“A Multimodal Machine Learning Approach for Solar Flare Prediction Using Polarity Inversion Lines Data with Low-Rank Fusion” accepted at the IEEE 24th International Conference on Machine Learning and Applications.

“Towards Multimodal Solar Flare Prediction Using Magnetic Polarity Inversion Lines” accepted at the IEEE 7th International Conference on Cognitive Machine Intelligence.

“A Multimodal Tiered Magnetic Polarity Inversion Features Dataset for Space Weather Forecasting” accepted for publication in Elsevier’s Data in Brief journal.

“Building an Extensible Data Ecosystem for Solar Magnetic Polarity Inversion Lines” presented at the 32nd ACM International Conference on Advances in Geographic Information Systems.

Began designing a multimodal deep learning framework for solar flare prediction, integrating image-based and time-series data from solar observatories.

Joined the Department of Computer Science at GSU as a Ph.D. student and Research Assistant in the Data Mining Lab.

Contributed to EU Horizon 2020 CANOPIES project on human–robot collaboration for precision agriculture at Sapienza University of Rome.