvas LMS<\/p>\n\n\n\nThis project explores how instructor-student appointment scheduling can be integrated into a university\u2019s Learning Management System (LMS), specifically Canvas, while enabling synchronization with external calendars such as Google and Outlook. Although Canvas centralizes academic workflows, it lacks built-in support for course-aware, role-sensitive scheduling. As a result, instructors rely on disjoint third-party tools that lack integration with course data and often lead to scheduling conflicts and increased administrative burden. This project implements a unified scheduling system that uses OAuth authentication, Canvas API integration and supports external calendar providers for event synchronization. Users can create and manage availability programs, manage appointments, and seamlessly sync events across their preferred calendars. The system was developed using agile practices, offering practical experience in scalable software design, modular architecture, and API integration. This solution addresses a clear gap in academic infrastructure and offers a scalable approach for improved user-experience and efficient scheduling for students, instructors, and administrators.<\/p>\n\n\n\n
Friday, August 8<\/h4>\n\n\n\nAATMAN RAJESHKUMAR PRAJAPATI<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Munehiro Fukuda
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> An Enhancement of Distributed Graph Queries in an Agent-Based Graph Database<\/p>\n\n\n\nGraph databases are increasingly employed in domains that demand efficient data-modelling and querying of highly interconnected data. This project enhances an agent-based graph database system built on the MASS (Multi-Agent Spatial Simulation) Java library, which adopts the property graph model to support dynamic schemas and rich relationship semantics in distributed environments. Unlike traditional graph systems, this implementation leverages autonomous agents to navigate and manipulate in-memory data across multiple computing nodes, enabling scalable and parallel graph operations.<\/p>\n\n\n\n
This work focuses on extending the system’s query capabilities by integrating support for the Cypher WHERE clause, a critical feature for filtering and refining data retrieval. A modular approach was adopted to implement this functionality\u2014beginning with an abstract syntax tree (AST) for parsing, followed by a dynamic evaluation mechanism for efficient constraint resolution. The enhancements improve the expressiveness and performance of read operations, while preserving the system’s core agent-based execution model. This development not only broadens the practical utility of the system but also establishes a foundation for future support of more complex query patterns and mutation operations.<\/p>\n\n\n\n
Monday, August 11<\/h4>\n\n\n\nRAVITEJA TANIKELLA<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Dong Si
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
8:45 A.M.
Thesis:<\/strong> Emotionally Intelligent Voice Language Models For Mental Health Therapy<\/p>\n\n\n\nConversational assistants for mental health therapy primarily rely on text-based models or cascaded architectures that first transcribe speech to text, a process that discards crucial paralinguistic information. This information bottleneck limits the AI’s ability to perceive critical emotional cues and broader psychological states, hindering its capacity to reason over human voice and provide effective therapy. This thesis details the development of an emotionally intelligent voice language model designed to overcome these limitations. The process began with a systematic evaluation of heuristic-based approaches and end-to-end model architectures, where automated benchmarking and a double-blind human study confirmed that large audio language models provided the most effective foundation for voice understanding and reasoning. Building on these findings, we propose and implement policy optimization methods to fine-tune this base voice language model on therapy data. The resulting aligned model demonstrated improved performance in benchmark evaluations, exhibiting emotional intelligence and generating therapeutically relevant responses. By presenting a complete development framework, from architectural validation to targeted alignment, this research establishes a clear and proven roadmap for creating the next generation of efficient and adaptive voice language models for mental health therapy and multimodal conversational AI.<\/p>\n\n\n\n
\n\n\n\nKAUSHIK REDDY MITTA<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Marc Dupuis
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Project:<\/strong> Demographic Disparities in Cybersecurity: Analyzing Victimization, Tool Adoption, and Awareness Across Ethnicity and Region<\/p>\n\n\n\nCybersecurity disparities across different demographic groups and regions have become a growing concern, as certain populations face disproportionate risks in terms of cyber victimization, tool adoption, and security awareness. These disparities are often overlooked in conventional cybersecurity research, which tends to simplify the relationship between demographic factors and cyber preparedness. This study analyzes these disparities by examining how ethnicity and geographic region influence cybersecurity outcomes in the United States. Using survey data from 470 participants collected by Professor Marc Dupuis, the research employs comprehensive statistical methods including correlation analysis, ANOVA, Kruskal-Wallis tests, and logistic regression to explore patterns across ethnic groups and urban, suburban, and rural settings. The study focuses on four key areas: cyber victimization, cybersecurity tool adoption, cyber incident experiences, and levels of cybersecurity awareness. The findings reveal significant disparities that challenge conventional assumptions about cybersecurity preparedness and risk exposure, highlighting the complex interactions between technology adoption, socioeconomic factors, and the strategies of threat actors targeting specific groups. The study calls for culturally competent interventions to address the unique vulnerability profiles of different demographic groups.<\/p>\n\n\n\n
\n\n\n\nMANPREET KAUR<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Dong Si
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
3:30 P.M.
