Physicist Paul Ginsparg awarded Compton Medal

The American Institute of Physics (AIP) has awarded the 2020 Karl Taylor Compton Medal for Leadership in Physics to Paul Ginsparg, professor of physics and information science and founder of arXiv. The medal and $10,000 prize is presented by AIP every four years to “highly distinguished physicists who have made outstanding contributions through exceptional statesmanship in physics.”

The selection committee cited Ginsparg “for his paradigm-changing contributions to physics information sharing by inventing, developing and managing the arXiv system for electronic distribution of pre-publication (or preprint) papers.” More than 155,000 papers per year are uploaded to arXiv from researchers around the world; it currently contains 1,635,489 articles and processes millions of downloads a month.

“Open, global communication drives science forward. No longer is the broadcast of a lab’s results hostage to its preprint budget, the number of envelope stuffers it employs or the limited distribution lists that result,” said David Helfand, chair of the AIP board of directors. “The Compton Medal for statesmanship in science is an appropriate recognition of Prof. Ginsparg’s paradigm-changing work to enhance scientific communication worldwide.”

“It’s an incredible honor to have my name appear alongside a distinguished group of physicists, many of whom I’ve known, all of whom I’ve regarded as role models since early in my career,” Ginsparg said.

He has also been recognized for his work as a Fellow of the American Physical Society, a MacArthur Fellow and a White House Champion of Change, among other fellowships and honors. He currently serves on arXiv’s Scientific Advisory Board and Physics Advisory Committee.

Ginsparg’s academic focus is on quantum information and quantum computing. His current projects include studying the research applications of quantum computing to high energy physics as well as the impacts of machine learning on both quantum physics and information science. He uses techniques from statistical mechanical models to investigate community detection in networks and has used these to develop new algorithms for clustering subject areas.

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