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Physics Next: Machine Learning Agenda

October 8-10, 2018

Hyatt Place Long Island hotel, Riverhead, NY

Monday, October 8
7:30 - 8:30 Breakfast (Sea Star South Meeting Room)
8:45 - 8:55 Opening remarks from Organizers
8:55 - 9:00 Brief statement from videotaping crew
9:00 - 12:30 Session I: Overview of Machine Learning
9:00 - 10:00 Statistical physics and machine learning: A 30 years perspective
Naftali Tishby (Hebrew University of Jerusalem)
10:00 - 10:30 Machine learning and the renormalization group
Maciej Koch-Janusz (Swiss Federal Institute of Technology in Zurich)
10:30 - 11:00 Coffee Break
11:00 - 11:30 Opportunities for infusing physics into AI/ML algorithms
Animashree Anandkumar (California Institute of Technology)
11:30 - 12:00 Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks
Yoav Levine (Hebrew University of Jerusalem)
12:00 - 12:30 On Learning Graph Inverse Problems with Neural Networks
Joan Bruna (New York University)
12:30 - 1:30 Lunch (Terrace)
1:30 - 2:00 Free time
2:00 - 5:30 Session II: Machine Learning in Astro and Particle Physics
2:00 - 3:00 Promise and Challenges of Machine Learning in Particle Physics, Astrophysics, and Cosmology
Kyle Cranmer (New York University)
3:00 - 3:30 Deep Learning for Science: Steps to opening the blackbox
Shirley Ho (Flatiron Institute and Princeton University)
3:30 - 4:00 Coffee Break
4:00 - 4:30 Understanding Neutrino Interactions Using Deep Learning
Adam Aurisano (University of Cincinnati)
4:30 - 5:00 Statistical challenges in cosmological distance measurements
Markus Rau (Carnegie Mellon University)
5:00 - 5:30 Deep learning for generation of events and processes in particle Physics, cosmology, and fluid dynamics
Karthik Kashinath (National Energy Research Scientific Computing Center)
5:30 - 7:00 Free time
7:00 Dinner in the Aquarium (Coliseum)
Tuesday, October 9
7:30 - 8:15 Breakfast (Sea Star South Meeting Room)
9:00 - 12:30 Session III: Machine Learning in Quantum Many-Body Physics
9:00 - 10:00 Learning quantum states with generative models
Juan Carrasquilla (Vector Institute for Artificial Intelligence)
10:00 - 10:30 Neural-Network and String-Bond States: From Chiral Topological Order to Image Recognition
Ignacio Cirac (Max-Planck-Institute for Quantum Optics)
10:40 - 11:00 Coffee Break
11:00 - 11:30 Predicting energies from electron densities: Machine learning for reactive molecular dynamics
Leslie Vogt (New York University)
11:30 - 12:00 Neural Network Renormalization Group
Lei Wang (Chinese Academy of Sciences)
12:00 - 12:30 From Physics to Machine-learning and Back
Edgar A. Engel (Swiss Federal Institute of Technology in Lausanne)
12:30 - 1:30 Lunch (Terrace)
1:30 - 2:00 Free time
2:00 - 4:00 Session IV: Applied and Instrumental Physics
2:00 - 3:00 Optical random features for large-scale machine learning
Laurent Daudet (Paris Diderot University / LightOn.io)
3:00 - 3:30 Intelligent Controls for Particle Accelerators and Other Research and Industrial Infrastructures
Sandra Biedron (University of New Mexico)
3:30 - 4:00 Machine learning in biology: Teaching an autonomous glider to soar like a bird
Gautam Reddy (University of California, San Diego)
4:15 - 7:00 Reception/Meet the Editors (Bus from hotel to editorial office and back)
7:00 Dinner at the editorial office
Wednesday, October 10
7:30 - 8:30 Breakfast (Sea Star South Meeting Room)
9:00 - 12:30 Session V: Miscellaneous Topics; Quantum Machine Learning, Machine Learning in Biophysics and Classical Physics
9:00 - 10:00 Making quantum algorithms learn from data
Maria Schuld (University of KwaZulu-Natal)
10:00 - 10:30 From Reinforcement Learning to Spin Glasses: The Many Surprises on Quantum State Preparation
Pankaj Mehta (Boston University)
10:30 - 11:00 Coffee Break
11:00 - 11:30 Using Machine Learning for Analysis and Prediction of High-Dimensional Spatiotemporal Chaotic Dynamical Systems
Jaideep Pathak (University of Maryland)
11:30 - 12:00 Advances in machine learned potentials for molecular dynamics simulation
Kipton Barros (Los Alamos National Laboratory)
12:30 Lunch (Terrace) and Conclusion of Workshop

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