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During a downtime period in which the Large Hadron Collider is not producing data, Princeton and CERN physicists work on the installation of the pixel luminosity telescope of the CMS experiment. Shown from left: David Stickland, Princeton; and Rob Loos and Anne Dabrowski, CERN.
© 2015-2018 CERN
With the goal of creating next-generation computing power to support high-energy physics research, the National Science Foundation (NSF) announced today that Princeton University will lead a new NSF-funded coalition of 17 research universities to be called the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP).
Deep Learning Leader William Tang
(Photo by Elle Starkman/Office of Communications)
To capture and control the process of fusion that powers the sun and stars in facilities on Earth called tokamaks, scientists must confront disruptions that can halt the reactions and damage the doughnut-shaped devices. Now an artificial intelligence system under development at the U.S.
At podium, Alfredo Canziani, post-doc at NYU’s Center for Data Science, speaks to CoDaS-HEP attendees about machine learning. Photo by Florevel Fusin-Wischusen, Princeton Institute for Computational Science and Engineering
As thunderstorms rumbled across the region, fifty-six high energy physics graduate students and twelve presenters from across the United States, as well as from India, Pakistan, France, and Norway, gathered at the Princeton Center for Theoretical Science (PCTS) to attend the Computational and Data S
The TIGER HPC computing cluster in the High Performance Computing Research Center, Princeton’s 47,000-square-foot data center.
Four members of the PICSciE-affiliated research team that designed the experiments and developed the FRRN code: Chief architect of the FRNN is Julian Kates-Harbeck (inset photo), a graduate student at Harvard University and a DOE Office of Science Computational Science Graduate Fellow; Eliot Feibush (standing, center), a computational scientist and experienced data analyst at PPPL, Dr. Alexey Svyatkovskiy (seated), a deep learning/AI expert and collaborator, and Dr. William Tang, lead researcher. Photo credit: Elle Starkman, Princeton Plasma Physics Laboratory
In scientific research, results don’t just have to be good—they have to be reproducible. Members of the PICSciE-affiliated research team are achieving both, as they apply a deep-learning AI approach to the problem of disruption forecasting in tokamak fusion plasmas.
Princeton’s Research Software Engineering group is in high demand, reflecting a growing trend in academic research.
Inventor Jeroen Tromp, the Blair Professor of Geology and professor of geosciences and applied and computational mathematics
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