AI and in particular machine learning has been progressing so fast that we sometimes know that an algorithm works well under certain assumptions but know much less about why it works, when it fails, or how to control it intuitively and directly. Studying algorithms via first explaining them, then making predictions about their output based on our hypotheses, and finally controlling them effectively are thus fundamental problems our lab strives to solve.