Name: ML4Jets2022
Start: 2022-11-01T08:00:00-04:00
End: 2022-11-04T17:50:00-04:00
Location: Rutgers University

ML4Jets2022

from Tuesday 1 November 2022 (08:00) to Friday 4 November 2022 (17:50)

Monday 31 October 2022
Tuesday 1 November 2022

08:00 Registration
Registration
08:00 - 09:00
Room: Multipurpose Room (aka Livingston Hall)
09:00
09:00 - 10:45
Room: Multipurpose Room (aka Livingston Hall)
Contributions

09:00 Welcome from Local Organizers - David Shih Dean Thu Nguyen (Rutgers University)

09:15 Experimental Opening I - Petar Maksimovic (Johns Hopkins University (US))

09:45 Experimental Opening II - Tobias Golling (Universite de Geneve (CH))

10:15 Theory Opening - Ian James Moult
10:45 Coffee
Coffee
10:45 - 11:15
Room: Multipurpose Room (aka Livingston Hall)
11:15
11:15 - 12:35
Room: Multipurpose Room (aka Livingston Hall)
Contributions

11:15 Does Lorentz-symmetric design boost network performance in jet physics? - Congqiao Li (Peking University (CN))

11:35 Transformer models for heavy flavor jet identification in CMS - Sitian Qian (Peking University (CN))

11:55 A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer - Shikai Qiu (Lawrence Berkeley National Lab. (US))

12:15 Equivariant Neural Networks for Particle Physics: PELICAN - Alexander Bogatskiy (Flatiron Institute, Simons Foundation)
12:35 Lunch
Lunch
12:35 - 14:00
14:00
14:00 - 15:40
Room: Multipurpose Room (aka Livingston Hall)
Contributions

14:00 Symmetries, Safety, and Self-Supervision - Peter Rangi Sorrenson (Universität Heidelberg)

14:20 Transformer Architectures for Quenched Jet Tagging - João Pedro de Arruda Gonçalves (LIP)

14:40 Topological Data Analysis for Collider Events - Tianji Cai (University of California, Santa Barbara)

15:00 Solving Combinatorial Problems in Multijet Signatures Using Machine Learning - Lawrence Lee Jr (University of Tennessee (US))

15:20 Equivariant Point Cloud Generation for Particle Jets - Erik Buhmann (Hamburg University (DE))
15:40 Coffee
Coffee
15:40 - 16:10
Room: Multipurpose Room (aka Livingston Hall)
16:10
16:10 - 17:50
Room: Multipurpose Room (aka Livingston Hall)
Contributions

16:10 Particle Cloud Generation - Raghav Kansal (Univ. of California San Diego (US))

16:30 Point Cloud Generation using Transformer Encoders and Normalising Flows - Benno Kach (Deutsches Elektronen-Synchrotron (DE))

16:50 Conditional generative networks for pure quark and gluon jets - Ayodele Ore

17:10 Modeling Hadronization with Machine Learning - Manuel Szewc

17:30 MadNIS: Neural networks for multi-channel integration - Ramon Winterhalder (UC Louvain)
18:15 Reception
Reception
18:15 - 19:15
Wednesday 2 November 2022
09:00
09:00 - 10:45
Room: Multipurpose Room (aka Livingston Hall)
Contributions

09:00 Introduction to Anomaly Detection - Barry Dillon (University of Heidelberg)

09:25 Results from Unsupervised Machine Learning in an ATLAS Dijet Resonance Search - Julia Lynne Gonski (Columbia University (US))

09:45 A Normalized Autoencoder for LHC triggers - Luigi Favaro

10:05 Robust anomaly detection using NuRD - Abhijith Gandrakota (Fermi National Accelerator Lab. (US))

10:25 Challenges for unsupervised anomaly detection in particle physics - Katherine Fraser (Harvard University)
10:45 Coffee
Coffee
10:45 - 11:15
Room: Multipurpose Room (aka Livingston Hall)
11:15
11:15 - 12:40
Room: Multipurpose Room (aka Livingston Hall)
Contributions

11:15 ML Keynote Talk -- Generative models, manifolds and symmetries: From QFT to molecules - Danilo Rezende

11:55 Panel Discussion - Danilo Rezende David Shih Jesse Thaler (MIT) Nick Dunn (Two Sigma) Savannah Jennifer Thais (Princeton University (US)) Tilman Plehn
12:40 Lunch
Lunch
12:40 - 14:15
14:15
14:15 - 16:00
Room: Multipurpose Room (aka Livingston Hall)
Contributions

14:15 Introduction to Generative Models for Fast Detector Simulation - Claudius Krause (Rutgers University)

14:40 AtlFast3, the new ATLAS fast simulation tool - Michele Faucci Giannelli (INFN e Universita Roma Tor Vergata (IT))

