How To Hack A Hackathon

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 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

Old NID

255467

Tommaso Dorigo

Tommaso Dorigo is an experimental particle physicist, who works for the INFN at the University of Padova, and collaborates with the CMS experiment at the CERN LHC. He coordinates the European network AMVA4NewPhysics as well as research in accelerator-based physics for INFN-Padova, and is an editor of the journal Reviews in Physics. In 2016 he published the book "Anomaly! Collider physics and the quest for new phenomena at Fermilab". Read more