Name: 4th Inter-experiment Machine Learning Workshop
Start: 2020-10-19T09:00:00+02:00
End: 2020-10-23T18:10:00+02:00
Location: No location set

4th Inter-experiment Machine Learning Workshop

from Monday, 19 October 2020 (09:00) to Friday, 23 October 2020 (18:10)

Monday, 19 October 2020

14:00 hls4ml tutorial
hls4ml tutorial
14:00 - 17:00
Tuesday, 20 October 2020
10:00 Tuesday morning
Tuesday morning
10:00 - 12:00
Contributions

10:00 Introduction - Andrea Wulzer (CERN and EPFL) David Rousseau (LAL-Orsay, FR) Gian Michele Innocenti (CERN) Lorenzo Moneta (CERN) Pietro Vischia (Universite Catholique de Louvain (UCL) (BE)) Riccardo Torre (CERN) Simon Akar (University of Cincinnati (US))

10:15 CERN Knowledge Transfer - Han Hubert Dols (CERN) Nick Ziogas (CERN)

10:25 Machine Learning in Procter and Gamble - Michele Floris (University of Derby (GB))

10:55 Using Topological Data Analysis to Disentangle Complex Data Sets - Maurizio Sanarico (SDG Group)

11:25 Zenseact : Deep learning and computer vision for self-driving cars - Christoffer Petersson
14:00 Tuesday afternoon
Tuesday afternoon
14:00 - 14:45
Contributions

14:00 Solving Inverse Problems with Invertible Neural Networks - Ullrich Koethe (Visual Learning Lab Heidelberg)
15:00
15:00 - 16:00
Contributions

15:00 Structured models of objects, relations, and physics - Peter Battaglia (DeepMind)
16:00 Tuesday Afternoon'
Tuesday Afternoon'
16:00 - 18:00
Contributions

16:00 Deep Learning @ LHC: An ATLAS Perspective - Amir Farbin (University of Texas at Arlington (US))

16:45 End-to-End, Machine Learning-based Data Reconstruction for Particle Imaging Neutrino Detectors - kazuhiro terao (Stanford University)
Wednesday, 21 October 2020
10:00 Wednesday morning
Wednesday morning
10:00 - 12:55
Contributions

10:00 GANplifying Event Samples - Sascha Daniel Diefenbacher (Hamburg University (DE))

10:20 Generative models for calorimeters response simulation - from GANs through VAE to e2e SAE - Kamil Rafal Deja (Warsaw University of Technology (PL))

10:40 Reduced Precision Strategies for Deep Learning: 3DGAN Use Case - Florian Rehm (Hochschule Coburg (DE))

11:00 FastCaloGAN: a tool for fast simulation of the ATLAS calorimeter system with Generative Adversarial Networks - Michele Faucci Giannelli (INFN e Universita Roma Tor Vergata (IT))

11:05 Estimating Support Size of Distribution Learnt by Generative Adversarial Networks for Particle Detector Simulation - Kristina Jaruskova (Czech Technical University in Prague)

11:10 Fast simulation of Time Projection Chamber response at MPD using GANs - Artem Maevskiy (National Research University Higher School of Economics (RU))

11:15 Domain Adaptation Techniques in Particle Identification for the ALICE experiment - Michal Kurzynka (Warsaw University of Technology (PL))

11:20 Black-Box Optimization with Local Generative Surrogates - Vladislav Belavin (Yandex School of Data Analysis (RU))

11:40 Using Machine Learning to Speed Up and Improve Detector R&D - Alexey Boldyrev (NRU Higher School of Economics (Moscow, Russia))

12:00 Matrix Element Regression with Deep Neural Networks -- breaking the CPU barrier - Florian Bury (UCLouvain - CP3)

12:20 Adaptive divergence for rapid adversarial optimization & (1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets - Maxim Borisyak (Yandex School of Data Analysis (RU))
14:00 Wednesday afternoon
Wednesday afternoon
14:00 - 17:00
Contributions

14:00 Accelerated pixel detector tracklet finding with Graph Neural Networks on FPGAs - Savannah Jennifer Thais (Princeton University (US))

14:20 Set2Graph: Secondary Vertex finding in Jets with Neural Networks - Jonathan Shlomi (Weizmann Institute of Science (IL))

14:40 Invertible Networks or Partons to Detector and Back Again - Anja Butter

15:00 Hit-reco: ProtoDUNE denoising with DL models - Marco Rossi

15:20 Efficiency parametrization with Neural Networks - Nilotpal Kakati (Weizmann Institute of Science (IL))

16:10 Zero-Permutation Jet Parton Assignment - Seungjin Yang (University of Seoul, Department of Physics (KR))

16:30 Design by intelligent committee: use of machine learning as a scientific advisor - Stephen Burns Menary (University of Manchester)
17:00 Plenary Wednesday Afternoon
Plenary Wednesday Afternoon
17:00 - 17:30
Contributions

17:00 Neural Network Pruning:  from over-parametrized to under-parametrized networks - Michela Paganini (Facebook AI Research)
Thursday, 22 October 2020
09:00 Thursday morning
Thursday morning
09:00 - 12:25
Contributions

09:00 AutoDQM: A Statistical Tool for Monitoring Data Quality in the CMS Detector - Vivan Thi Nguyen (Northeastern University (US))

09:20 Quantum Graph Neural Networks for Track Reconstruction in Particle Physics and Beyond - Cenk Tuysuz (Middle East Technical University (TR))

