足彩app哪个是正规的sis/Project Final Defense Schedule

Join us as the School of STEM master’s degree candidates present their culminating thesis and project work. 足彩app哪个是正规的 schedule is updated throughout the quarter, check back for new defenses.

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Master of Science in Computer Science & Software Engineering

SPRING 2026

Monday, May 18

Khushaal Kamal Kurswani

Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Khushaal Kamal Kurswani’s online defense
Project: Building an Extensible Explainable AI Module for Mental Health Conversational AI

Due to the rise of mental health issues and shortage of mental health professionals, people turn towards AI powered chatbots and virtual assistants for support and mental health related advice. One such chatbot application is the Data Analysis & Intelligent Systems (DAIS) laboratory’s iCare web application. 足彩app哪个是正规的 Large Language Models used in such chatbots are black boxes and it is difficult to trust and verify their advice. 足彩app哪个是正规的 solution to this lack of transparency is Explainable AI which are tools and algorithms that can provide insight into a machine learning model’s inner workings and explain their decision-making process in a human understandable manner.

This project integrates several Explainable AI algorithms such as Feature Ablations, Layer Integrated Gradients, and Shapley Value Sampling into the iCare web application to explain the LLM’s text generation process. Furthermore, an extensible framework was added to iCare to allow for easy integration of Explainable AI in the future. 足彩app哪个是正规的se algorithms were evaluated based on time and accuracy. User surveys were also conducted to gather feedback on user experience of the explanation feature. Based on the evaluation results, all three algorithms achieved similar levels of accuracy and had excessive processing times. Layer Integrated Gradients performed the best with the highest accuracy and shortest processing time. Additionally, user feedback highlighted a significant preference for natural language explanations over raw token attributions, indicating a need for more intuitive communication of model reasoning.

Tuesday, May 19

Bo Fu

Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Bo Fu’s online defense
足彩app哪个是正规的sis: Enhancing Parallelization of Agent-based Graph Computing

足彩app哪个是正规的 demand for distributed data processing grows as modern applications involve increasingly large and complex datasets. Traditional distributed computing frameworks, such as Apache Spark and Hadoop MapReduce, are effective for large-scale data processing but are not always well suited for graph computation. 足彩app哪个是正规的 MASS (Multi-Agent Spatial Simulation) Java library instead provides an agent-based approach to distributed graph computation and has been proven effective for graph computing applications and graph database. However, the performance of MASS Java remains limited in some cases because graph applications often require many agent operations, which introduces significant overhead.

To address these limitations, this thesis introduces several enhancements for improving agent execution performance in MASS Java and evaluates them using graph computing applications and graph database queries. 足彩app哪个是正规的 evaluation shows that the enhancements can improve MASS Java performance in both graph computing and graph database query execution. In addition, this thesis identifies a major overhead in the current MASS graph database and proposes a solution to reduce it. Overall, this thesis contributes to the optimization and evaluation of MASS Java for graph applications and provides useful guidance for future development.

Wednesday, May 20

Josiah Zacharias

Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Discovery Hall (DISC) 464
Project: Opto-mistic 足彩app哪个是正规的rapy: Modernizing Stereoscopic Vision 足彩app哪个是正规的rapy through Cutting-Edge Games