15:00 CaloFlow for CaloChallenge - Ian Pang (Rutgers) Yi En Ian Pang

15:20 Score-based Generative Models for Calorimeter Shower Simulation - Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))

15:40 CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds - Jesse Cresswell (Layer 6 AI)
16:00 Coffee
Coffee
16:00 - 16:30
Room: Multipurpose Room (aka Livingston Hall)
16:30
16:30 - 18:00
Room: Multipurpose Room (aka Livingston Hall)
Contributions

16:30 Generative Models for Fast Simulation of Electromagnetic and Hadronic Showers in Highly Granular Calorimeters - Sascha Daniel Diefenbacher (Hamburg University (DE))

16:50 Fast calorimeter simulation with VQVAE - Chase Owen Shimmin (Yale University (US))

17:10 IEA-GAN: Intra-Event Aware GAN with Relational Reasoning for the Fast Detector Simulation - Hosein Hashemi (LMU Munich) Nikolai Hartmann (LMU Munich)

17:30 Discussion
Thursday 3 November 2022
09:00
09:00 - 10:40
Room: Multipurpose Room (aka Livingston Hall)
Contributions

09:00 Recent ML-usage in searches with boosted jets in CMS - Oz Amram (Johns Hopkins University (US))

09:20 Constituent-Based Top-Quark Tagging with the ATLAS Detector - Kevin Thomas Greif (University of California Irvine (US))

09:40 Adversarial training for b-tagging algorithms in CMS - Annika Stein (Rheinisch Westfaelische Tech. Hoch. (DE)) CMS Collaboration (CMS Experiment, CERN)

10:00 Truth tagging for efficiency parametrization of b-jets using Graph Neural Networks - Krunal Bipin Gedia (ETH Zurich (CH))

10:20 Heterogeneous Graph Representation for Identifying Hadronically Decayed Tau Leptons at the High Luminosity LHC - Andris Huang (University of California-Berkeley) Xiangyang Ju (Lawrence Berkeley National Lab. (US))
09:00 - 10:40
Room: 202ABC
Contributions

09:00 Multi-differential Jet Substructure Measurement in High $Q^{2}$ Deep-Inelastic Scattering with the H1 Detector - Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))

09:20 Machine learning for top physics in CMS - Philip Daniel Keicher (Hamburg University (DE))

09:40 ML Unfolding based on conditional Invertible Neural Networks using iterative training - Mathias Josef Backes (Universität Heidelberg)

10:00 Moment Unfolding using Deep Learning - Krish Desai

10:20 Invertible Networks for the Matrix Element Method - Theo Heimel (Heidelberg University)
10:40 Coffee
Coffee
10:40 - 11:10
Room: Multipurpose Room (aka Livingston Hall)
11:10
11:10 - 12:50
Room: Multipurpose Room (aka Livingston Hall)
Contributions

11:10 Identification of hadronic tau decays using a deep neural network with the CMS experiment at LHC - Mykyta Shchedrolosiev (Deutsches Elektronen-Synchrotron (DE))

11:30 Robust Signal Detection using a Classifier Decorrelated through Optimal Transport (CDOT) - Purvasha Chakravarti (University College London)

11:50 VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger - Cheng-Wei Chiang (National Taiwan University)

12:10 Machine learning based jet and event classification at the Electron-Ion Collider - James Mulligan (University of California, Berkeley (US))

12:30 Search for dimuon events in IceCube using decision trees - Nakul Aggarwal (University of Alberta)
11:10 - 12:50
Room: 202ABC
Contributions

11:10 Constraining quark and gluon jet energy loss distributions in quark-gluon plasma using Bayesian inference - Alexandre Falcão (University of Bergen)

11:30 Estimating Uncertainties for Trained Neural Networks - Sebastian Guido Bieringer (Hamburg University)

11:50 How can Bayesian networks be used for uncertainty quantification in particle physics? - Christina Peters (University of Delaware)

12:10 Using Machine Learning to Improve our Understanding of the Jet Background in Nucleus-Nucleus Collisions. - Tanner Mengel (University of Tennessee)

12:30 Loop Amplitudes from Precision Networks - Tilman Plehn
12:50 Lunch
Lunch
12:50 - 14:00
14:00
14:00 - 15:40
Room: Multipurpose Room (aka Livingston Hall)
Contributions

14:00 CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transporting Adjacent INtervals - Johnny Raine (Universite de Geneve (CH))

14:20 Generative Models for Resonant Anomaly Detection - Elham E Khoda (University of Washington (US))

14:40 HEP-Sim2Real: creating background templates with normalizing flows - Radha Mastandrea (University of California, Berkeley)

