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

ML4Jets2022

from Tuesday, November 1, 2022 (8:00 AM) to Friday, November 4, 2022 (5:50 PM)

Monday, October 31, 2022
Tuesday, November 1, 2022

8:00 AM Registration
Registration
8:00 AM - 9:00 AM
Room: Multipurpose Room (aka Livingston Hall)
9:00 AM
9:00 AM - 10:45 AM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

9:00 AM Welcome from Local Organizers - David Shih Dean Thu Nguyen (Rutgers University)

9:15 AM Experimental Opening I - Petar Maksimovic (Johns Hopkins University (US))

9:45 AM Experimental Opening II - Tobias Golling (Universite de Geneve (CH))

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

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

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

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

12:15 PM Equivariant Neural Networks for Particle Physics: PELICAN - Alexander Bogatskiy (Flatiron Institute, Simons Foundation)
12:35 PM Lunch
Lunch
12:35 PM - 2:00 PM
2:00 PM
2:00 PM - 3:40 PM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

2:00 PM Symmetries, Safety, and Self-Supervision - Peter Rangi Sorrenson (Universität Heidelberg)

2:20 PM Transformer Architectures for Quenched Jet Tagging - João Pedro de Arruda Gonçalves (LIP)

2:40 PM Topological Data Analysis for Collider Events - Tianji Cai (University of California, Santa Barbara)

3:00 PM Solving Combinatorial Problems in Multijet Signatures Using Machine Learning - Lawrence Lee Jr (University of Tennessee (US))

3:20 PM Equivariant Point Cloud Generation for Particle Jets - Erik Buhmann (Hamburg University (DE))
3:40 PM Coffee
Coffee
3:40 PM - 4:10 PM
Room: Multipurpose Room (aka Livingston Hall)
4:10 PM
4:10 PM - 5:50 PM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

4:10 PM Particle Cloud Generation - Raghav Kansal (Univ. of California San Diego (US))

4:30 PM Point Cloud Generation using Transformer Encoders and Normalising Flows - Benno Kach (Deutsches Elektronen-Synchrotron (DE))

4:50 PM Conditional generative networks for pure quark and gluon jets - Ayodele Ore

5:10 PM Modeling Hadronization with Machine Learning - Manuel Szewc

5:30 PM MadNIS: Neural networks for multi-channel integration - Ramon Winterhalder (UC Louvain)
6:15 PM Reception
Reception
6:15 PM - 7:15 PM
Wednesday, November 2, 2022
9:00 AM
9:00 AM - 10:45 AM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

9:00 AM Introduction to Anomaly Detection - Barry Dillon (University of Heidelberg)

9:25 AM Results from Unsupervised Machine Learning in an ATLAS Dijet Resonance Search - Julia Lynne Gonski (Columbia University (US))

9:45 AM A Normalized Autoencoder for LHC triggers - Luigi Favaro

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

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

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

11:55 AM Panel Discussion - Danilo Rezende David Shih Jesse Thaler (MIT) Nick Dunn (Two Sigma) Savannah Jennifer Thais (Princeton University (US)) Tilman Plehn
12:40 PM Lunch
Lunch
12:40 PM - 2:15 PM
2:15 PM
2:15 PM - 4:00 PM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

2:15 PM Introduction to Generative Models for Fast Detector Simulation - Claudius Krause (Rutgers University)

2:40 PM AtlFast3, the new ATLAS fast simulation tool - Michele Faucci Giannelli (INFN e Universita Roma Tor Vergata (IT))

3:00 PM CaloFlow for CaloChallenge - Ian Pang (Rutgers) Yi En Ian Pang

3:20 PM Score-based Generative Models for Calorimeter Shower Simulation - Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))

3:40 PM CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds - Jesse Cresswell (Layer 6 AI)
4:00 PM Coffee
Coffee
4:00 PM - 4:30 PM
Room: Multipurpose Room (aka Livingston Hall)
4:30 PM
4:30 PM - 6:00 PM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

4:30 PM Generative Models for Fast Simulation of Electromagnetic and Hadronic Showers in Highly Granular Calorimeters - Sascha Daniel Diefenbacher (Hamburg University (DE))

4:50 PM Fast calorimeter simulation with VQVAE - Chase Owen Shimmin (Yale University (US))

5:10 PM IEA-GAN: Intra-Event Aware GAN with Relational Reasoning for the Fast Detector Simulation - Hosein Hashemi (LMU Munich) Nikolai Hartmann (LMU Munich)

5:30 PM Discussion
Thursday, November 3, 2022
9:00 AM
9:00 AM - 10:40 AM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

9:00 AM Recent ML-usage in searches with boosted jets in CMS - Oz Amram (Johns Hopkins University (US))