Pediatric vision impairments frequently go undiagnosed in underserved communities, impacting learning and cognitive development. 足彩app哪个是正规的 EYE Toolbox, developed by the Near Vision Institute (NVI) in partnership with the EYE Research Group at UW Bothell, is a web-based platform supporting NVI’s school-based optometry services across 50+ Washington districts. This project modernized the platform in three engineering phases. Phase 0 hardened 573 PHP files: raw mysqli_query references dropped from 3,905 to 97, jQuery was upgraded from 1.7.1 to 3.7.1, credentials moved to environment variables, and 41 of 45 live black-box attack probes were rejected against the dev deployment. Phase 1 introduced a REST API and React-based frontend to coexist with the legacy PHP/jQuery pages; paired-endpoint benchmarks showed response payloads 30–98% smaller across five surfaces and cumulative session bandwidth 65.1% lower than the legacy path. Phase 2 improved the production RDS vergence therapy application and added three new gamified prototypes (Base Builder, Balloon Pop, Animal Cart) on the shared Phase-1 infrastructure, each preserving the fusion-required vergence demand mechanism. Within-clinic before/after analysis of NVI’s session telemetry (14,653 sessions, 321 patients) found median peak vergence per session rose from 12.0 to 16.0 prism diopters under the new RDS application (+33% relative), with post-cutover patients leading at 19 of 20 session positions when controlling for therapy-course position; per-session personal-best rate rose from 7.5% to 10.4%. NVI standardized on the new application from cutover forward. A 5-week public demo-portal pilot (199 users, 207 sessions, 16 multiplayer challenges all finishing cleanly) shows voluntary engagement absent clinical referral pressure. A controlled clinical efficacy study integrating all four evaluation pillars is documented for follow-on work.

Thursday, May 21

Dazhi Li

Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Join Dazhi Li’s online defense
足彩app哪个是正规的sis: Towards Smarter Trading: An AI Trading Framework Combining Reinforcement Learning and Large Language Model

足彩app哪个是正规的 rapid evolution of financial markets demands intelligent trading systems capable of synthesizing heterogeneous information and making adaptive decisions under uncertainty. In this paper, we propose a trading framework that leverages reinforcement learning (RL) to fine-tune a Large Language Model (LLM) for autonomous trade decision-making. Unlike prior approaches that depend on supervised pre-training with expert-annotated analyses or domain-specific corpora for cold-start guidance, our method applies Group Relative Policy Optimization (GRPO) directly to a general-purpose instruction-tuned LLM, enabling the model to develop trading competence purely through reward-driven exploration without curated professional signals.

足彩app哪个是正规的modelingests multi-source market observations — encompassing technical indicators, financial news, and corporate financial statements — within a rolling temporal window, and outputs structured trading strategies specifying action type, share quantity, take-profit price, and stop-loss price. This formulation enforces strategy completeness through explicit exit conditions while supporting flexible position sizing, bridging the gap between simplified academic models and practical trade execution.

To guide learning, we design a multi-dimensional reward function grounded in profitability and trading discipline. Each strategy is evaluated on path-dependent profit-and-loss, risk exposure relative to stop-loss levels, position sizing appropriateness, and regulatory adherence, providing fine-grained feedback that cultivates the model’s awareness of both return potential and downside risk.

We conduct comprehensive experiments along three dimensions: (1) model comparisons —contrasting the RL-trained LLM against its base model, alternative LLM architectures, and a DQN-based traditional RL trading agent to quantify improvements from RL fine tuning and LLM-based reasoning respectively; (2) training budget analyses — investigating how training steps influence the model; and (3) strategy ablations — examining the contributions of quantity-based position sizing. Results demonstrate that the proposed framework produces coherent, risk-aware trading strategies without supervised warm-up; the ablation analyses further yield insights into the respective roles of model capacity, training sufficiency, and strategy design.

Aqsa Inamdar

Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Aqsa Inamdar’s online defense
Project: FinWise: Personalized Financial Empowerment

FinWise is a personal finance web application designed to help users better understand their spending behavior, manage transactions, track financial goals, and receive actionable financial guidance. 足彩app哪个是正规的 system combines a React and TypeScript frontend with a Node.js, Express, and Firebase backend to support transaction management, PDF-assisted transaction import, visual analytics, goal planning, and AI-assisted financial reasoning.