09:40 Quantum Generative Adversarial Networks - Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))

10:20 SWAN: Powering CERN's Data Analytics and Machine Learning Use cases - Luca Canali (CERN) Riccardo Castellotti (CERN) Prasanth Kothuri (CERN)

10:40 Accelerating GAN training using distributed tensorflow and highly parallel hardware - Renato Paulo Da Costa Cardoso (Universidade de Lisboa (PT))

11:00 Using an Optical Processing Unit for tracking and calorimetry at the LHC - David Rousseau (IJCLab-Orsay)

11:20 MLaaS4HEP: Machine Learning as a Service for HEP - Luca Giommi (Universita e INFN, Bologna (IT))

11:25 Distributed training of graph neural network at HPC - Xiangyang Ju (Lawrence Berkeley National Lab. (US))

11:30 Hyperparameter Optimisation for Machine Learning using ATLAS Grid and HPC - Rui Zhang (University of Wisconsin Madison (US))

11:35 Identifying jets in the Lund plane - Frederic Alexandre Dreyer (University of Oxford)

11:55 General recipe to form input space for deep learning analysis of HEP scattering processes. - Lev Dudko (M.V. Lomonosov Moscow State University (RU))
14:00 Thursday afternoon
Thursday afternoon
14:00 - 16:00
Contributions

14:00 Lorentz Equivariant Neural Networks for Particle Physics - Alexander Bogatskiy (University of Chicago)

14:20 Graph Neural Network-based Event Classification for Measurement of the Higgs-Top Yukawa Interaction - Ryan Roberts (Lawrence Berkeley National Lab. (US))

14:40 Disentangling Boosted Higgs Boson Production Modes with Machine Learning - Yi-Lun Chung (National Tsing Hua University (TW))

14:45 Bayesian Neural Networks for Predictions from High Dimensional Theories - Braden Kronheim
16:00
16:00 - 18:00
Contributions

16:00 Deep Dive on Graph Networks for Learning Simulation (Deep Mind) - Alvaro Sanchez-Gonzalez (DeepMind)

17:00 Tracking GNN Walk Through - Daniel Thomas Murnane (Lawrence Berkeley National Lab. (US)) Xiangyang Ju (Lawrence Berkeley National Lab. (US))
Friday, 23 October 2020
10:00 Friday morning
Friday morning
10:00 - 12:20
Contributions

10:00 Foundations of a Fast, Data-Driven, Machine-Learned Simulator - Jessica N. Howard (Department of Physics & Astronomy, UC Irvine) Jessica Nicole Howard (University of California Irvine (US))

10:20 Selective background MC simulation with graph neural networks at Belle II - Nikolai Hartmann (Ludwig Maximilians Universitat (DE))

10:25 Pixel Detector Background Generation using Generative Adversarial Networks at Belle II - Hosein Hashemi (LMU)

10:30 Reinforcement learning environment for deep learn physics dataset - Maciej Majewski (AGH-UST) Maciej Witold Majewski (AGH University of Science and Technology (PL))

10:35 Improving particle-flow with deep learning - Sanmay Ganguly (Weizmann Institute of Science (IL))

10:55 Super-resolution for calorimetry - Francesco Armando Di Bello (Sapienza Universita e INFN, Roma I (IT))

11:15 Deep learning solutions for 2D calorimetric cluster reconstruction at LHCb - Michal Mazurek (National Centre for Nuclear Research (PL))

11:35 Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data - Jan Kieseler (CERN)

11:55 UCluster: Unsupervised clustering for HEP - Vinicius Massami Mikuni (Universitaet Zuerich (CH))

12:00 A readily-interpretable fully-convolutional autoencoder-like algorithm for unlabelled waveform analysis - Benjamin Krikler (University of Bristol (GB))
14:00 Friday afternoon
Friday afternoon
14:00 - 17:55
Contributions

14:00 Teaching Machine Learning with ATLAS Open Data - Meirin Oan Evans (University of Sussex (GB))

14:20 Active Anomaly Detection for time-domain discoveries - Emille Eugenia DE OLIVEIRA ISHIDA (CNRS)

14:40 Generative Adversarial Network for Identifying the Dark Matter Distribution of a Dwarf Spheroidal Galaxy - Sung Hak Lim (Rutgers University)

15:00 Pre-Learning a Geometry Using Machine Learning to Accelerate High Energy Physics Detector Simulations - Evangelos Kourlitis (Argonne National Laboratory (US))

15:05 High Fidelity Simulation of High Granularity Calorimeters with High Speed - Engin Eren (Deutsches Elektronen-Synchrotron DESY)

15:10 Graph Convolutional Operators in the the PyTorch JIT - Lindsey Gray (Fermi National Accelerator Lab. (US))

15:15 GPU and FPGA as a Service for Machine Learning Inference Accelerations - Yu Lou (University of Washington (US))

15:50 DisCo: Robust Networks and automated ABCD background estimation - David Shih (Rutgers University)

16:10 Decorrelation via Disentanglement - Justin Tan

16:30 Enhancing searches for resonances with machine learning and moment decomposition - Ouail Kitouni (Massachusetts Inst. of Technology (US))

16:35 Simulation-Assisted Decorrelation for Resonant Anomaly Detection - Kees Christian Benkendorfer (Lawrence Berkeley National Lab. (US))

16:55 Anomaly Awareness for BSM Searches at the LHC - Charanjit Kaur Khosa

17:15 Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests - Purvasha Chakravarti (Imperial College London)

17:20 Conclusion and wrap-up

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