Solar event archives from NOAA Space Weather Prediction Center (SWPC) contain observations of solar phenomena such X-ray flares (XRA), optical flares (FLA), disappearing solar filament (DSF), radio bursts (RSP), and other solar events. However, these data are currently stored across multiple sources and incompatible formats. As a result, this makes event retrieval, cross-comparison, and large-scale analysis complicated. In this study, we introduce a computational framework to extract and standardize solar event data from SPWC archives into a unified structure. Our approach automates parsing event reports, extracts key features such as event classification and timing, and organizes them to convert records into a consistent format across datasets. By reducing differences in how event records are stored and represented, this framework can enhance the usability of the solar records. The ultimate goal is to support the development of a tool supporting easier and faster access to solar event records based on user-selected criteria such as event type or time range. This standardization aims to improve data accessibility, providing a foundation for further space weather research.

Machine learning-based phishing detection systems increasingly rely on high-confidence predictions from deep neural models, yet confidence alone provides limited assurance of reliability in adversarial environments. Small, semantics-preserving manipulations such as homoglyph substitution and paraphrasing can induce confident misclassifications while remaining indistinguishable to human recipients, exposing a critical vulnerability in modern email security pipelines. We present TAED, a Trust-Aware Explainable Defense system that explicitly evaluates prediction trustworthiness rather than relying solely on opaque confidence scores. TAED computes a trust score by integrating model confidence with explanation fidelity, which measures alignment between model reasoning and known phishing indicators, and explanation stability, which quantifies sensitivity to minor input perturbations. We evaluate TAED alongside a diverse set of statistical and neural phishing detectors using a realistic adversarial dataset constructed through multiple evasion strategies. Our results reveal a systematic confidence–robustness paradox in which complex Transformer-based models exhibit strong clean-data performance but substantial brittleness under adversarial manipulation, while simpler feature-based models demonstrate greater resilience. By leveraging explanation-derived trust signals and selective escalation within a hybrid detection pipeline, TAED identifies unreliable high-confidence predictions and improves robustness against adversarial evasion. These findings demonstrate that explainability can be operationalized as a practical security mechanism for assessing model reliability in adversarial phishing detection systems.

Agent-based models (ABMs) are widely used in computational biology to simulate complex processes such as infectious disease transmission. However, many research-grade models are implemented primarily as backend systems and lack graphical interfaces that allow researchers to efficiently configure simulations and interpret outputs. In collaboration with the Biophysics Department, this project focused on the development of a graphical user interface (GUI) for an existing viral agent-based simulation platform previously implemented without an interactive frontend.
The implemented interface integrates with the existing backend simulation environment deployed on laboratory systems, enabling structured parameter configuration, simulation execution, and visualization of model outputs. Development focused on frontend architecture, parameter validation mechanisms, backend connectivity, and data visualization components for simulation result analysis. Additional work included interface refactoring and codebase cleanup to improve maintainability and usability.
The resulting system provides a structured workflow for configuring and executing simulations while preventing invalid parameter configurations through input validation. By extending the existing modeling framework with a robust graphical interface and visualization capabilities, this work improves accessibility and operational efficiency for researchers conducting computational epidemiology experiments within the laboratory environment.

Machine Performance Check Plus (MPC+) is a software platform designed to improve quality assurance workflows for Varian TrueBeam linear accelerators used in radiation therapy. The system automatically collects and processes Machine Performance Check (MPC) data generated by clinical machines, including imaging files and measurement results, and converts them into structured, analyzable information. The platform provides a web-based dashboard that allows medical physicists and clinical staff to review machine performance metrics, visualize trends, and quickly identify values that fall outside acceptable tolerances. MPC+ also supports digital sign-off workflow and audit trails to ensure compliance with regulatory and clinical standards. By consolidating data from multiple machines and clinics into a single interface, the system reduces the time required for daily QA review while improving reliability and traceability. Overall, the project aims to make the quality assurance process more efficient, data-driven, and scalable for radiation oncology clinics operating Varian TrueBeam systems.

This senior design project centers on VANTAGE (Visual Autonomous Navigation and Task-driven Agentic Ground-to-air Engine), an AI-driven drone operations platform developed with MavenCode, a leading AI/ML solutions provider in the Dallas–Fort Worth area. VANTAGE enables users to command drones through natural language while integrating real-time perception tools such as speech-to-text, text-to-speech, object detection, semantic segmentation, and vision-language reasoning. The system combines a FastAPI backend, agent-based tool orchestration, and a web dashboard that supports both mission control and direct testing of AI tools through uploaded audio, image, and video inputs. Our team’s work spans full-stack development, model integration, and interface design to deliver a practical, user-centered platform for intelligent aerial autonomy. Together, these components demonstrate an end-to-end AI product approach that aligns with MavenCode’s mission of empowering organizations through training, product development, and consulting.