A key focus of the project is explainability. Rather than presenting users with opaque predictions, FinWise combines deterministic financial calculations, machine learning models, and large language model narration to produce responses that are traceable and easy to understand. 足彩app哪个是正规的 goal projection module uses LightGBM-based regression and classification models to forecast monthly savings, estimate goal completion timelines, and evaluate whether users are likely to meet their deadlines. 足彩app哪个是正规的 assistant supports descriptive, predictive, and prescriptive finance questions, helping users interpret trends, compare categories, forecast savings, and explore spending-reduction scenarios.

足彩app哪个是正规的 project emphasizes accessibility, usability, and financial literacy by presenting complex financial insights in plain language while preserving the underlying calculations. FinWise demonstrates how machine learning and AI-assisted interfaces can be integrated into a practical personal finance tool that supports informed decision-making and goal-oriented financial planning.

Friday, May 22

Yumeng Pang

Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Join Yumeng Pang’s online defense
足彩app哪个是正规的sis: Design and Benchmarking of a Citation Graph DB Across Neo4j, ArangoDB, and MASS Graph DB Systems

Academic collaboration, citation influence, and institutional research visibility are increasingly reflected through scholarly relationship networks. However, existing academic platforms remain largely profile-centered and do not provide an institution-focused, interactive, and queryable graph system for multi-hop exploration across authors, works, affiliations, and citations. This thesis investigates the design and benchmarking of a UWB citation graph, for practicality, seeded from CSS faculty scholarly activities, and examines how effectively different graph database systems support this richer graph model for practical scholarly exploration.

To address this problem, this work designs and implements a scholarly citation and co-authorship graph pipeline that constructs a heterogeneous Author–Work–citation–Affiliation graph using institutional seed data and OpenAlex-derived metadata. 足彩app哪个是正规的 resulting graph is intended to support practical use cases such as collaborator discovery and referee exploration for UWB CSS faculty. 足彩app哪个是正规的 system is evaluated across three graph databases—Neo4j, ArangoDB, and MASS Graph DB—and is benchmarked using LDBC-aligned workloads and metrics, including bulk ingestion throughput, query throughput, and multi-hop traversal latency. In addition to the institutional citation graph, the evaluation framework includes public benchmark datasets of different graph types, densities, and scales to enable broader cross-platform comparison.

足彩app哪个是正规的 results show that the heterogeneous scholarly citation graph provides substantially richer analytical capability than simpler single-relation citation graphs by enabling cross-relational exploration over authorship, affiliation, and citation structure. 足彩app哪个是正规的 benchmarking results further indicate that platform strengths are workload-and-graph-dependent: Neo4j performs strongly for interactive read-heavy exploration on the institutional citation graph, ArangoDB remains competitive in selected ingestion-oriented scenarios, and MASS Graph DB performs especially well on loading structurally simpler single-relation graph workloads.

Overall, the findings suggest that richer institutional citation graph modeling can remain practical for interactive exploration, while platform suitability depends on how well each system handles heterogeneous topology, traversal complexity, and deployment conditions.

Kris Yu

Chair: Dr. Annuska Zolyomi
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Kris Yu’s online defense
Project: Mystoria: AI-Assisted Authoring of Personalized Social Stories for Autistic Children

Social Stories? are a widely used intervention that helps autistic children understand and prepare for social situations, but creating personalized stories that follow established Social Stories? criteria can be time-consuming for caregivers. Existing digital tools often provide either fixed story libraries or free-form editors with limited support for methodological fidelity. This project presents Mystoria, an iPad and iPhone application that supports caregivers in authoring personalized Social Stories? with large language model assistance while preserving caregiver control. Mystoria combines multimodal story creation, including text, AI-generated and camera-sourced images, AAC pictograms, and caregiver-recorded audio, with an AI Draft workflow designed around the structural criteria for Social Stories?. 足彩app哪个是正规的 application includes an in-app draft-quality scorer that helps caregivers review saved drafts against those criteria, as well as a hybrid AI design that combines cloud-based generation with an optional on-device fine-tuned Gemma model for more privacy-aware authoring. Mystoria will be evaluated through a caregiver-only within-subject study in which each participant creates two stories on the same anchor topic: one manually and one with AI Draft. 足彩app哪个是正规的 study examines caregiver preference, trust in AI-generated content, perceived usability and usefulness, analysis of edits and sentence types, and feedback on appropriateness, privacy, and acceptable boundaries for AI assistance. 足彩app哪个是正规的 goal is to understand whether AI-assisted authoring can reduce caregiver burden while preserving personalization, caregiver agency, and adherence to established Social Stories? criteria.

