Name: ML4Jets2021
Start: 2021-07-06T08:00:00+02:00
End: 2021-07-08T22:00:00+02:00
Location: No location set

ML4Jets2021

from Tuesday 6 July 2021 (08:00) to Thursday 8 July 2021 (22:00)

Monday 5 July 2021
Tuesday 6 July 2021

08:00

08:00 - 09:00
09:00 Equivariance / Invariance
Equivariance / Invariance
09:00 - 10:20
Contributions

09:00 SO(3)-equivariant Neural Network for b-tagging - Ema Catalina Smith

09:20 Equivariant energy flow networks for jet tagging - Ayodele Ore (The University of Melbourne)

09:40 SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention - Michael James Fenton (University of California Irvine (US))

10:00 Particle Convolution for Jets - Chase Owen Shimmin (Yale University (US))
10:20 New Strategies or Representations
New Strategies or Representations
10:20 - 12:00
Contributions

10:20 Linearized Optimal Transport for Jet Physics - Tianji Cai (Department of Physics, University of California, Santa Barbara)

10:40 Supervised Attention for Jet Classification - Jonathan Shlomi (Weizmann Institute of Science (IL))

11:00 The information content of quenched jets - James Mulligan (University of California, Berkeley (US))

11:20 Identifying Heavy-Flavor Jets Using Vectors of Locally Aggregated Descriptors - Jitka Mrazkova

11:40 A new approach to unsupervised learning in jet physics - Peter Rangi Sorrenson (Universität Heidelberg)
14:00 Overdensity Methods
Overdensity Methods
14:00 - 15:20
Contributions

14:00 High-dimensional Anomaly Detection with Radiative Return in e+e- Collisions - Julia Lynne Gonski (Columbia University (US))

14:20 CATHODE part 1: introducing a new model-agnostic search strategy for resonant new physics at the LHC - Anna Hallin (Test IDP - Rutgers, The State University of New Jerse)

14:40 CATHODE part 2: robustness and comparison to other methods - Manuel Sommerhalder (Hamburg University (DE))

15:00 Anomaly Detection in the Copula Space - Tommaso Dorigo (Universita e INFN, Padova (IT))
14:00 - 18:00
Contributions

14:00 Invertible Neural Networks beyond Particle Physics - Lynton Ardizzone (Heidelberg)

14:40 Combining Neural Network predictions with Hypothesis Testing for discovery in the LHC - Michael Soughton (University of Sussex)

15:00 Invertible Networks or Partons to Detector and Back Again - Anja Butter

15:20 Towards a new generation of PDFs using ML - Roy Stegeman (University of Milan)

15:40 Emerging techniques for sampling, searching, and summing over the combinatorially large space of shower histories - Sebastian Macaluso (New York University)

16:00 Tuning the parton shower parameters with the marginal likelihood - Matthew Drnevich (New York University (US))

16:20 Computing the exact optimal classifier for Ginkgo jets - Lauren Greenspan (NYU)

16:40 Parametrized classifiers for optimal EFT sensitivity - Alfredo Glioti (EPFL - Ecole Polytechnique Federale Lausanne (CH))

17:00 Measuring QCD Splittings with Invertible Networks - Theo Heimel (Universität Heidelberg)

17:20 Jet-based TMD measurements with H1 data, unfolded using machine-learning techniques - Miguel Ignacio Arratia Munoz (Lawrence Berkeley National Lab. (US)) Miguel Ignacio Arratia Munoz

17:40 Parameter Inference from Event Ensembles and the Top-Quark Mass - Katherine Fraser (Harvard University)
15:20 Latent Space Anomaly Detection
Latent Space Anomaly Detection
15:20 - 16:40
Contributions

15:20 Jet Metrics and Autoencoders - Rashmish Mishra (Harvard University)

15:40 Detecting Anomalous jets with Graph Neural Networks - Vishal Singh Ngairangbam (Physical Research Laboratory)

16:00 Review of the Dark Machine Anomaly Score Challenge I - Joe Davies

16:20 Review of the Dark Machine Anomaly Score Challenge II - Bryan Ostdiek (Harvard University)
16:40 Data Space Searches with Autoencoders
Data Space Searches with Autoencoders
16:40 - 18:00
Contributions

16:40 Boosting new physics sensitivity with Variational Autoencoders - Kinga Anna Wozniak (University of Vienna (AT))

17:00 Autoencoders for unsupervised anomaly detection in high energy physics - Thorben Finke

