The school on Computational and Data Science for High Energy Physics (CoDaS-HEP) was created to fill a void in the training of graduate students and postdocs working in HEP. Training young researchers in the latest computational tools and techniques is essential to their future careers in both research and industry. To be successful today’s grad students require a mix of particle physics domain knowledge and advanced software skills. Relevant computing topics are however often missing from traditional physics coursework and, as young researchers begin their active research careers, they discover the need for these skills, but are no longer in a position to follow such courses.
The school was intially developed in 2017-2018 as part of the NSF-funded Parallel Kalman Filter Tracking project, in particular through grants PHY-1520942, PHY-1520969, PHY-1521042 and ACI-1450377. The school is realized in partnership with the Princeton Institute for Computational Science and Engineering (PICSciE).
Building on the success of the first iterations of the school, direct support will continue from 2019 through two other NSF-funded projects: FIRST-HEP and IRIS-HEP. Additional collaborations have also been added (see full list below).
A dedicated compute platform (the CoDaS-HEP Platform) is also being developed by the Enrico Fermi Institute at the University of Chicago to support the exercises portion of these training activities.
Collaborating Research Projects
Scientific Advisory Committee
Peter Elmer (Chair) - Princeton University, Department of Physics
Ian Cosden - Princeton University, Research Computing
Kyle Cranmer - New York University, Department of Physics & Center for Data Science
Oliver Gutsche - Fermi National Accelerator Laboratory
Sudhir Malik - University of Puerto Rico - Mayaguez
Avi Yagil - University of California, San Diego, Department of Physics
Peter Wittich - Cornell University, Department of Physics