Tuesday, May 26

Siddharth Thammineni

Chair: Dr. Geethapriya Thamilarasu
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Discovery Hall (DISC) 464
Project: Hybrid XAI-based Intrusion Detection for IoT Networks

足彩app哪个是正规的 rapid expansion of the Internet of Things (IoT) has created the need for anomaly-based intrusion detection systems (IDS) to use Machine Learning. Deep learning models are effective at identifying adaptive security threats, but their opaque nature limits interpretability. Explainable Artificial Intelligence (XAI) addresses this by providing techniques for producing an explanation for a model’s predictions. This project addresses how a hybrid XAI approach can provide accurate, valid explanations for IoT ML IDS models while maintaining real-time performance. 足彩app哪个是正规的 proposed solutions aggregate local explanations for global insight, using efficient feature attribution calculation provided by the FESP method, to produce an explanation using the MAPLE explanation framework that provides local diagnostics with awareness of global patterns. Experimental evaluation demonstrates that hybrid explanation approaches can provide accurate and defensible global and local interpretability while maintaining performance within practical real-time constraints

Thursday, May 28

Rishabh Pratap Singh

Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Discovery Hall (DISC) 464
足彩app哪个是正规的sis: Feature Extension of MASS C++ towards a General Purpose Library

MASS (Multi-Agent Spatial Simulation) C++ is a parallel computing library for agent-based simulations on distributed memory clusters, organised around the Bulk Synchronous Parallel model and a master-worker coordination scheme. Three structural limitations have constrained its applicability as a general-purpose runtime. An integer-based method dispatch scheme couples user-defined Place and Agent classes to the framework and propagates renumbering errors silently. A per-iteration master coordination cost of K × N barrier round-trips dominates wall-clock time on communication-intensive workloads. And a Places abstraction restricted to regular grids leaves social, biological, and transportation graphs without first-class support.

This thesis presents three feature extensions that address these limitations while preserving backward compatibility with existing MASS C++ programs. A three-tier dispatch architecture replaces integer switch/case with string-named methods, header-defined lambdas, and JIT-compiled lambdas, unified through a single registry. An IterationConfig phase pipeline backed by an AsyncHandle executor collapses the K × N master round-trip pattern into a single dispatch by allowing workers to advance through compute, communication, and agent-management phases autonomously. A graph stack consisting of GraphTopology, GraphPlaces, and GraphAgents extends MASS C++ from grid-only topologies to arbitrary graphs, with bidirectional adjacency, locality-aware partitioning, edge-constrained agent migration, Pregel-inspired combiners and aggregators, and a pluggable parser interface for user-defined graph formats.

足彩app哪个是正规的 extensions are evaluated on five benchmarks (Wave2D, SugarScape, PageRank, BFS Wavefront, and Random Walk) covering correctness, performance, and programmability. Compound execution reduces barrier round-trips by orders of magnitude and yields speedups that grow with the number of workers on Places-only workloads. SugarScape scaling exposes an O(P?) bottleneck in the existing all-to-all agent exchange protocol, which caps compound speedup as P grows and motivates the sparse neighbour-rank exchange that GraphAgents adopts. 足彩app哪个是正规的 graph stack also positions MASS C++ as the only system among the surveyed prior art that supports mobile agents over irregular topologies while remaining a drop-in extension of the existing grid-based runtime. 足彩app哪个是正规的 new dispatch tiers reduce per-method boilerplate by approximately 46% at overheads of 0-6% for header lambdas and 5-15% for JIT lambdas.

