Interdisciplinary ML: Bridging Gaps and Building Graphs

Patrick Perrine

[ Abstract ] [ Website ]
[ Slides
Wed 8 Dec noon PST — 2 p.m. PST


Given the many disciplines that encompass ML, it is important that we as researchers better understand academics with differing backgrounds than our own to produce valued contributions.

In this 2-hour social, we pair participants together based on differing levels of experience in related disciplines of ML. These pairings would be determined by a confidential online form. For example, suppose Researcher A identifies as being highly experienced in Reinforcement Learning but has little to no experience in Neuroscience. Researcher A could then be paired with Researcher B, who has a great background in Neuroscience but has had no exposure to Reinforcement Learning. This cycle could repeat every 10 minutes so that every participant can meet a diverse set of researchers across a multitude of disciplines.

To help guide the discussions in these breakout sessions, they will be provided a script of suggested, generic questions to help bridge the gaps of understanding between their fields of expertise. These questions will cover more formal topics such as finding similarity between their fields of study. Some suggested questions may encompass less formal topics such as which leisure activities are commonly pursued by groups of specific disciplines. Participants are encouraged to document these similarities from session to session.

The final activity will include round-table discussions of researchers who have similar backgrounds. These discussions will allow researchers to share what they found to be similar in groups different than their own. This round-table group will be tasked to produce a graph that connects various aspects from field to field. These graphs can then be shared via social media or other platforms for the rest of the conference to view.

These interactions could allow us to form better connections across disciplines and build a better internal knowledge graph of the vast landscape of ML.

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