I have developed and made significant contributions to the following open source software packages:

Falcon: Fast Non-Parametric Detector Simulator

 

Authors: Sergei Gleyzer, Ali Hariri, Harrison Prosper, Omar Zapata Mesa, Darya Dyachkova

Publications:

• Ali Hariri, S. Gleyzer and D. Dyachkova, “Graph Generative Models for Fast Detector Simulations in High Energy Physics”, arXiv:2104.01725
• S. Gleyzer et al., “Graph Generative Models for Fast Detector Simulation in Particle Physics“, 2020
• S. Gleyzer et al., “Falcon: Towards an Ultra Fast Non-Parametric Detector Simulator”, arxiv: 1605.02684, 2016

Google Summer of Code Project(s):

• On the potential of graph-based models in High Energy Physics (2021)
• Normalizing Flows for Fast Detector Simulation (2021)
• Fast Simulation with Deep Generative Models (2020)
• Optimize fast detector simulation and multiobjective regression (2018)
• Scaling up Falcon: TMVA implementation of neural networks for multi-jet regression (2017)

IRIS-HEP Fellowship Project(s):

• Graph Generative Models for Fast Detector Simulations in Particle Physics (2021)

Code: Falcon, forthcoming new release (DeepFalcon)

 

CMS End-to-End Deep Learning (E2E) Project:

Authors: Michael Andrews, Sergei Gleyzer, Manfred Paulini, Barbabas Poczos

 

 

Publications:

• M. Andrews et al., End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data“, arXiv:2104.14659 , 2021
• M. Andrews et al., “End-to-End Identification of Quarks and Gluons with the CMS Open Data”, Nuclear Instruments and Methods A 977 (2020) 164304
• M. Andrews, M. Paulini, S. Gleyzer and B. Poczos,, “End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC“, Computing and Software for Big Science 4 (2020) 6

Google Summer of Code Project(s):

• Graph Neural Networks for End-to-End Particle Identification with the CMS Experiment (2021)
• End-to-End Deep Learning Regression for Measurements with the CMS Experiment (2021)
• End-to-End Deep Learning Reconstruction for CMS Experiment (2021)
• End-to-end Deep Learning Reconstruction for the CMS Experiment (2020)

IRIS-HEP Fellowship Project(s):

• Accelerating End-to-End Deep Learning Reconstruction using Graph Neural Networks (2021)

 

DeepLense: Deep Learning Analysis and Simulation Framework for Strong Gravitational Lensing

Authors: Stephon Alexander, Sergei Gleyzer, Hanna Parul, Pranath Reddy, Michael Toomey, Emanuele Usai, Ryker von Klar

Publications:

• S. Alexander et al., “Decoding Dark Matter Without Supervision“, arXiv:2008.12731
• S. Alexander et al., “Deep Learning the Morphology of Dark Matter Substructure”, arXiv: 1909.07346, The Astrophysical Journal 893 (2020)

Google Summer of Code Project(s):

• Domain Adaptation for Decoding Dark Matter with Strong Gravitational Lensing (2021)
• Equivariant Neural Networks for Dark Matter Morphology with Strong Gravitational Lensing (2021)
• Direct Objective Function for Anomaly Detection (2021)
• Building a Python-based Framework for Unsupervised Deep Learning Applications in Strong Lensing Cosmology  (2020)

 

Code: Forthcoming Release

 

Quantum Machine Learning for High Energy Physics (QMLHEP) Project

Google Summer of Code Project(s):

• Quple – Quantum GAN (2021)
• Quantum Convolutional Neural Networks for High Energy Physics Analysis at the LHC (2021)
• Quantum Machine Learning for HEP (2020)

Code: Quple (2020)

ROOT/TMVA – The Toolkit for Multivariate Data Analysis

 

 

The Toolkit for Multivariate Data Analysis provides a ROOT-integrated machine-learning environment for the processing and parallel evaluation of sophisticated machine learning classification and regression techniques.

Since 2015, I have led a significant upgrade and re-design of TMVA focused on robust gpu-capable deep learning libraries, modularity and parallelization.

Authors: Sergei Gleyzer, Lorenzo Moneta, Omar Zapata, Kim Albertsson et al.

Website: http://www.root.ch/tmva

Publication:

• S. Gleyzer et al., “Machine Learning Developments n ROOT”, in Proceedings of International Conference in High Energy and Nuclear Physics, 2017

Google Summer of Code Project(s):

• TMVA Deep Learning Developments – Inference Code Generation for Batch Normalization (2021)
• 3D Convolutions for GPU (2021)
• ROOT Storage of Deep Learning models in TMVA (2021)
• Inference Code Generation for Recurrent Neural Networks (2021)
• Graph Neural Networks for HEP (2020)
• Development of 3D CNN in TMVA (2020)
• Development of PyTorch Interface in TMVA (2020)
• LSTM and GRU Layers in TMVA (2019)
• Generative Adversarial Networks for Particle Physics Applications (2019)
• Recurrent Neural Networks and LSTMs for Particle Physics Appplications (2018)
• Generative Adversarial Networks for Particle Physics Applications (2018)
• Convolutional Neural Networks on GPUs for Particle Physics Applications (2018)
• Variational Auto-encoders on GPUs for Particle Physics Applications (2018)
• Development of Deep Learning Optimization Algorithms (2018)
• Convolutional Neural Networks on GPUs for Particle Physics Applications (2017)
• Recurrent Neural Networks on GPUs for Particle Physics Applications (2017)
• Deep Auto-Encoders for Particle Physics Applications (2017)
• Integration of TMVA and OpenML (2017)
• GPU-Accelerated Deep Neural Networks in TMVA (2016)
• Integrating Machine Learning in Jupyter Notebooks (2016)
• Integration of Spark Parallelization in TMVA (2016)
• Feature engineering in TMVA (2016)

Code: TMVA

 

Other Google Summer of Code Projects and Software Development:

• Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System (2021)
• Machine Learning Model for the Albedo of Mercury (2021)
• Machine Learning Model for the Planetary Albedo (2021)
• Background Estimation with Neural Autoregressive Flows (2021)
• Dimensionality Reduction for Studying Diffuse Circumgalactic Medium (2021)
• Uncovering the Enigma of Type-Ia Supernovae: Thermonuclear Supernova Classification via their Nuclear Signatures (2021)
• Deep Learning Algorithms for Momentum Estimation in the CMS Trigger System (2020)
• Cosmic-Ray Imaging Studies via Mission Imagery from Space (2020)

CODER: CMS Open Data Analysis Environment

 

 

CODER is a collection of interactive Jupyter notebooks focused on introductory programming concepts and analysis of Open data for K-12 teachers and students.

 

Authors: Sergei Gleyzer, Omar Zapata

Website: coder.cern.ch

Code: Gallery

 

 

PARADIGM: Decision-making Framework for Variable Selection and Reduction in High Energy Physics

 

Primary Authors: Sergei Gleyzer

 

Publication:

• S. Gleyzer and H. Prosper, “PARADIGM: Decision-Making Framework for Variable Selection and Reduction in High Energy Physics”, in Proceedings of XII International Workshop on Advanced Computing and Analysis Techniques in Physics Research, 2009

 

Code: partially integrated into TMVA since 2015