Physics Program

June 7th

• Physics Track: Machine Learning in Large Physics Experiments
• 9:00-9:40 – Hunter Gabbard (LIGO)
• 9:40-10:20 – Michael Wood-Vasey ( of Pittsburgh)
• 10:20-10:40 – Rachel Mandelbaum (CMU) – “Deep learning applications to astronomical imaging”
• break
• 11:00-11:40 – Sergei Gleyzer (University of Florida) – “Machine Learning at the Large Hadron Collider”
• 11:40-12:00 – Mauro Verzetti ( of Rochester) – “Machine learning techniques for jet flavour identification at CMS”
• 12:00-12:20 – Michael Andrews (CMU) – “End-to-end Deep Learning Applications for Event Classification in CMS”
• 12:30-1:30 – lunch/posters
• 1:30-2:30 – plenary
• Physics Track: Machine Learning in Large Physics Experiments
• 2:30-3:10 – Michael Richman (IceCube) – “Machine Learning Applications in Neutrino Astrophysics with IceCube“
• 3:10-3:50 – Alex Malz (NYU) – “How to advance cosmology with the data products of machine learning”
• 3:50-4:00 – discussion time
• break
• 4:30-4:50 – Bennett Marsh (UC Santa Barbara) – “Monitoring Tools for the Muon System in the Compact Muon Solenoid Detector”
• 4:50-5:10 – Lucio Anderlini (INFN Firenze) – “Advanced machine-learning solutions in LHCb operations and data analysis”
• 5:10-5:30 – Kamil Deja (Warsaw University of Technology) – “Using Machine Learning Methods for Improving Data Quality in the ALICE Experiment”
• 5:30-5:50 – Simon Wilson (Trinity College Dublin) – “Scalable Bayesian source separation applied to the Cosmic Microwave Background”

June 8th

• Physics Track: Emerging Physics from Data
• 9:00-9:40 – Gautham Narayan (Space Telescope Science Institute) – “Using Machine Leaning to Identify Things That Go Bump in the Night”
• 9:40-10:20 – Daniel Tamayo (University of Toronto) – “Predicting the Fate of Planetary Systems Using Supervised Learning”
• 10:20-10:40 – William Heller (Oak Ridge National Laboratory) – TBA
• break
• 11:00-11:40 – Gabriel Perdue (Fermilab) – “Overview of ML applications in non-LHC particle physics experiments”
• 11:40-12:00 – Raphael Friese ( für Exp. Kernphysik Karlsruhe) – “Mass regression with deep neural networks”
• 12:00-12:20 – Zhenbin Wu (University of Illinois at Chicago) – “Implementing Machine Learning Algorithms on FPGAs”
• 12:30-1:30 – lunch/posters
• 1:30-2:30 – plenary
• Physics Track: Computational Physics & Theory
• 2:30-3:10 – Karan Jani (LIGO)
• 3:10-3:30 –
• 3:30-3:50 – Matthew Ho (CMU) – “Improving Mass Measurements of Galaxy Clusters through Applications of Machine Learning”
• break
• 4:30-4:40 – 2 x 5 min Poster Presentations
• 4:40-5:20 – Etienne Bachelet (Las Cumbres Observatory) – “Exoplanet detection with Machine Learning”
• 5:30-6:00