At Princeton’s CoDaS-HEP summer school, young physicists gain an edge in computational skills

Thursday, Jul 26, 2018
by Melissa B. Moss

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 Science School for High Energy Physics, or CoDaS-HEP from July 23-27, 2018.

The school, a combination of lectures and hands-on trainings led by instructors from Princeton, Cornell, Fermilab, Intel, UC San Diego, Lawrence Berkeley National Lab, Microsoft, New York University, and University of Chicago, is in its second year and is supported by the National Science Foundation, the Princeton Institute for Computational Science and Engineering (PICSciE), and co-sponsored by the Physics Department and the Office of the Dean for Research of Princeton University.

Princeton physicist Peter Elmer, the lead organizer, noted that CoDaS-HEP sprung from the realization that training young researchers in the latest computational tools and techniques is essential to their future careers in both research and industry. To be successful, he explained, today’s grad students “require a mix of deep physics domain knowledge and advanced software skills.”

Tim Mattson, a senior principal engineer at Intel and a CoDaS-HEP instructor, agreed. “I cannot imagine being a physicist today without having deep knowledge of programming,” he said. “CoDaS-HEP is a crucial service to the physics community by teaching computing topics often skipped in a traditional physics education, from key tools such as Git for revision control to parallel programming and machine learning.”


Students at the reception at Prospect House

Students get to know one another at the opening reception at Prospect House. Photo by Florevel Fusin-Wischusen, Princeton Institute for Computational Science and Engineering


One attendee, Namita Shokeen, heard about the summer school from someone who attended last year. Shokeen, a 4th-year doctoral candidate at Wayne State University, is studying the dynamics of nano and microscale particles in different polymer solutions using microscopic and spectroscopic techniques. “I am really excited for the machine learning methods that I can apply to my current research,” she explained. “I’m having issues in reconstructing the particle trajectories in particle tracking.  A few of the CoDaS-HEP lectures, such as Slava Krutelyov’s talk on charged particle tracking, relate directly to my work, so I’m really looking forward to them.”

Schools like CoDaS-HEP “act as a bridge to fill the generation gap that some physicists have,”  she added. “My undergraduate work did not prepare me for the computational science demands of graduate research in physics. This is a great way to learn more about the current needs of computational methods in physics. I would definitely recommend it to other graduate students.”

Namita Shokeen in group discussion

Graduate student Namita Shokeen discusses machine learning with instructor Slava Krutelyov and other attendees.

Peter Elmer sees a rapidly growing need for this Princeton summer program and others like it. “Ultimately my aim would be to ensure that every US graduate student in high energy physics has the opportunity to learn these computational skills. It will help them all individually, whether they stay working in HEP or go elsewhere—and some of them will also wind up contributing to the common/shared software tools used by the whole community.”


Peter Elmer with participants

CoDaS-HEP lead organizer Peter Elmer, third from left, in discussion with students following a lecture.  Photo by Florevel Fusin-Wischusen, Princeton Institute for Computational Science and Engineering

Thanks to generous support, Elmer continued, opportunities should expand to fill the need.  “NSF originally provided the funding for the CoDaS-HEP school as a small educational/training component within a larger R&D project. This covered the school in 2017 and 2018. Earlier this year, Sudhir Malik of the University of Puerto Rico, Ian Cosden of PICSciE, and I submitted a dedicated three-year proposal for half a million dollars to the NSF cybertraining program. We recently learned that our dedicated training proposal was funded and will allow us to continue not only the CoDaS-HEP school for another three  years, but also pursue a develop a broader program of computational training with the US and international HEP community.”


CoDaS-HEP school group photo

The participants of the 2018 CoDaS-HEP school gather for a group photo outside Jadwin Hall. Photo by Rick Soden, Princeton Department of Physics.

Thus far, said Elmer, “with some careful budgeting, and some help from PICSciE, I have been able to accept all qualified students who apply. But I made exactly one announcement about it on various mailing lists of the school, so I'm certain that if I had promoted it more widely, I would have received many more applications. Clearly, one challenge in the coming years will be scalability.”

Questions about CoDaS-HEP can be sent to