Grace Vesom

Ardoriously and arduously, I research the intersection of feature development and machine learning, specifically for images and video -- this includes unsupervised, supervised, and engineered features. With sufficient amounts of data and a given paradigm, the data can self-express its most outstanding factors in a learning environment, though paradigms and interpretation require some intuition. I believe well-chose image processing mechanisms and representation transformations can vastly improve the process.

I curate, adapt, and implement computer vision and machine learning research for application development.

To boot, I'm extremely passionate about gender equality, perception, volleyball, and the science of many, many things.