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, July 6, 2021 (8:00 AM) to Thursday, July 8, 2021 (10:00 PM)

Monday, July 5, 2021
Tuesday, July 6, 2021

8:00 AM

8:00 AM - 9:00 AM
9:00 AM Equivariance / Invariance
Equivariance / Invariance
9:00 AM - 10:20 AM
Contributions

9:00 AM SO(3)-equivariant Neural Network for b-tagging - Ema Catalina Smith

9:20 AM Equivariant energy flow networks for jet tagging - Ayodele Ore (The University of Melbourne)

9:40 AM SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention - Michael James Fenton (University of California Irvine (US))

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

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

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

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

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

11:40 AM A new approach to unsupervised learning in jet physics - Peter Rangi Sorrenson (Universität Heidelberg)
2:00 PM Overdensity Methods
Overdensity Methods
2:00 PM - 3:20 PM
Contributions

2:00 PM High-dimensional Anomaly Detection with Radiative Return in e+e- Collisions - Julia Lynne Gonski (Columbia University (US))

2:20 PM 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)

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

3:00 PM Anomaly Detection in the Copula Space - Tommaso Dorigo (Universita e INFN, Padova (IT))
2:00 PM - 6:00 PM
Contributions

2:00 PM Invertible Neural Networks beyond Particle Physics - Lynton Ardizzone (Heidelberg)

2:40 PM Combining Neural Network predictions with Hypothesis Testing for discovery in the LHC - Michael Soughton (University of Sussex)

3:00 PM Invertible Networks or Partons to Detector and Back Again - Anja Butter

3:20 PM Towards a new generation of PDFs using ML - Roy Stegeman (University of Milan)

3:40 PM Emerging techniques for sampling, searching, and summing over the combinatorially large space of shower histories - Sebastian Macaluso (New York University)

4:00 PM Tuning the parton shower parameters with the marginal likelihood - Matthew Drnevich (New York University (US))

4:20 PM Computing the exact optimal classifier for Ginkgo jets - Lauren Greenspan (NYU)

4:40 PM Parametrized classifiers for optimal EFT sensitivity - Alfredo Glioti (EPFL - Ecole Polytechnique Federale Lausanne (CH))

5:00 PM Measuring QCD Splittings with Invertible Networks - Theo Heimel (Universität Heidelberg)

5:20 PM 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

5:40 PM Parameter Inference from Event Ensembles and the Top-Quark Mass - Katherine Fraser (Harvard University)
3:20 PM Latent Space Anomaly Detection
Latent Space Anomaly Detection
3:20 PM - 4:40 PM
Contributions

3:20 PM Jet Metrics and Autoencoders - Rashmish Mishra (Harvard University)

3:40 PM Detecting Anomalous jets with Graph Neural Networks - Vishal Singh Ngairangbam (Physical Research Laboratory)

4:00 PM Review of the Dark Machine Anomaly Score Challenge I - Joe Davies

4:20 PM Review of the Dark Machine Anomaly Score Challenge II - Bryan Ostdiek (Harvard University)
4:40 PM Data Space Searches with Autoencoders
Data Space Searches with Autoencoders
4:40 PM - 6:00 PM
Contributions

4:40 PM Boosting new physics sensitivity with Variational Autoencoders - Kinga Anna Wozniak (University of Vienna (AT))

5:00 PM Autoencoders for unsupervised anomaly detection in high energy physics - Thorben Finke

5:20 PM Recognizing hadronic SUEP at the LHC with Unsupervised Machine Learning - Jared Barron (University of Toronto)

5:40 PM Classifier-based Anomalous Jet Tagging - Taoli Cheng (University of Montreal)
8:00 PM
8:00 PM - 9:20 PM
Contributions

8:00 PM Latent Space Refinement for Deep Generative Models - Ramon Winterhalder (ITP Heidelberg)

8:20 PM Compressing PDF sets using Generative Adversarial Networks - Tanjona Radonirina Rabemananjara (INFN - National Institute for Nuclear Physics)

8:40 PM Exploring phase space with Neural Importance Sampling - Timo Janßen (Georg-August-Universität Göttingen)

9:00 PM Lorentz Group Equivariant Autoencoder - Zichun Hao (Univ. of California San Diego (US))
Wednesday, July 7, 2021
9:00 AM
9:00 AM - 12:00 PM
Contributions

9:00 AM Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks - Jack Araz (IPPP - Durham University)

9:20 AM Pushing the limit of jet tagging with graph neural networks - Huilin Qu (CERN)

9:40 AM Jet tagging in the Lund plane with graph networks - Frederic Alexandre Dreyer (University of Oxford)

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

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

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

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

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

11:40 AM 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))
2:00 PM Calibration
Calibration
2:00 PM - 3:20 PM
Contributions

2:00 PM Machine learning based Particle Flow algorithm and application of super-resolution techniques - Sanmay Ganguly (Weizmann Institute of Science (IL))

2:20 PM Using Machine Learning for Heavy-Ion Jet $p_{\rm T}$ Reconstruction in ALICE - Hannah Bossi (Yale University (US))

2:40 PM Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics - Rikab Gambhir (MIT)

3:00 PM ML in jet physics beyond classification - Loukas Gouskos (CERN)
Detector Simulation
Detector Simulation
2:00 PM - 3:40 PM
Contributions

2:00 PM CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows - Claudius Krause (Rutgers University)

2:20 PM Multi-detector geomotery modeling and Geant4 Integration - Dalila Salamani (CERN)

