Machine Learning Project - Convolutional Deep Neural Networks on GPUs for Particle Physics Applications
Project Description
Toolkit for Multivariate Analysis (TMVA) is a multi-purpose machine learning toolkit integrated into the ROOT scientific software framework, used in many particle physics data analysis and applications. Last year we have expanded TMVA’s capabilities to include feed-forward deep learning library (DNN) that supports interactive training on GPUs. This summer we would like to expand the toolkit with optimized convolutional deep neural networks (CNN). CNNs have very promising applications in particle physics such as particle and event classification, imaging calorimetry and particle tracking, allowing physicists to use new techniques to identify particles and search for new physics.
Task ideas and expected results
- Production-ready convolutional deep learning library
- Design of first-stage filters
- Support for GPUs for training
- integration with existing low-level interface designed for the DNN library
Requirements
Strong C++ skills, solid knowledge of deep learning, understanding of convolutional networks, familiarity with GPU interfaces a plus