Interdisciplinary ML Mixer

Patrick R Perrine

Room 310


In this in-person, 2-hour Social, we pair participants together based on differing types of experience in related subdisciplines of ML. These pairings would be determined by a confidential online form that participants would fill out upon entering the Social. For example, suppose Researcher A identifies as being highly experienced in Reinforcement Learning but has little to no experience in Optimization. Researcher A could then be paired with Researcher B, who has a great background in studying Optimization but has had no exposure to Reinforcement Learning. This cycle could repeat every 15 minutes so that every participant can meet a diverse set of researchers across a multitude of subdisciplines. The Social would end with a round table discussion talking about the various topics that they learned about and how it could influence their research moving forward.

Chat is not available.