17:20 Recognizing hadronic SUEP at the LHC with Unsupervised Machine Learning - Jared Barron (University of Toronto)

17:40 Classifier-based Anomalous Jet Tagging - Taoli Cheng (University of Montreal)
20:00
20:00 - 21:20
Contributions

20:00 Latent Space Refinement for Deep Generative Models - Ramon Winterhalder (Universität Heidelberg)

20:20 Compressing PDF sets using Generative Adversarial Networks - Tanjona Radonirina Rabemananjara (INFN - National Institute for Nuclear Physics)

20:40 Exploring phase space with Neural Importance Sampling - Timo Janßen (Georg-August-Universität Göttingen)

21:00 Lorentz Group Equivariant Autoencoder - Zichun Hao (Univ. of California San Diego (US))
Wednesday 7 July 2021
09:00
09:00 - 12:00
Contributions

09:00 Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks - Jack Araz (IPPP - Durham University)

09:20 Pushing the limit of jet tagging with graph neural networks - Huilin Qu (CERN)

09:40 Jet tagging in the Lund plane with graph networks - Frederic Alexandre Dreyer (University of Oxford)

10:00 Higgs tagging with the Lund jet plane - Charanjit Kaur Khosa (University of Genova and INFN Genova)

10:20 Identifying the Quantum Properties of Hadronic Resonances using Machine Learning - Jakub Filipek (University of Washington)

10:40 Morphology for Jet Classification - Sung Hak Lim (Rutgers University)

11:00 Boosted jet tagging in CMS - Congqiao Li (Peking University (CN))

11:20 A $W^\pm$ polarization analyzer from Deep Neural Networks - Taegyun Kim (University of Notre Dame)

11:40 Testing Universality in various Monte Carlo Generators in Deep Learning with Application in Higgs-boson pair searches in 2HDM - Yi-Lun Chung (National Tsing Hua University (TW))
14:00 Calibration
Calibration
14:00 - 15:20
Contributions

14:00 Machine learning based Particle Flow algorithm and application of super-resolution techniques - Sanmay Ganguly (Weizmann Institute of Science (IL))

14:20 Using Machine Learning for Heavy-Ion Jet $p_{\rm T}$ Reconstruction in ALICE - Hannah Bossi (Yale University (US))

14:40 Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics - Rikab Gambhir (MIT)

15:00 ML in jet physics beyond classification - Loukas Gouskos (CERN)
Detector Simulation
Detector Simulation
14:00 - 15:40
Contributions

14:00 CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows - Claudius Krause (Rutgers University)

14:20 Multi-detector geomotery modeling and Geant4 Integration - Dalila Salamani (CERN)

14:40 Angular Conditioning of Deep Generative Models for Fast Simulation of High Granularity Calorimeters - Peter McKeown (Deutsches Elektronen-Synchrotron DESY)

15:00 Fast and Accurate Electromagnetic and Hadronic Showers from Generative Models - Engin Eren (Deutsches Elektronen-Synchrotron DESY)

15:20 AtlFast3: The next generation of fast simulation in ATLAS - Joshua Falco Beirer (CERN, Georg-August-Universitaet Goettingen (DE))
15:20 Regression
Regression
15:20 - 16:40
Contributions

15:20 Pileup mitigation in CMS - Benedikt Maier

15:40 Lightweight Jet Reconstruction as an Object Detection Task - Adrian Alan Pol (CERN)

16:00 Measurement of Muon Energy From Radiative Losses in a Granular Calorimeter - Giles Chatham Strong (Universita e INFN, Padova (IT))

16:20 Deep learning jet modifications in heavy-ion collisions - Yilun Du (University of Bergen)
15:40 Event and Jet Generation
Event and Jet Generation
15:40 - 18:00
Contributions

15:40 Fast Simulation of Jets with VAEs - Mary Touranakou (National and Kapodistrian University of Athens (GR))

16:00 Foundations of a Fast, Data-Driven, Machine-Learned Simulator - Jessica N. Howard (Department of Physics & Astronomy, UC Irvine) Jessica N. Howard (University of California Irvine (US))

16:20 Particle Cloud Generation with Message Passing GANs - Raghav Kansal (Univ. of California San Diego (US))

16:40 White Box AI for parton shower development - Felix Ringer (Lawrence Berkeley National Laboratory)

17:00 How to GAN Event Unweighting - Mathias Backes (Uni Heidelberg)

17:20 Sparse Data Generation with Convolutional VAE - Breno Orzari (UNESP - Universidade Estadual Paulista (BR))

