ML4Jets2020

from Wednesday 15 January 2020 (08:30) to Friday 17 January 2020 (19:00)
Kimmel Center for University Life (KC 802 )

        : Sessions
    /     : Talks
        : Breaks
15 Jan 2020
16 Jan 2020
17 Jan 2020
AM
08:30
Registration (until 09:00) (KC 802)
09:00
Introduction - Ben Nachman (Lawrence Berkeley National Lab. (US)) Tilman Plehn (until 10:40)
09:00 Welcome and Logistics - Kyle Stuart Cranmer (New York University (US)) Sebastian Macaluso (New York University)  
09:10 Theory Introduction (20'+10') - Prof. Maxim Perelstein  
09:40 Experiment Introduction (20'+10') - Alison Lister (University of British Columbia (CA))  
10:10 ML Introduction (20'+10') - Gilles Louppe (New York University (US))  
10:40 --- Coffee break ---
11:10
Architectures - Taoli Cheng (University of Montreal) Matthew Schwartz (until 12:50)
11:10 CapsNets Continuing the Convolutional Quest - Sascha Daniel Diefenbacher (Hamburg University (DE))  
11:30 Quark-gluon discrimination with point clouds - Vinicius Massami Mikuni (Universitaet Zuerich (CH))  
11:50 Modeling a top jet classifier with two-point energy correlation and geometry of soft emission - Sung Hak Lim (KEK)  
12:10 Deep Learning Jet Substructure from Two-Particle Correlation - Dr Yang-Ting Chien (Stony Brook University)  
12:30 CLARIANT: Covariant Lorentz Group Architecture for Artificial Neural Networks - Alexander Bogatskiy  
09:00
Decorrelation and Semi/Unsupervised approaches - Chase Owen Shimmin (Yale University (US)) Nhan Viet Tran (Fermi National Accelerator Lab. (US)) (until 11:00)
09:00 Data ex Machina: Machine Learning with Jets in CMS Open Data - Eric Metodiev (Massachusetts Institute of Technology)  
09:20 Representation Learning for Collider Events - Jack Collins (SLAC)  
09:40 Tagger-mass decorrelation: experience within CMS - Huilin Qu (Univ. of California Santa Barbara (US))  
10:00 Disco Fever - Gregor Kasieczka (Hamburg University (DE))  
10:20 A Normalizing Flow Model for Boosted Jets - Matthew Drnevich (NYU)  
10:39 Metrics and Machine Learning Algorithms for Collider Space - Ms Tianji Cai (University of California, Santa Barbara)  
11:00 --- Coffee break ---
11:30
ML Beyond HEP - Kyle Stuart Cranmer (New York University (US)) (until 13:00)
11:30 Optimal Transport - Jonathan Niles-Weed (NYU)  
12:00 Beyond monotonic, autoregressive sequence modeling - Kyunghyun Cho  
12:30 Guided discussion  
09:00
Machine Learning Inference and Interpretation - Anja Butter Jesse Thaler (MIT) (until 11:50) (KC 914)
09:00 Deep-Learning Jets with Uncertainties and More - Michel Luchmann (Universität Heidelberg)  
09:20 Bounding high-dimensional uncertainties with adversarial approaches - Chase Owen Shimmin (Yale University (US))  
09:40 OmniFold: Simultaneously Unfolding All Observables - Patrick Komiske (Massachusetts Institute of Technology)  
10:00 GANning away detector effects - Marco Bellagente (Universität Heidelberg)  
10:20 --- Coffee break ---
10:50 Looking into Jets with Machine Learning - Sebastian Macaluso (New York University)  
11:10 Realigning the goals of machine learning with the goals of physics - Prasanth Shyamsundar (University of Florida)  
11:30 ROB: Reproducible Open Benchmarks for Data Analysis Platform - Heiko Mueller Sebastian Macaluso (New York University)  
11:50 --- Lunch ---
PM
12:50 --- Lunch ---
14:10
Generative Models - Jana Schaarschmidt (University of Washington (US)) Martin Erdmann (Rheinisch Westfaelische Tech. Hoch. (DE)) (until 17:45)
14:10 Introduction - Martin Erdmann (Rheinisch Westfaelische Tech. Hoch. (DE)) Jana Schaarschmidt (University of Washington (US))  
14:15 DijetGAN: A Generative-Adversarial Network approach for the simulation of QCD Dijet events at the LHC - Serena Palazzo (The University of Edinburgh (GB))  
14:35 A generator cell for LHC event GANs - Niclas Eich (RWTH Aachen University (DE))  
