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Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory
Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models.
Interpretable Uncertainty Quantification in AI for HEP
Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty.
Data Science and Machine Learning in Education
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research.
MonarchNet: Differentiating Monarch Butterflies from Butterflies Species with Similar Phenotypes
We seek to contribute to the study of biodiversity and butterfly ecology by providing a novel method for computational classification of these particular butterfly species.
Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery
Natural disasters ravage the world's cities, valleys, and shores on a regular basis.
