The virtual conference "From Quarks to Cosmos with AI", organized by Carnegie-Mellon University and which took place last week, included a set of problems in particle and astroparticle physics that participants were invited to tackle with machine learning tools, during four 2-hour afternoon sessions.
I took part to the conference by lecturing about applications of differentiable programming to fundamental physics, as well as by organizing (with my collaborators Giles Strong and Lukas Layer) a data challenge centered on a tough regression problem.
The problem in question The virtual conference "From Quarks to Cosmos with AI", organized by Carnegie-Mellon University and which took place last week, included a set of problems in particle and astroparticle physics that participants were invited to tackle with machine learning tools, during four 2-hour afternoon sessions.
I took part to the conference by lecturing about applications of differentiable programming to fundamental physics, as well as by organizing (with my collaborators Giles Strong and Lukas Layer) a data challenge centered on a tough regression problem.
The problem in question
The virtual conference "From Quarks to Cosmos with AI", organized by Carnegie-Mellon University and which took place last week, included a set of problems in particle and astroparticle physics that participants were invited to tackle with machine learning tools, during four 2-hour afternoon sessions.
I took part to the conference by lecturing about applications of differentiable programming to fundamental physics, as well as by organizing (with my collaborators Giles Strong and Lukas Layer) a data challenge centered on a tough regression problem.
The problem in question
The virtual conference "From Quarks to Cosmos with AI", organized by Carnegie-Mellon University and which took place last week, included a set of problems in particle and astroparticle physics that participants were invited to tackle with machine learning tools, during four 2-hour afternoon sessions.
I took part to the conference by lecturing about applications of differentiable programming to fundamental physics, as well as by organizing (with my collaborators Giles Strong and Lukas Layer) a data challenge centered on a tough regression problem.
The problem in question