2:40 PM Angular Conditioning of Deep Generative Models for Fast Simulation of High Granularity Calorimeters - Peter McKeown (Deutsches Elektronen-Synchrotron DESY)

3:00 PM Fast and Accurate Electromagnetic and Hadronic Showers from Generative Models - Engin Eren (Deutsches Elektronen-Synchrotron DESY)

3:20 PM AtlFast3: The next generation of fast simulation in ATLAS - Joshua Falco Beirer (CERN, Georg-August-Universitaet Goettingen (DE))
3:20 PM Regression
Regression
3:20 PM - 4:40 PM
Contributions

3:20 PM Pileup mitigation in CMS - Benedikt Maier

3:40 PM Lightweight Jet Reconstruction as an Object Detection Task - Adrian Alan Pol (CERN)

4:00 PM Measurement of Muon Energy From Radiative Losses in a Granular Calorimeter - Giles Chatham Strong (Universita e INFN, Padova (IT))

4:20 PM Deep learning jet modifications in heavy-ion collisions - Yilun Du (University of Bergen)
3:40 PM Event and Jet Generation
Event and Jet Generation
3:40 PM - 6:00 PM
Contributions

3:40 PM Fast Simulation of Jets with VAEs - Mary Touranakou (National and Kapodistrian University of Athens (GR))

4:00 PM 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))

4:20 PM Particle Cloud Generation with Message Passing GANs - Raghav Kansal (Univ. of California San Diego (US))

4:40 PM White Box AI for parton shower development - Felix Ringer (Lawrence Berkeley National Laboratory)

5:00 PM How to GAN Event Unweighting - Mathias Backes (Uni Heidelberg)

5:20 PM Sparse Data Generation with Convolutional VAE - Breno Orzari (UNESP - Universidade Estadual Paulista (BR))

5:40 PM Super-Resolution for QCD and Top Jets - Lukas Blecher (Universität Heidelberg)
4:40 PM Fast Inference
Fast Inference
4:40 PM - 5:40 PM
Contributions

4:40 PM Matrix Element Calculations on the GPU - Joshua Isaacson (Fermilab)

5:00 PM Jet Identification in L1 Trigger at HL-LHC based on DNN implementation on FPGA - Andre Sznajder (UERJ (Brazil))

5:20 PM OnlineFlow: Trigger Free Analysis Using Online Learned Generative Models - Sascha Daniel Diefenbacher (Hamburg University (DE))
8:00 PM
8:00 PM - 9:40 PM
Contributions

8:00 PM Implementation of Jupyter Notebooks into The Reproducible Open Benchmarks for Data Analysis Platform (ROB) - Aaron Wang Heiko Mueller

8:20 PM Shared Data and Algorithms for Deep Learning in Fundamental Physics - William Korcari (Hamburg University (DE))

8:40 PM Introduction to Anomaly Detection Challenge - Katya Govorkova (CERN)

9:00 PM Calorimeter Simulation Challenge Proposal - David Shih (Rutgers University)
Thursday, July 8, 2021
9:00 AM Data Structure
Data Structure
9:00 AM - 10:20 AM
Contributions

9:00 AM Learning Symmetries and Conserved Quantities of Physical Systems - Sven Krippendorf

9:20 AM Bump Hunting in Latent Space - Aleks Smolkovic (Jozef Stefan Institute Ljubljana)

9:40 AM Symmetry Discovery with Deep Learning - Krish Desai (University of California, Berkeley)

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

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

10:40 AM 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 AM Better latent spaces for better autoencoders - Barry Dillon (University of Heidelberg)

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

11:40 AM Jet Topology - Sijun Xu (Hong Kong University of Science and Technology)
2:00 PM Intro
Intro
2:00 PM - 2:40 PM
Contributions

2:00 PM Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes - Manuel Haußmann (Universität Heidelberg)
2:40 PM Uncertainties
Uncertainties
2:40 PM - 4:00 PM
Contributions

2:40 PM Uncertainties Associated with GAN Generated Datasets - Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

3:00 PM Generative Networks with Uncertainties - Michel Luchmann Tilman Plehn

3:20 PM Uncertainty Aware Learning for High Energy Physics - Aishik Ghosh (University of California Irvine (US))

3:40 PM Amplifying Statistics with Generative Models - Sebastian Bieringer
4:00 PM Information Content
Information Content
4:00 PM - 4:40 PM
Contributions

4:00 PM Safety of Quark/Gluon Jet Classification - Alexis Romero

4:20 PM Thoughts on the expressive power and inductive bias of DeepSets and Tree-Based models - Kyle Stuart Cranmer (New York University (US))
4:40 PM Constructing Observables
Constructing Observables
4:40 PM - 6:00 PM
Contributions

4:40 PM Explainable AI for ML Jet Taggers - Christine Angela McLean (SUNY Buffalo)

5:00 PM Bayesian Inference in for Four Tops at the LHC - Ezequiel Alvarez de los Alvarez de San Luis

5:20 PM Spectral Clustering for Jet Formation - Henry Day-Hall (University of Southampton)

5:40 PM Deep-Learned Event Variables - Doojin Kim (Texas A & M University (US))
8:00 PM
8:00 PM - 9:45 PM
Contributions

8:00 PM ML in Cosmological Simulations - Annalisa Pillepich

8:40 PM Conditional invertible neural networks to probe cosmic-ray sources - Josina Schulte (RWTH Aachen University)

9:00 PM Via Machinae - Matthew Buckley (Rutgers University)

9:20 PM Synergies between Quantum Computing and Machine Learning - Michael Spannowsky (University of Durham (GB))
9:45 PM 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))
9:45 PM - 9:50 PM

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