17:40 Super-Resolution for QCD and Top Jets - Lukas Blecher (Universität Heidelberg)
16:40 Fast Inference
Fast Inference
16:40 - 17:40
Contributions

16:40 Matrix Element Calculations on the GPU - Joshua Isaacson (Fermilab)

17:00 Jet Identification in L1 Trigger at HL-LHC based on DNN implementation on FPGA - Andre Sznajder (UERJ (Brazil))

17:20 OnlineFlow: Trigger Free Analysis Using Online Learned Generative Models - Sascha Daniel Diefenbacher (Hamburg University (DE))
20:00
20:00 - 21:40
Contributions

20:00 Implementation of Jupyter Notebooks into The Reproducible Open Benchmarks for Data Analysis Platform (ROB) - Aaron Wang Heiko Mueller

20:20 Shared Data and Algorithms for Deep Learning in Fundamental Physics - William Korcari (Hamburg University (DE))

20:40 Introduction to Anomaly Detection Challenge - Katya Govorkova (CERN)

21:00 Calorimeter Simulation Challenge Proposal - David Shih (Rutgers University)
Thursday 8 July 2021
09:00 Data Structure
Data Structure
09:00 - 10:20
Contributions

09:00 Learning Symmetries and Conserved Quantities of Physical Systems - Sven Krippendorf

09:20 Bump Hunting in Latent Space - Aleks Smolkovic (Jozef Stefan Institute Ljubljana)

09:40 Symmetry Discovery with Deep Learning - Krish Desai (University of California, Berkeley)

10:00 The Blessing of Dimensionality: Dimensionality Estimation for Event Clustering - Malte Jacobsen (Hamburg University (DE))
10:20 Latent Space Exploration
Latent Space Exploration
10:20 - 12:00
Contributions

10:20 Detecting hidden patterns in jet substructure with probabilistic models - Darius Faroughy (University of Zurich)

10:40 Attention and Dynamic Graph Convolution Neural Network in the context of classifying ttH(bb) vs. tt(bb) in the semi-leptonic top quark pair decay channel - Christina Reissel (ETH Zurich (CH))

11:00 Better latent spaces for better autoencoders - Barry Dillon (University of Heidelberg)

11:20 Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network - Erik Buhmann (Hamburg University (DE))

11:40 Jet Topology - Sijun Xu (Hong Kong University of Science and Technology)
14:00 Intro
Intro
14:00 - 14:40
Contributions

14:00 Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes - Manuel Haußmann (Universität Heidelberg)
14:40 Uncertainties
Uncertainties
14:40 - 16:00
Contributions

14:40 Uncertainties Associated with GAN Generated Datasets - Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

15:00 Generative Networks with Uncertainties - Michel Luchmann Tilman Plehn

15:20 Uncertainty Aware Learning for High Energy Physics - Aishik Ghosh (University of California Irvine (US))

15:40 Amplifying Statistics with Generative Models - Sebastian Bieringer
16:00 Information Content
Information Content
16:00 - 16:40
Contributions

16:00 Safety of Quark/Gluon Jet Classification - Alexis Romero

16:20 Thoughts on the expressive power and inductive bias of DeepSets and Tree-Based models - Kyle Stuart Cranmer (New York University (US))
16:40 Constructing Observables
Constructing Observables
16:40 - 18:00
Contributions

16:40 Explainable AI for ML Jet Taggers - Christine Angela McLean (SUNY Buffalo)

17:00 Bayesian Inference in for Four Tops at the LHC - Ezequiel Alvarez de los Alvarez de San Luis

17:20 Spectral Clustering for Jet Formation - Henry Day-Hall (University of Southampton)

17:40 Deep-Learned Event Variables - Doojin Kim (Texas A & M University (US))
20:00
20:00 - 21:45
Contributions

20:00 ML in Cosmological Simulations - Annalisa Pillepich

20:40 Conditional invertible neural networks to probe cosmic-ray sources - Josina Schulte (RWTH Aachen University)

21:00 Via Machinae - Matthew Buckley (Rutgers University)

21:20 Synergies between Quantum Computing and Machine Learning - Michael Spannowsky (University of Durham (GB))
21:45 Closeout and ML4Jets2022 - David Shih (Rutgers University) Tilman Plehn Ben Nachman (Lawrence Berkeley National Lab. (US))
Closeout and ML4Jets2022
- David Shih (Rutgers University)
- Tilman Plehn
- Ben Nachman (Lawrence Berkeley National Lab. (US))
21:45 - 21:50

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