Events
Spring 2026 Computing Showcase (C-Day) Winners
Undergraduate Project Winners
First Place
Abstract: Multiplayer Bathtub Racing revives a well-known Southern Polytechnic State University tradition through a digital multiplayer experience built in Unity. The project extends a prior single-player tub racing game by adding online multiplayer
Department: CS
Supervisor: Capstone Professor: Prof. Sharon Perry; Industry Mentor: Shaun Sheppard
Poster
Second Place
Abstract: Modern software development teams routinely introduce subtle bugs — mutable default arguments, bare exception handlers, insecure eval()/exec() calls, resource leaks, hardcoded secrets — that escape manual review but accumulate into technical debt and security risk. Existing linters identify problems but leave remediation to the developer. This project investigates whether a coordinated multi-agent system, combining rule-based static analysis with generative LLM reasoning, can autonomously detect, fix, and document such issues with no developer involvement beyond providing the input file.
Department: CS
Supervisor: Prof. Sharon Perry
Poster | More Information
Third Place
UC-097-186 Nudox - Compiler Based Information Retrieval by Slabysh, Mikita, McCrary, Robert, Wirht, Miles, Ferguson, Lindsey,
Abstract: Nudox is a language-agnostic, version-aware documentation and search platform backed
by compiler-level analysis. By lowering source code to intermediate representations,
Nudox extracts structural metadata, like function signatures, types, and modules independent
of the source language. At the core of the platform is a custom search engine built
around versioned knowledge: queries resolve to graph nodes and expand outward along
structural edges using semantic heuristics, surfacing contextually relevant symbols
rather than flat text matches. The result is a canonical, automatically generated
source of truth that tracks how a codebase evolves across commits.
Department: CS
Supervisor: Prof. Sharon Perry
Poster
Graduate Project Winners
First Place
Abstract: C-Day showcases some of the strongest computing projects at KSU, but once each event ends, past work becomes scattered across semester pages, posters, PDFs, and videos, making it difficult to see long-term trends or build on prior ideas. C-Day Explorer addresses this gap with a centralized, domain-aware web platform that aggregates project records from 21 semesters of C-Day archives, KSU Digital Commons, winner pages, and YouTube presentation videos. The system organizes 1,286 projects into 11 computing domains with high abstract coverage, poster and video links, and similarity-based connections that help users quickly find related work and promising directions for extension. By turning isolated showcase artifacts into an integrated, searchable history of student innovation, C-Day Explorer helps students, judges, and faculty discover prior projects, identify patterns, and move ideas forward beyond a single showcase.
Department: CS
Supervisor: Dr. Bin Luo, Mentor
Poster | More Information
Second Place
GC-173-232 A Surrogate Accountability Framework for Agentic AI Systems by Tubbs, Crystal
Abstract: Agentic AI systems introduce new accountability challenges because autonomous agents
can act, adapt, and execute decisions without continuous human oversight. This research
develops the Surrogate Accountability Framework (SAF), an architectural approach for
embedding external oversight, traceability, and control directly within agentic workflows.
To operationalize SAF, a working system, Chrysalis, was designed and implemented as
a real time governance layer that monitors agent behavior, evaluates decision pressure,
and enforces constraints through validation and intervention mechanisms. By shifting
accountability from post hoc evaluation to continuous system level enforcement, this
approach reduces the risk of compounding errors and enables interpretable, actionable
control signals. The results demonstrate a practical pathway for deploying agentic
AI systems with enforceable accountability in real world environments.
Department: CS
Supervisor: Dr. Martin Brown
Poster | More Information
Third Place
Abstract: Generative AI (GenAI) is increasingly used in pharmacy for drug information and decision support, yet accuracy remains variable. We systematically reviewed studies that reported full prompts and model responses to evaluate correctness across pharmacy-relevant tasks. GenAI performed well on basic drug facts but was inconsistent for patient-specific recommendations and interaction checking, with occasional hallucinations. Findings support cautious, supplementary use in pharmacy practice and education.
Department: SDSA
Supervisor: Dr. Amir Karami
Poster
Undergraduate Research Winners
First Place
Abstract: Students frequently experience delays and confusion regarding financial aid refunds due to unclear system statuses and lack of communication. This project introduces AidFlow, a predictive financial aid transparency system that translates complex financial data into clear explanations, predicts refund timelines, and provides actionable guidance. A rule-based model and system pipeline were developed to simulate real-world scenarios and improve student understanding and decision-making.
Department: CS
Supervisor: Prof. Sharon Perry
Poster
Second Place
Abstract: Quantum machine learning (QML) has emerged as a promising method for overcoming the computational limitations of classical machine learning when analyzing large and complex data sets. This project investigates the application of QML algorithms to real-world science and engineering problems, with a focus on civil and environmental engineering datasets. We develop and evaluate a Python-based system, implemented in Google Colab, that integrates multiple quantum computing frameworks, including PennyLane, TensorFlow Quantum, and Qiskit, to implement and compare several QML models against their classical counterparts. The proposed system explores a range of algorithms such as Quantum Neural Networks, Quantum Support Vector Machines, Quantum Principal Component Analysis, and Quantum Logistic Regression across at least five engineering use cases, including structural health monitoring, traffic flow prediction, air quality, flood forecasting, and pollution modeling. Performance is evaluated using accuracy, computational efficiency, and scalability. The results highlight scenarios in which QML demonstrates a potential advantage over a classical approach, while also identifying the current limitations that come with near-term quantum devices. This work contributes to a modular framework for learning and applying quantum machine learning and provides insight into its practical viability and application within science and engineering applications.
