Physics ∩ ML

a virtual hub at the interface of theoretical physics and deep learning.

In April 2019 five of us organized a meeting at Microsoft Research, Physics ∩ ML, that brought together researchers from machine learning and theoretical physics to learn from each other and push research forward together. As interest in this interface has only grown, the time seems ripe to begin a virtual seminar series in this area. It begins with a cast of wide ranging expertise in order to spark new ideas, giving biweekly seminars on Wednesdays in May and June 2020. We will see how physical insights can motivate advanced algorithms in machine learning, and analysis of theoretical data with machine learning can yield critical new insights in fundamental physics. We plan to add lectures, blog posts, and datasets over time.

For link and password to the talks, please sign up for the Physics ∩ ML mailing list.


Yasaman Bahri, Google Brain
Anna Golubeva, IAIFI
Jim Halverson, Northeastern University and IAIFI
Sven Krippendorf, LMU Munich
Michela Paganini, DeepMind
Fabian Ruehle, Northeastern University and IAIFI
Gary Shiu, University of Wisconsin
Greg Yang, Microsoft Research

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