Name: ML4Jets2020
Start: 2020-01-15T08:30:00-05:00
End: 2020-01-17T19:00:00-05:00
Location: Kimmel Center for University Life

ML4Jets2020

from Wednesday 15 January 2020 (08:30) to Friday 17 January 2020 (19:00)

Monday 13 January 2020
Tuesday 14 January 2020
Wednesday 15 January 2020

08:30

08:30 - 09:00
Room: KC 802
09:00
09:00 - 10:40
Room: KC 802
Contributions

09:00 Welcome and Logistics - Kyle Stuart Cranmer (New York University (US)) Sebastian Macaluso (New York University)

09:10 Theory Introduction (20'+10') - 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
Coffee break
10:40 - 11:10
Room: KC 802
11:10
11:10 - 12:50
Room: KC 802
Contributions

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 - Yang-Ting Chien (Stony Brook University)

12:30 CLARIANT: Covariant Lorentz Group Architecture for Artificial Neural Networks - Alexander Bogatskiy
12:50 Lunch
Lunch
12:50 - 14:10
14:10
14:10 - 17:45
Room: KC 802
Contributions

14:10 Introduction - Jana Schaarschmidt (University of Washington (US)) Martin Erdmann (Rheinisch Westfaelische Tech. Hoch. (DE))

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 - Claudius Krause (Fermilab) Christina Gao

16:25 Lund jet images from generative and cycle-consistent adversarial networks - Frederic Alexandre Dreyer (Oxford) Stefano Carrazza (CERN)

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

19:00 - 21:00
Room: Bier Haus
Thursday 16 January 2020
09:00
09:00 - 11:00
Room: KC 802
Contributions

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 - Tianji Cai (University of California, Santa Barbara)
11:00 Coffee break
Coffee break
11:00 - 11:30
Room: KC 802
11:30
11:30 - 13:00
Room: KC 802
Contributions

11:30 Optimal Transport - Jonathan Niles-Weed (NYU)

12:00 Beyond monotonic, autoregressive sequence modeling - Kyunghyun Cho

12:30 Guided discussion
13:00 Lunch
Lunch
13:00 - 14:20
14:20
14:20 - 18:10
Room: KC 802
Contributions

14:30 LHCO Introduction and overview - David Shih (Rutgers University) Gregor Kasieczka (Hamburg University (DE))

14:50 Unsupervised new physics searches with data-driven inference - 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)

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 - Ben Nachman (Lawrence Berkeley National Lab. (US)) Gregor Kasieczka (Hamburg University (DE)) David Shih (Rutgers University)
Friday 17 January 2020
09:00
09:00 - 11:50
Room: KC 914
Contributions

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)

10:50 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 - Sebastian Macaluso (New York University) Heiko Mueller
11:50 Lunch
Lunch
11:50 - 13:00
13:00
13:00 - 14:50
Room: KC 914
Contributions

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
Coffee break
14:50 - 15:20
Room: KC 914
15:20
15:20 - 17:40
Room: KC 914
Contributions

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 - Sijun Xu (Department of Physics, The Hong Kong University of Science and Technology)

16:40 The Di-Higgs Photography with Deep Neural Networks - Jeong Han Kim (University of Notre Dame)

17:00 Cornering charming Higgs decays - Joseph Walker (University of Durham )

17:20 Using machine learning to constrain the Higgs total width - Dylan Sheldon Rankin (Massachusetts Inst. of Technology (US)) Cristina Ana Mantilla Suarez (Johns Hopkins University (US))
17:40 Closeout - Tilman Plehn
Closeout
- Tilman Plehn
17:40 - 17:50
Room: KC 802

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