Department: CS
Supervisor: Dr. Yong Shi; Prof. Sharon Perry
Poster | More Information
Third Place
Abstract: Diagnosing Alzheimer’s disease often depends on costly neuroimaging techniques such as MRIs and PET scans, which are not always accessible and can place a significant financial burden on healthcare systems. Existing clinical workflows lack a reliable way to determine which patients truly require these advanced tests, resulting in either unnecessary imaging or delayed and inaccurate diagnoses. To address this challenge, we propose the Uncertainty-Driven Dual-view (UDD) model, a multi-stage framework that integrates low-cost clinical and structural data with uncertainty-aware learning. The model first generates predictions using accessible data and quantifies its confidence, referring only high-uncertainty cases for further evaluation with expensive imaging modalities. This selective escalation strategy enables more efficient use of resources while preserving diagnostic accuracy. By combining multi-modal learning with uncertainty-guided decision making, the proposed approach offers a cost-effective and scalable solution for early Alzheimer’s disease screening.
Department: CS
Supervisor: Dr. Chen Zhao
Poster
Master's Research Winners
First Place
Abstract: This study evaluates how speech disfluencies and prompting strategies impact LLM-based Alzheimer’s Disease (AD) detection. We compared transcripts with preserved disfluencies (ADReSS) against clean transcripts (ADReSSo) using four state-of-the-art LLMs. Key Discovery: Complex prompts induce a "mirror-image" classification bias, where DeepSeek models severely over-classify AD, and GPT-5.2 over-classifies Cognitively Normal (CN) individuals. Optimization Fix: Applying DSPy MIPROv2 effectively mitigated bias in simpler prompts, while TextGrad successfully optimized complex, multi-step prompts.
Department: SWEGD
Supervisor: Dr. Seyedamin Pouriyeh
Poster
Second Place
Abstract: Parametric approaches, such as Mixed Models for Repeated Measures (MMRM), are standard in Alzheimer’s Disease (AD) clinical trials. However, these models often falter when data violates assumptions of normality or follows non-linear trajectories—common occurrences in AD due to floor/ceiling effects on cognitive scales and heterogeneous disease progression. This study evaluates Longitudinal Rank Sum Tests (LRST) as a non-parametric alternative to maintain statistical power and robustness.
Department: SDSA
Supervisor: Dr. Dhrubajyoti Ghosh
Poster
Third Place
Abstract: This study addresses the high hallucination rates of Vision-Language Models (LLMs) when analyzing complex, hybrid construction blueprints. We developed a dual-input pipeline that pairs high-resolution images with a four-layer JSON "Digital Twin" (vector text, raster OCR, geometry) to mathematically ground the LLM's visual interpretation. Key Engineering Achievement: We processed a massive 137-sheet civil engineering project with zero errors. By introducing a multi-tier JSON pruning strategy, we cut token usage by up to 70% and processed the entire batch from $13-$15 to just $0.93. Decision Support Extension: We integrated real-time traffic data (TomTom) and GDOT procedural policies to transform the pipeline from a simple document reader into a strategic project management advisor.
Department: SWEGD
Supervisor: Dr. Minsoo Baek
Poster
PhD Research Winners
First Place
Abstract: We study whether changes in contact-network topology predict transitions into high-risk epidemic periods across several classical empirical proximity network datasets. We use temporal graph learning with persistent homology-based topological signals and evaluate large-outbreak risk using SIR simulations to test whether topological drift serves as an early warning signal for epidemic instability.
Department: SDSA
Supervisor: Dr. Mehmet Aktas
Poster
Second Place
Abstract: We study whether topological loss-based constraints improve multidomain whole-chain protein structure prediction beyond the ColabFold baseline by better preserving the topologies of folded proteins. We benchmark against Wasserstein metrics with our own virtual persistence and RKHS semi-metric constraints as well as higher-order virtual persistence diagrams.
Department: SDSA
Supervisor: Dr. Mehmet Aktas
Poster
Third Place
Abstract: The Strait of Hormuz carries roughly 20% of the world’s daily oil supply. Its closure on March 4, 2026, sent Brent crude surging 36.6%, from $74.64 to a peak of $118.35/barrel. Traditional time-series models fail during such unprecedented shocks because the historical price data contains no analog. This study evaluates whether NLP sentiment analysis can detect crisis signals in news text before they appear in prices, and whether agreement patterns across models predict volatility. We score 2,249 Guardian news articles using five deterministic sentiment models across three tiers: a lexicon baseline (VADER), a general-purpose transformer (RoBERTa-CardiffNLP), and three financial-domain transformers (FinBERT, FinBERT-Tone, DistilRoBERTa-Financial). Financial-domain models detected the crisis 47 days before closure, outperforming general-purpose models by 26 days. Article volume proved the strongest volatility predictor (r=0.76, p<0.0001), while model consensus, not disagreement, signals crisis severity, inverting our original hypothesis. LSTM and XGBoost forecasting experiments show all model variants converging near a naive persistence baseline during the crisis, confirming that point prediction of unprecedented events remains fundamentally limited and reinforcing the value of upstream textual early warning.
Department: SDSA
Supervisor: Dr. Kevin Gittner
Poster
Audience Favorite Presenter
UC-131-162 Physical 8-Bit CPU by Hoerner, Samuel, Merchant, Aryan, Dislen, John, Day, Kyran,
Abstract: This isn’t an app - it’s the machine behind it. A fully functional 8-bit CPU, built from scratch, turning raw signals into real computation. The system combines our custom assembly-to-run, FPGA-driven control unit (CU), discrete transistor Arithmetical Logical Unit (ALU), external memory, and 5 registers to execute programs through a fetch-execute cycle. The result is a tangible computing platform that bridges low-level digital logic with high-level system behavior.
Department: CS
Supervisor: Prof. Sharon Perry
Poster