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Abstract: All-sky photometric time-series exoplanet missions have allowed for the monitoring of hundreds of thousands of stars, allowing for statistical analyses of stellar properties, specifically activity, across the Hertzsprung-Russell diagram. In this talk, I will discuss the convolutional neural network (CNN), stella, specifically trained to find flares in TESS short-cadence data. I will present the results of the CNN applied to 3200 young (< 1 Gyr) stars in order to evaluate flare rates as a function of age and mass. Additionally, we measure rotation periods for 1500 of our targets. The combination of flare rates and rotation periods allowed us to investigate surface starspot coverage as well as develop analytical models for magnetic field braiding to interpret differences in flare frequency distributions (FFDs). The efficiency and accuracy of the CNN allows for rapid flare detection on all stars observed at 2-minute cadence. Towards the end of my talk, I will present FFDs for 10^5 stars observed during TESS's primary mission. By fitting the FFD for different mass bins, we find that all stars exhibit distributions of flaring events indicative of a self-organized critical state. This suggests that magnetic reconnection events maintain the topology of the coronal magnetic fields in a self-organized critical state in all stars, universally. If this is true, we will be able to infer properties of magnetic fields, interior structure, and dynamo mechanisms for all stars, which are otherwise unresolved point sources.