Introduce to Jason Yosinski
Hello there! I'm Jason, a Machine Learning scientist, founding member of Uber AI Labs (previously Geometric Intelligence),
and scientific advisor to Recursion Pharmaceuticals. My research focuses on training and understanding neural networks and
figuring out how to make them better. I completed my Ph.D. at Cornell, where at various times I worked with Hod Lipson
(at the Creative Machines Lab), Yoshua Bengio (at U. Montreal's MILA), Thomas Fuchs (at Caltech JPL), and Google DeepMind.
I was fortunate to be supported by a NASA Space Technology Research Fellowship, which gave me the opportunity to trek around and
work with all these great folks.
Introduce to Been Kim
I am interested in designing high-performance machine learning methods that make sense to humans.
Quanta magazine
described well why I am doing what I am doing. Thank you John Pavlus for writing this piece! Here is another short writeup about why I care.
My focus is building interpretability method for already-trained models or building inherently interpretable models. In particular, I believe the language of explanations should include higher-level, human-friendly concepts so that it can make sense to everyone.