APS Journals

Physics Next: Machine Learning

October 8-10, 2018

Organizers

Lenka Zdeborova, CEA Saclay, France
Giuseppe Carleo, Flatiron Institute, US

Meeting Coordinators

Yujun Wang, Associate Editor, Physical Review A
Sarma Kancharla, Associate Editor, Physical Review B

Location

Hyatt Place Long Island hotel, Riverhead, Long Island, NY
For questions about logistics please contact Eunice Toro (eunice@aps.org, 631-591-4000).

Machine learning is bringing a revolutionary reform to industry and scientific research. In a variety of applications about automated control, instrumental design, and data analysis, machine learning has proven to be an efficient and accurate tool to replace or even supersede human intelligence. In physics research, machine learning has found its place in astronomy, particle physics, large-scale computations, etc. Machine learning provides a bright future for making breakthroughs in physics by its power of finding solutions with processing/analyzing massive data sets.

Targeted Attendees

• Invited experts on machine learning techniques and applications in interdisciplinary studies, including physics, biology, computer science, mathematics, etc.
• Physical Review editorial staff from across the journals

Meeting Activities

• Overview presentations
• Structured discussions
• Meet the APS Editors Reception
• Afternoon/Evening excursions

Agenda