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