Friday, May 29

Deepak Sujay Gudiseva

Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
5:45 PM.; Discovery Hall (DISC) 464
Project: Agent-Based Distributed Node2vec

Graph representation learning algorithms like Node2Vec generate highly accurate topological embeddings but impose severe memory and computational bottlenecks on centralized architectures. This paper presents a scalable, distributed Node2Vec engine engineered natively within the Multi-Agent Spatial Simulation (MASS) framework. By mapping second-order, biased random walks to autonomous mobile software agents, our architecture efficiently samples complex networks while bypassing single-machine memory limitations. We introduce a novel Compute-Node-Centric training paradigm that pairs isolated Skip-Gram neural network optimization with a decentralized, logarithmic tree-reduction synchronization protocol. Empirical evaluations across benchmark graphs (Cora and OGBL-DDI) demonstrate that this distributed engine achieves strict predictive parity with industry-standard PyTorch baselines across key ranking metrics like MAP, Recall.. etc. Ultimately, this architecture successfully outperforms linear methods like FastRP in topological accuracy while comprehensively unlocking the spatial scalability required for parallelized, deep graph learning.

Monday, June 1

Meghana Dayathri

Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Join Meghana Dayathri’s online defense
Project: Model-Grounded Explanations for Responses Generated from Conversational AI

Mental health conversational AI systems are becoming more common as tools for emotional support, reflection, and early guidance. For researchers and system designers, transparency helps evaluate whether a system’s responses are grounded in the conversation rather than simply sounding supportive. For users, this transparency matters because they may want to understand whether the system recognized their concern and responded to the right part of what they shared. A common approach is to ask a language model to explain its own response in free-form text, but these explanations can sound reasonable without being closely tied to the model behavior behind the response. This project develops a user-facing explanation feature for CareBot, a conversational AI system within the iCare project at the Data Analysis and Intelligent Systems (DAIS) Laboratory. Instead of generating a free-form justification, the feature traces a selected CareBot response back to earlier user messages that most strongly supported it. 足彩app哪个是正规的 approach uses Layer Integrated Gradients to compute token-level attribution scores for a selected response, maps those scores back to the original conversation, and converts the strongest user-side evidence into short, readable phrases. 足彩app哪个是正规的 explanation feature was evaluated through phrase-level deletion and human evaluation to examine both model-level faithfulness and user perceptions. Results showed that top-ranked phrases had a stronger effect on the model’s response score than low-ranked phrases, while human evaluation helped assess the explanation feature in terms of clarity, usefulness, and transparency.

Tuesday, June 2

Enbai Kuang

Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Enbai Kuang’s online defense
足彩app哪个是正规的sis: Application of Deep Learning for the Detection of Intracranial Hemorrhage Through Different Planes Using Ultrasound Imaging

Traumatic Brain Injury (TBI) is a significant medical condition that can result in long-term neurological impairment or life-threatening intracranial hemorrhage. While computed tomography (CT) and magnetic resonance imaging (MRI) are effective diagnostic tools, their high cost and lack of portability limit accessibility in certain scenarios such as rural and combat environments where rapid triage is essential. Tissue Pulsatility Imaging (TPI), an ultrasound-based technique developed at the University of Washington through Department of Defense funding, offers a potential alternative by enabling the collection of ultrasound data with a portable device. This method measures tissue displacement within the brain resulting from pulsatile blood flow. Previous work at the University of Washington utilized TPI displacement data and CT-derived masks as ground truth to train machine learning models for cranial feature and hemorrhage detection; however, blood segmentation remained challenging due to excessive noise and a limited sample size.