Project:<\/strong> A Multimodal AI System for Mental Health Support Using Emotion Recognition<\/p>\n\n\n\nEmotion\u2011aware conversational agents have the potential to transform mental health support by providing accessible, empathetic and context\u2011aware interactions. This project enhances the iCare Carebot by expanding it beyond a text\u2011only interface to a multimodal system that integrates text, audio and visual cues to better infer and respond to user emotions. We develop dedicated pipelines for each modality and benchmark both unimodal and multimodal emotion\u2011recognition models, fusing their outputs through cutting\u2011edge foundation models. For visual emotion recognition, we evaluate several vision\u2011language models including Phi\u20114, Qwen2.5\u2011VL\u20117B and Qwen2.5\u2011Omni\u20117B and find that Qwen2.5\u2011VL\u20117B offers the best balance of accuracy and inference efficiency. Across multimodal tasks, instruction\u2011tuned models consistently outperform zero\u2011shot and supervised\u2011fine\u2011tuned baselines. The system\u2019s performance is assessed via the Multimodal Language Analysis benchmark and complemented by a user survey, which indicates broad receptiveness to a multimodal chatbot provided privacy concerns are addressed. Comparative evaluation using the Multimodal Language Analysis benchmark reveals that, among these models, Phi-4-multimodal-instruct provides the most balanced and effective performance across tasks. This work delivers a complete end-to-end framework from emotion signal extraction to context-aware response generation and marks a significant step toward making iCare a more empathetic and emotionally perceptive mental health support tool.<\/p>\n\n\n\n
Tuesday, August 12<\/h4>\n\n\n\nSWANAND WAGH<\/h5>\n\n\n\n
Chair<\/strong>: Dr. Min Chen
Candidate<\/strong>: Master of Science in Computer Science & Software Engineering
11:00 A.M.
Project:<\/strong> Designing a User-Centered Digital Experience for the Entomon Institute<\/p>\n\n\n\nIn the digital era, a strong online presence is essential for educational institutions to effectively engage with their audiences. However, typical institute websites for Zoology often suffer from poor accessibility, suboptimal UI\/UX design, excess resources cluttered altogether, and inadequate adaptability, hindering their effectiveness. This project asks: how can I design a digital platform that meaningfully enhances public interaction with zoology & entomology?<\/p>\n\n\n\n
To answer this, I partnered with the Entomon Institute to develop a cutting-edge, user-centric web platform to address these issues. The envisioned website serves as a dynamic resource hub, offering personalized materials on zoology and entomology, facilitating collaboration among institutes and research labs, and managing events such as insect hunts and ant colony explorations. Key features include interactive blogs, streamlined event management & proper user & admin dashboards. By leveraging modern web technologies, the platform aims to enhance educational outreach for zoology & entomology, fostering an engaging community, and provide up-to-date information in an accessible and interactive format.<\/p>\n\n\n\n
At its core, this project is about making an institutional website feel less like a brochure and more like a space people actually want to visit. The result is a fast, accessible, and visually engaging platform that finally brings the Entomon Institute into the modern digital era.<\/p>\n\n\n\n
\n\n\n\nHARI PRIYA DHANASEKARAN<\/h5>\n\n\n\n