9:20 AM Constituent-Based Top-Quark Tagging with the ATLAS Detector - Kevin Thomas Greif (University of California Irvine (US))

9:40 AM Adversarial training for b-tagging algorithms in CMS - Annika Stein (Rheinisch Westfaelische Tech. Hoch. (DE)) CMS Collaboration (CMS Experiment, CERN)

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

10:20 AM 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))
9:00 AM - 10:40 AM
Room: 202ABC
Contributions

9:00 AM Multi-differential Jet Substructure Measurement in High $Q^{2}$ Deep-Inelastic Scattering with the H1 Detector - Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))

9:20 AM Machine learning for top physics in CMS - Philip Daniel Keicher (Hamburg University (DE))

9:40 AM ML Unfolding based on conditional Invertible Neural Networks using iterative training - Mathias Josef Backes (Universität Heidelberg)

10:00 AM Moment Unfolding using Deep Learning - Krish Desai

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

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

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

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

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

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

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

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

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

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

12:30 PM Loop Amplitudes from Precision Networks - Tilman Plehn
12:50 PM Lunch
Lunch
12:50 PM - 2:00 PM
2:00 PM
2:00 PM - 3:40 PM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

2:00 PM CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transporting Adjacent INtervals - Johnny Raine (Universite de Geneve (CH))

2:20 PM Generative Models for Resonant Anomaly Detection - Elham E Khoda (University of Washington (US))

2:40 PM HEP-Sim2Real: creating background templates with normalizing flows - Radha Mastandrea (University of California, Berkeley)

3:00 PM Weakly supervised methods for LHC analyses - Thorben Finke

3:20 PM Resonant anomaly detection without background sculpting - Manuel Sommerhalder (Hamburg University (DE))
3:40 PM Coffee
Coffee
3:40 PM - 4:10 PM
Room: Multipurpose Room (aka Livingston Hall)
4:10 PM
4:10 PM - 6:10 PM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

4:10 PM Overview of ML for Gravitational Waves - Eric Anton Moreno (Massachusetts Institute of Technology (US))

4:40 PM Overview of ML for Gaia - Matthew Buckley

5:10 PM Overview of ML for Astro/Cosmo - Miles Cranmer (Princeton)

5:40 PM Overview of ML for Neutrinos - Fernanda Psihas (Fermi National Accelerator Laboratory)
7:00 PM Conference Dinner
Conference Dinner
7:00 PM - 10:00 PM
Friday, November 4, 2022
9:00 AM
9:00 AM - 10:40 AM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

9:00 AM Infra-red and collinear safe Graph Neural Networks - Vishal Singh Ngairangbam

9:20 AM Resilience of Quark-Gluon Tagging - Lorenz Vogel (ITP, Heidelberg University)

9:40 AM Boost-Invariant Polynomials: an efficient and interpretable approach to jet tagging - Jose Miguel Munoz Arias (EIA University)

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

10:20 AM Feature selection with Distance Correlation - RANIT DAS
9:00 AM - 10:40 AM
Room: 202ABC
Contributions

9:00 AM Machine learning for particle flow at CMS - Dylan Sheldon Rankin (Massachusetts Inst. of Technology (US))

9:20 AM Point Cloud Deep Learning Methods for Pion Reconstruction in the ATLAS Experiment - Piyush Karande (Lawrence Livermore National Laboratory)

9:40 AM 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 AM Optimal transport solutions for pileup mitigation at hadron colliders - Fabio Iemmi (Chinese Academy of Sciences (CN))

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

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

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

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

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

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

11:10 AM 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 AM 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 AM Likelihood-Free Frequentist Inference for Calorimetric Muon Energy Measurement - Luca Masserano (Carnegie Mellon University)

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

12:30 PM Jet SIFT-ing - Joel Walker (Sam Houston State University)
12:50 PM Lunch
Lunch
12:50 PM - 2:00 PM
2:00 PM
2:00 PM - 3:20 PM
Room: Multipurpose Room (aka Livingston Hall)
Contributions

2:00 PM Anomaly detection in a perspective of interdisciplinary research - Taoli Cheng (University of Montreal)

2:20 PM Optimal Mass Variables for Semivisible Jets - Kevin Pedro (Fermi National Accelerator Lab. (US))

2:40 PM Neural Embedding: Learning the Embedding of the Manifold of Physics Data - Sang Eon Park (Massachusetts Inst. of Technology (US))

3:00 PM Hunting for signals using Gaussian Process regression - Abhijith Gandrakota (Fermi National Accelerator Lab. (US))
3:20 PM Closing - David Shih Manuel Sommerhalder (Hamburg University (DE))
Closing
- David Shih
- Manuel Sommerhalder (Hamburg University (DE))
3:20 PM - 3:30 PM
Room: Multipurpose Room (aka Livingston Hall)

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