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):**

**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):**

**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