Papers

• Coughlan, M., Keesee, A., Pinto, V., Mukundan, R., Marchezi, J. P., Johnson, J., Connor, H., & Hampton, D. (2023). Probabilistic forecasting of ground magnetic perturbation spikes at mid‐latitude stations. Space Weather https://doi.org/10.1029/2023sw003446
• Pinto, V. A., Keesee, A. M., Coughlan, M., Mukundan, R., Johnson, J. W., Ngwira, C. M., & Connor, H. K. (2022). Revisiting the ground magnetic field perturbations challenge: A machine learning perspective. Frontiers in Astronomy and Space Sciences https://doi.org/10.3389/fspas.2022.869740

Posters

• GEM 2025: Towards an Interpretable Machine Learning Model of Localized Geomagnetic Disturbances in Terms of Solar Wind and M-I Processes
• AGU 2024: Connecting Localized Geomagnetic Disturbances to the Solar Wind: A Survey of Model Architectures
• GEM 2024: Uncovering the Relationships between Localized Geomagnetic Disturbances and the Solar Wind
• AGU 2023: Multiscale Geoeffectiveness Forecasting: Upgrading the DAGGER Pipeline
• AGU 2023: Characterizing the Spatial Scales of Localized Ground-Level Magnetic Perturbations
• GEM 2023: A Regional dB/dt Forecast Using Deep Learning and Spherical Elementary Current Systems
• AGU 2022: The Influence of Inner Magnetosphere Data on a Regional Geomagnetically Induced Current Forecasting Model
• GEM 2022: Investigating Localized Geomagnetic Perturbations with a Spherical Elementary Current Systems Approach
• ML-Helio 2022: Optimizing a Neural Network for Regional Forecasting of Ground Magnetic Perturbations Using Spherical Elementary Current Systems
• AGU 2021: Forecasting Ground-Level Magnetic Perturbations Using a Spherical Elementary Current System Method
• Virtual-GEM 2021: Forecasting Geomagnetically Induced Currents with a Global Machine Learning Model

Other Works

• Undergraduate senior thesis: Exploring Ground Level Enhancement Precursor Signals in Neutron Monitor Data