A Cross-Model Validation Framework for Solar Flare Forecasting Using NASA CCMC Flare Scoreboard Predictions

Type: Undergraduate
Author(s): Kritika Karanjit Computer Science Robin Chataut Computer Science Chetraj Pandey Computer Science
Advisor(s): Chetraj Pandey Computer Science

Solar flares are significant space weather phenomena that can impact satellites, communication systems, and many technological infrastructures, rendering accurate flare forecasting a crucial objective in heliophysics study.The NASA Community Coordinated Modeling Center (CCMC) Flare Scoreboard collects predictions from multiple solar flare forecasting models developed by different research groups. While this resource provides a useful platform for comparing different forecasting approaches, systematic validation of these models remains challenging because predictions are reported in different formats and are not easily comparable across models. In this work, we develop an automated framework to collect and organize flare forecasts from several models available in the CCMC Flare Scoreboard and convert them into a consistent dataset that allows direct comparison between models. The processed dataset includes predictions across multiple years and forecast windows. To evaluate model performance, we compare the predicted flare probabilities with observed flare events reported in the SolarSoft (SSW) Latest Events archive. By aligning forecast windows with actual flare occurrences, we establish a consistent approach for validating model predictions. This approach facilitates a systematic comparison of forecasting behaviour among various models and assists in identifying those that exhibit superior or inferior predicted ability.The resulting pipeline provides a reproducible way to analyze solar flare forecasting systems and supports future efforts to improve the reliability of space weather prediction methods.

Integrating Full-Disk and Active-Region Models for Reliable Forecasting of Solar Flares

Type: Undergraduate
Author(s): Duc Le Computer Science Robin Chataut Computer Science Chetraj Pandey Computer Science
Advisor(s): Chetraj Pandey Computer Science

Solar flares are major drivers of space-weather disturbances and can disrupt satellites, communication systems, and navigation infrastructure. Recent deep learning approaches have demonstrated promising performance for solar flare forecasting, yet many models operate either on full-disk solar observations or on isolated active-region patches. This separation limits their ability to combine global solar context with localized magnetic structure and can affect the reliability of predictions. In addition, full-disk models often provide limited information about which regions drive their forecasts. This study presents a two-stage deep learning framework that integrates full-disk and active-region–level analysis within a unified flare forecasting pipeline. The system first performs full-disk inference using a convolutional neural network trained on solar magnetograms to estimate the global probability of flare occurrence. Attribution-based explanations are then generated to identify regions that most strongly influence the model prediction. These regions are mapped back to the solar disk and converted into candidate active-region patches, accounting for solar rotation and spatial alignment. The resulting patches are subsequently analyzed using a dedicated active-region forecasting model trained on SDO HMI SHARP data to produce localized flare probabilities. By integrating global context with targeted active-region analysis, the proposed framework combines two complementary forecasting models into an end-to-end prediction system. The resulting pipeline provides both full-disk and region-level flare probabilities, improving interpretability while enhancing the reliability of flare forecasts through focused secondary analysis of the most relevant solar regions.

Using AlphaFold2 to Identify Novel Drug Targets Against Cryptococcus

Type: Undergraduate
Author(s): Francisco Lugo Gonzales Computer Science
Advisor(s): Natalia Castro Lopez Biology

Building a Unified Interface for Solar Event Data Retrieval from SWPC Archives

Type: Undergraduate
Author(s): Cathy Nguyen Computer Science Thu My Banh Computer Science
Advisor(s): Chetraj Pandey Computer Science

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.

TAED: A Trust-Aware Explainable Defense for Phishing Detection Under Adversarial Manipulation

Type: Undergraduate
Author(s): Tam Nguyen Computer Science Robin Chataut Computer Science
Advisor(s): Robin Chataut Computer Science

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.