14:55 How to GAN LHC Events - Ramon Winterhalder (Universität Heidelberg)  
15:15 GAN based event subtraction for Monte Carlo methods - Anja Butter  
15:35 Teaching a Computer to Integrate - Christina Gao Claudius Krause (Fermilab)  
15:55 --- Coffee break ---
16:25 Lund jet images from generative and cycle-consistent adversarial networks - Stefano Carrazza (CERN) Frederic Alexandre Dreyer (Oxford)  
16:45 Fast Calorimeter Simulation in ATLAS: FastCaloSimV2 - Sean Joseph Gasiorowski (University of Washington (US))  
17:05 Fast Calorimeter Simulation in ATLAS with DNNs - Dalila Salamani (Universite de Geneve (CH))  
17:25 Generative Models in CALICE - Engin Eren  
19:00
Workshop Dinner (until 21:00)
13:00 --- Lunch ---
14:20
Anomaly detection (LHCO) - David Shih (Rutgers University) Gregor Kasieczka (Hamburg University (DE)) (until 18:10)
14:30 LHCO Introduction and overview - Gregor Kasieczka (Hamburg University (DE)) David Shih (Rutgers University)  
14:50 Unsupervised new physics searches with data-driven inference - Dr Dillon Barry (Jozef Stefan Institute)  
15:10 Tag N’ Train : Combining Autoencoders and CWoLa for Better Unsupervised Searches - Oz Amram (Johns Hopkins University (US))  
15:30 Variational Autoencoders for Anomalous Jet Tagging - Taoli Cheng (University of Montreal)  
15:50 --- Coffee break ---
16:20 Comparing weak and unsupervised anomaly detection - Pablo Martín  
16:40 LHC Olympics 2020: Columbia University - Alan Mathew Kahn (Columbia University (US))  
17:00 LHC Olympics 2020: MIT - Nilai Sarda (MIT)  
17:20 LHC Olympics 2020: Berkeley Cosmology - George Stein  
17:40 LHCO2020: Outcome of the Challenge - David Shih (Rutgers University) Gregor Kasieczka (Hamburg University (DE)) Ben Nachman (Lawrence Berkeley National Lab. (US))  
13:00
Experimental methods - Philip Coleman Harris (Massachusetts Inst. of Technology (US)) (until 14:50) (KC 914)
13:00 Deep Learning based Energy Reconstruction and Event Generation for the CALICE AHCAL - Erik Buhmann (Hamburg University (DE))  
13:20 Convolutional neural networks with event images for pileup mitigation [cancelled due to illness] - Bernard Brickwedde (Johannes Gutenberg Universitaet Mainz (DE))  
13:30 Secondary Vertex finding in Jets with Graph Neural Networks - Eilam Gross (Weizmann Institute of Science (IL))  
13:50 Mixture Density Networks for tracking in dense environments on ATLAS - Elham E Khoda (University of British Columbia (CA))  
14:10 Deep learning methods to improve Particle Flow reconstruction - Sanmay Ganguly (Weizmann Institute of Science (IL))  
14:30 Machine Learning Based Jet $p_{T}$ Reconstruction in ALICE - Hannah Bossi (Yale University (US))  
14:50 --- Coffee break ---
15:20
Applications - Dan Guest (University of California Irvine (US)) Christine Angela McLean (SUNY Buffalo) (until 17:40) (KC 914)
15:20 Jet substructure tagging and pileup mitigation - Alejandro Gomez Espinosa (ETH Zurich (CH))  
15:40 Machine learning approaches to the identification of jets originating from heavy-flavor quarks. - Philipp Windischhofer (University of Oxford (GB))  
16:00 Searching for long lived particles with a neural-network-based displaced jet tagger - Robert John Bainbridge (Imperial College (GB))  
16:20 Jet or Event? - Physics at Future $e^-e^+$ Colliders - Mr Sijun Xu (Department of Physics, The Hong Kong University of Science and Technology)  
16:40 The Di-Higgs Photography with Deep Neural Networks - Dr Jeong Han Kim (University of Notre Dame)  
17:00 Cornering charming Higgs decays - Mr Joseph Walker (University of Durham )  
17:20 Using machine learning to constrain the Higgs total width - Cristina Ana Mantilla Suarez (Johns Hopkins University (US)) Dylan Sheldon Rankin (Massachusetts Inst. of Technology (US))  
17:40 Closeout - Tilman Plehn