This thesis extends previous research by assessing whether blood-focused segmentation models trained on distinct participant subsets yield different detection outcomes. Eight blood-mode U-Net models were developed using participant groups categorized by scan orientation and blood-region location, with axial and coronal views further divided into top, bottom, left, and right regions. Each model was evaluated on its own held-out test set as well as on the test sets from the other seven groups, enabling comprehensive cross-test comparisons. Participant-level positivity was determined by thresholding predicted blood-mask pixels and applying a majority-vote analysis across models. Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) baselines were also evaluated for comparison with previous results using the entire dataset.

足彩app哪个是正规的 U-Net models demonstrated limited performance in blood localization, with an average cross-test Dice coefficient of 0.101. Models trained on axial views achieved the highest segmentation accuracy and were the only ones to produce participant-level positive predictions. However, when applying a stricter majority-vote criterion that required at least half of the models to classify a result as positive, no test set met this threshold, as each received only three out of eight positive votes. 足彩app哪个是正规的 SVM and KNN baselines yielded substantially higher positive classification rates, but their results were heavily influenced by class imbalance and lacked spatial blood localization capability. Collectively, these findings indicate that TPI displacement data contain signals relevant to hemorrhage detection, yet reliable intracranial hemorrhage localization remains difficult due to limitations in sample size, diversity, and model architecture.

Master of Science in Cybersecurity Engineering

SPRING 2026

Tuesday, May 26

Suryanarayana Putrrevu

Chair: Dr. Geethapriya Thamilarasu
Candidate: Master of Science in Cybersecurity Engineering
11:00 A.M.; Commons Hall (UW2) 327
Project: LLM-Based IoT Malware Detection via Similarity-Grounded Context

足彩app哪个是正规的 rapid growth of Internet-of-Things (IoT) devices has expanded the attack surface for network-based malware, creating demand for detection systems that are both accurate and interpretable. Machine learning classifiers achieve strong detection performance but provide limited insight into their decisions. Large Language Models (LLMs) offer structured reasoning capabilities, yet applying them directly to numeric network telemetry consistently fails — and the reasons for this failure have not been systematically studied.

This work introduces a reproducible, geometry-aware benchmark framework that investigates why LLMs succeed or fail at IoT malware detection, and presents the first LLM evaluation on the CIC IoT-DIAD 2024 dataset across both packet-level and flow-level traffic domains. Rather than treating Retrieval-Augmented Generation (RAG) as a performance tool, the framework identifies the class composition of the FAISS (Facebook AI Similarity Search) index — the pool of historical examples the LLM consults as the primary factor governing classification reliability. A controlled 2×2 experimental design isolates the effects of feature domain and class distribution independently, enabling clean attribution of performance changes to retrieval geometry rather than to model or prompt differences.

足彩app哪个是正规的 central finding is that retrieval geometry, not prompt design or model capability, governs classification reliability. When real-world traffic is heavily imbalanced, a standard FAISS index becomes geometrically corrupted, causing consistent misclassification — not because the LLM reasons poorly, but because the examples it retrieves are misleading. This work introduces NatBal (Natural-Balanced Index Conditioning), a correction that rebuilds the FAISS index from balanced training data regardless of test distribution, restoring reliable performance across all evaluation conditions without any model training or fine-tuning. 足彩app哪个是正规的 corrected framework approaches the detection performance of a fully supervised classifier while additionally producing human-readable, neighbor-grounded explanations of each decision.

足彩app哪个是正规的se results reframe LLM-based intrusion detection as a geometry-sensitive reasoning problem, and provide a fully reproducible reference benchmark for researchers evaluating LLM behavior in network security applications.

