I have developed and made significant contributions to the following open source software packages:
Falcon: Fast Non-Parametric Detector Simulator
Authors: Sergei Gleyzer, Harrison Prosper, Omar Zapata
Publication:
- S. Gleyzer et al., “Falcon: Towards an Ultra Fast Non-Parametric Detector Simulator”, arxiv: 1605.02684, 2016
Google Summer of Code Project(s):
- Optimize fast detector simulation and multiobjective regression (2018)
- Scaling up Falcon: TMVA implementation of neural networks for multi-jet regression (2017)
Code: Falcon
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):
- 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
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