Computational and data science research to enable discoveries in fundamental physics
IRIS-HEP is a software institute funded by the National Science Foundation. It aims to develop the state-of-the-art software cyberinfrastructure required for the challenges of data intensive scientific research at the High Luminosity Large Hadron Collider (HL-LHC) at CERN, and other planned HEP experiments of the 2020’s. These facilities are discovery machines which aim to understand the fundamental building blocks of nature and their interactions. Full Overview
News and Featured Stories:
The first USATLAS Bootcamp was a huge success. 31 students and more than 8 instructors met at the Lawrence Berkeley National Laboratory to cover four topics from both a general and experiment specific viewpoint.
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For the third consecutive summer, high energy physics graduate students, postdocs and instructors from across the United States, as well as from India, Italy and Switzerland, gathered at Princeton University to attend the school on Tools, Techniques and Methods for Computational and Data Science for High Energy Physics or CoDaS-HEP, held this year from July 22 to 26.
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The very first Machine Learning Hackathon at the Physics Department
of the University of Puerto Rico at Mayaguez took place on 24-26
April, 2019, organized by IRIS-HEP Training, Education and Outreach
coordinator (and UPRM physics professor) Sudhir Malik.
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The National Science Foundation (NSF) has launched the Institute
for Research and Innovation in Software for High-Energy Physics
(IRIS-HEP), a $25 million effort to tackle the unprecedented torrent
of data that will come from the High-Luminosity Large Hadron Collider
(HL-LHC), the world’s most powerful particle accelerator.
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IRIS-HEP Fellows
The IRIS-HEP Fellows program provides funding support for selected individuals to spend several months working closely with a mentor on an HEP software R&D topic relevant to the Institute. Find out more about opportunities with the IRIS-HEP Fellows program. Recent fellows include:
Jun-Aug 2019
Jan-Mar 2020
Jun-Sep 2019