As obesity continues to rise in the United States, bariatric surgery has become as increasingly common medical intervention to support significant and sustained weight loss. However, the procedure presents challenges, as patients must adopt strict dietary guidelines, develop consistent meal tracking habits, and maintain long-term lifestyle changes. Existing weight-loss applications fail to address the unique nutritional requirements of bariatric patients, which include surgery-specific restrictions, medical conditions, personal preference in food, and individualized lifestyle factors. Along with that, they lack integrated long-term monitoring tools that allow healthcare providers to effectively track patient progress and adherence after surgery. This senior design project presents a prototype mobile application developed from scratch to support patients throughout the bariatric journey. The application integrates AI-driven personalization to generate tailored daily nutritional guidance, adapt to individual health data, and provide meal tracking support. In addition, the platform centralizes patient data for healthcare providers, improving long-term monitoring, increasing tracking accuracy, and reducing manual workload. By combining personalized patient support with provider-facing analytics, this solution aims to enhance postoperative adherence and improve long-term surgical outcomes.

The Brazos Safety Systems Weather Application provides insight into weather conditions that affect aviation operations. Weather plays a critical role in flight safety, and understanding conditions during flight operations can help explain incidents or risky behavior that occur. The application integrates aviation weather data sources, including METAR reports and radar imagery, to visualize weather conditions around airports and during historical flights. Users can upload flight records and review the weather conditions associated with those flights through the application. By presenting aviation weather data in a centralized and accessible format, the application supports post-flight analysis and helps identify weather-related factors associated with flight incidents. The goal is to provide insights that assist in understanding past flight conditions and help prevent similar issues in future aviation operations.

Solar flares are among the most significant drivers of space-weather disturbances, motivating ongoing efforts to develop reliable forecasting methods from solar observations. The Solar Dynamics Observatory continuously produces high-resolution full-disk solar imagery used for monitoring solar activity. These observations have enabled substantial progress in machine learning–based flare prediction; however, most models remain confined to research studies, with limited deployment in operational systems that support continuous forecasting and systematic performance validation. This work presents a lightweight operational framework for near-real-time solar flare forecasting built around machine learning models proposed in the literature. The system retrieves full-disk solar imagery from the Helioviewer API, performs automated preprocessing, and generates predictions using a convolutional neural network–based forecasting model. Predictions and corresponding observations are stored to enable periodic forecast verification using standard performance metrics, allowing model performance to be monitored over time and potential prediction drift to be identified. The framework is implemented as an interactive application using Streamlit, providing an integrated interface for automated data ingestion, near-real-time inference, and ongoing model evaluation. The proposed system enables continuous monitoring of solar flare forecasts while providing a practical framework for tracking model performance and detecting prediction drift in operational settings.

We present the AI-Driven Adaptive Tutoring (AIAT) framework, a modular multi-agent system that generates structured, retrieval-grounded, and multimedia-enhanced courses. AIAT targets a common gap in AI in Education: large language models (LLMs) can produce fluent explanations, but they often lack pedagogical structure, factual grounding, and multimodal integration. To address this, AIAT uses a three-stage pipeline. First, a blueprint agent creates a course outline with learning objectives and topic dependencies using schema-validated structured outputs. Second, a chapter-expansion agent instantiates atomic topics with formative questions and summaries in JSON mode. Third, an enrichment agent generates topic-level explanations, visualization specifications, and triggers for narrated video production. Retrieval-augmented generation (RAG) combines a MongoDB Atlas Vector Search backend for course materials and a Pinecone pipeline for PDF-derived knowledge, grounding explanations in external content. A Next.js frontend streams responses and assembles text, diagrams, and videos into a unified learner experience.

The design is explicitly guided by mastery learning, cognitive load theory, and the Cognitive Theory of Multimedia Learning, with principles such as atomic topics, anti-fluff constraints, and visual-verbal alignment encoded in prompts and schemas. We report system-level metrics (e.g., latency by component) and operational reliability, and we outline a concrete evaluation plan, including pre/post-learning assessments, expert rubric-based accuracy checks, and subjective cognitive load measures. We also discuss the equity and accessibility implications of relying on commercial APIs and propose mitigation strategies (e.g., caching, partial use of lightweight models, and instructor-in-the-loop authoring). The contribution of this work is a reproducible architecture that connects multi-agent orchestration, RAG, and multimodal rendering to pedagogical theory, along with an evaluation roadmap that explicitly addresses the current lack of large-scale human studies.

Space Weather Forecasting relies on large volumes of time-stamped solar observations paired with event catalogs describing flare occurrence and intensity. While these datasets are widely available, preparing them for machine learning remains a substantial and often overlooked challenge. Researchers must convert irregular observation streams into consistent temporal samples, construct observation and prediction windows, align events with observations, manage missing data and cadence inconsistencies, and ensure that training and evaluation splits avoid temporal or regional data leakage. These preprocessing steps are typically implemented in ad-hoc scripts that are difficult to reproduce, extend, or compare across studies. We propose an open-source Python library that standardizes the construction of machine-learning-ready datasets for solar event forecasting. The library ingests user-provided observation tables (e.g., SDO image timestamps and file paths) and event catalogs (e.g., GOES flare lists) and automatically generates indexed training samples suitable for PyTorch datasets and data loaders. Users can define flexible observation windows ranging from single time points to multi-frame temporal sequences, specify prediction horizons, and configure event-labeling rules. The framework also provides mechanisms for handling missing observations, irregular cadences, and explicit representation of temporal gaps. To support rigorous experimental design, the library includes reproducible dataset partitioning strategies such as chronological and tri-monthly splits, as well as optional active-region-aware grouping based on NOAA region catalogs. These features allow researchers to build both full-disk and active-region-based forecasting datasets while minimizing common sources of information leakage. Although the initial implementation focuses on solar flare prediction, the framework is designed to be extensible to other space weather phenomena including coronal mass ejections (CMEs) and solar energetic particle (SEP) events. By formalizing the transformation from raw observational records and event lists into reproducible machine learning datasets, this tool reduces the overhead of data preparation and promotes more transparent, comparable, and scalable space weather forecasting research.