Wednesday, May 27

Harsh Makarand Jannawar

Chair: Dr. Min Chen
Candidate: Master of Science in Cybersecurity Engineering
9:30 A.M.; Join Harsh Makarand Jannawar’s online defense
足彩app哪个是正规的sis: AI Security Compliance and Testing Framework for Large Language Model Systems

Large Language Models are being integrated into enterprise workflows at a pace that has outrun the security frameworks designed to protect them. Conventional compliance standards such as SOC 2 and ISO 27001 provide no coverage for LLM-specific vulnerabilities including prompt injection, sensitive information disclosure, and system prompt leakage. 足彩app哪个是正规的 OWASP LLM Top 10 defines the relevant threat taxonomy, but no unified automated pipeline exists to translate those controls into repeatable, evidence-based test cases. This research investigates whether automated, systematically constructed adversarial testing can reliably surface exploitable vulnerabilities across LLM applications of varying hardness, and whether evolutionary attack strategies can reach attack surfaces that static prompt libraries cannot anticipate. To address these questions, this research employs a Design Science Research methodology. A 279-prompt library was constructed through three-source triangulation, drawing from CTF competition wins, industry AI red-teaming competition data, and peer-reviewed literature, grounding every technique in documented real-world effectiveness. Three target configurations were designed with isolated independent variables to evaluate detection rates across a baseline system, a hardened system, and a RAG-augmented system. An evolutionary attack engine implementing the SPE-NL genetic algorithm was developed and evaluated across all three configurations. Judge reliability and inter-rater agreement were validated through independent assessment by two practicing cybersecurity professionals.

Following this research design, AegisLLM was implemented as an automated security testing suite operationalizing six OWASP LLM Top 10 controls. Empirical evaluation demonstrates that targets resistant to the full static library fall to SPE-NL-evolved payloads within three to five generations, confirming that adaptive evolutionary testing reaches attack surfaces that curated static libraries cannot anticipate. 足彩app哪个是正规的 LLM-as-a-judge classification pipeline achieved 94.5% inter-rater agreement with zero crossover errors between SUCCESS and NO_SUCCESS labels. This thesis contributes to the field in three respects. First, it provides an empirically validated, open-source prompt library mapped explicitly to the OWASP LLM Top 10, sourced from ecologically valid real-world adversarial data. Second, it demonstrates the viability of evolutionary prompt mutation as a structured research method for LLM security evaluation, not merely as an engineering technique. Third, it establishes a replicable evaluation framework combining automated semantic judgment with human inter-rater validation, offering a methodological foundation for future LLM security research.

Master of Science in Electrical & Computer Engineering

SPRING 2026

Friday, May 8

Xiameng Zhang

Chair: Dr. Madhava Vemuri
Candidate: Master of Science in Electrical & Computer Engineering
10:00 A.M.; Discovery Hall (DISC) 464
足彩app哪个是正规的sis: A Study of Synchronous and Asynchronous Circuits in Monolithic 3D Integration

Monolithic three-dimensional integration (M3D) has emerged as a promising pathway for extending integrated-circuit scalability beyond conventional two-dimensional (2D) technology. By sequentially stacking active device layers and connecting them through fine-grained metal interlayer vias (MIVs), M3D can improve device density, reduce interconnect length, and enhance energy efficiency. This thesis investigates M3D from both application-driven and layout-methodology perspectives.

While M3D offers density and interconnect benefits, its sequential fabrication and vertical stacking introduce reliability concerns related to process variation, thermal effects, and timing uncertainty. To address these challenges, we first studied the quasi delay insensitive asynchronous circuits based on Null Conventional Logic (NCL). 足彩app哪个是正规的 asynchronous circuits address these challenges by eliminating the global clock and using local handshaking, making them robust to timing variations. To explore the complementary benefits of M3D and QDI design, this work proposes a transistor-level M3D methodology for static NCL threshold gates. Results show that M3D-NCL substantially reduces area while improving delay and power over 2D implementations.

足彩app哪个是正规的 second part studies the MIV placement opportunities and design consideration which affect the area, delay, skew, and power of M3D standard-cell designs. A methodology is proposed to study and compare conventional 2D and M3D standard cells in terms of power, performance, and area (PPA). Using this methodology, standard cells are implemented in both 2D and M3D, with different MIV placement strategies considered for the M3D case. Results show that the proposed designs achieve large area reduction with favorable delay, skew, and power trends.