My research focuses on one central challenge in heliophysics: understanding how solar eruptive activity produces downstream particle and radiation environments that affect both technology and human spaceflight. I study the physical relationship between solar flares, coronal mass ejections, and solar energetic particle detection, with particular interest in timing, propagation, and forecastability across the heliosphere.
This work sits at the intersection of solar physics, space weather, and applied statistical modeling. Across these areas, I use scientific inference, event-based analysis, and machine learning to investigate which solar and heliospheric conditions are most informative for anticipating hazardous radiation exposure.
I study solar flares, coronal mass ejections, and related activity as connected physical processes rather than isolated observations. My goal is to better understand how these events begin, evolve, and shape downstream space weather conditions.
A central focus of my work is understanding how solar energetic particles propagate from the Sun through interplanetary space and how that transport translates into radiation environments that impact human spaceflight. I study when these particles are detected, how long they take to arrive, and what physical conditions influence their timing, intensity, and potential hazard to astronauts and space-based systems.
I apply computer vision, survival analysis, and machine learning to improve how solar events are detected, characterized, and predicted. Rather than treating AI as separate from science, I use it as a scientific tool for extracting structure from complex, heterogeneous space data.