15:00 Weakly supervised methods for LHC analyses - Thorben Finke

15:20 Resonant anomaly detection without background sculpting - Manuel Sommerhalder (Hamburg University (DE))
15:40 Coffee
Coffee
15:40 - 16:10
Room: Multipurpose Room (aka Livingston Hall)
16:10
16:10 - 18:10
Room: Multipurpose Room (aka Livingston Hall)
Contributions

16:10 Overview of ML for Gravitational Waves - Eric Anton Moreno (Massachusetts Institute of Technology (US))

16:40 Overview of ML for Gaia - Matthew Buckley

17:10 Overview of ML for Astro/Cosmo - Miles Cranmer (Princeton)

17:40 Overview of ML for Neutrinos - Fernanda Psihas (Fermi National Accelerator Laboratory)
19:00 Conference Dinner
Conference Dinner
19:00 - 22:00
Friday 4 November 2022
09:00
09:00 - 10:40
Room: Multipurpose Room (aka Livingston Hall)
Contributions

09:00 Infra-red and collinear safe Graph Neural Networks - Vishal Singh Ngairangbam

09:20 Resilience of Quark-Gluon Tagging - Lorenz Vogel (ITP, Heidelberg University)

09:40 Boost-Invariant Polynomials: an efficient and interpretable approach to jet tagging - Jose Miguel Munoz Arias (EIA University)

10:00 Learning to Identify Semi-Visible Jets - Taylor James Faucett (University of California, Irvine)

10:20 Feature selection with Distance Correlation - RANIT DAS
09:00 - 10:40
Room: 202ABC
Contributions

09:00 Machine learning for particle flow at CMS - Dylan Sheldon Rankin (Massachusetts Inst. of Technology (US))

09:20 Point Cloud Deep Learning Methods for Pion Reconstruction in the ATLAS Experiment - Piyush Karande (Lawrence Livermore National Laboratory)

09:40 Particle reconstruction in jets with set transformer and hypergraph prediction architectures - Etienne Dreyer (Weizmann Institute of Science (IL)) Nilotpal Kakati (Weizmann Institute of Science (IL))

10:00 Optimal transport solutions for pileup mitigation at hadron colliders - Fabio Iemmi (Chinese Academy of Sciences (CN))

10:20 ν-flows: Conditional neutrino momentum regression - Matthew Leigh (University of Geneva)
10:40 Coffee
Coffee
10:40 - 11:10
Room: Multipurpose Room (aka Livingston Hall)
11:10
11:10 - 12:50
Room: Multipurpose Room (aka Livingston Hall)
Contributions

11:10 Jet tagging with deep sets of subjets - Dimitrios Athanasakos

11:30 Blueprints for Training Information Bottlenecks for Collider Analyses - Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

11:50 Weakly Supervised Learning for Muon Discrimination in Unlabeled Collider Data - Edmund Witkowski (UCI)

12:10 Can You Hear the Shape of a Jet? - Rikab Gambhir (MIT)

12:30 Neural Estimation of Energy Movers Distance - Ouail Kitouni (Massachusetts Inst. of Technology (US))
11:10 - 12:50
Room: 202ABC
Contributions

11:10 Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC - Michael David Sokoloff (University of Cincinnati (US))

11:30 Graph Neural Networks for a Deep-learning based Full Event Interpretation (DFEI) at the LHCb trigger - Julian Garcia Pardinas (Universita & INFN, Milano-Bicocca (IT))

11:50 Likelihood-Free Frequentist Inference for Calorimetric Muon Energy Measurement - Luca Masserano (Carnegie Mellon University)

12:10 A boosted kNN regressor with 66 million parameters - Tommaso Dorigo (Universita e INFN, Padova (IT))

12:30 Jet SIFT-ing - Joel Walker (Sam Houston State University)
12:50 Lunch
Lunch
12:50 - 14:00
14:00
14:00 - 15:20
Room: Multipurpose Room (aka Livingston Hall)
Contributions

14:00 Anomaly detection in a perspective of interdisciplinary research - Taoli Cheng (University of Montreal)

14:20 Optimal Mass Variables for Semivisible Jets - Kevin Pedro (Fermi National Accelerator Lab. (US))

14:40 Neural Embedding: Learning the Embedding of the Manifold of Physics Data - Sang Eon Park (Massachusetts Inst. of Technology (US))

15:00 Hunting for signals using Gaussian Process regression - Abhijith Gandrakota (Fermi National Accelerator Lab. (US))
15:20 Closing - David Shih Manuel Sommerhalder (Hamburg University (DE))
Closing
- David Shih
- Manuel Sommerhalder (Hamburg University (DE))
15:20 - 15:30
Room: Multipurpose Room (aka Livingston Hall)

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