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

Publication:

• S. Gleyzer et al., “Falcon: Towards an Ultra Fast Non-Parametric Detector Simulator”, arxiv: 1605.02684, 2016

Google Summer of Code Project(s):

• 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)

Code: Falcon

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

Authors: Stephon Alexander, Sergei Gleyzer, Michael Toomey, Emanuele Usai

Publications:

• 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):

• Building a Python-based Framework for Unsupervised Deep Learning Applications in Strong Lensing Cosmology  (2020)

 

Code: Forthcoming Release

 

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):

• 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:

• Quantum Machine Learning for HEP (2020)
• End-to-end Deep Learning Reconstruction for the CMS Experiment (2020)
• 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