DeepForge - A Machine Learning Gateway for Scientific Workflow Design

BIO

Peter Volgyesi is a Research Scientist at the Institute for Software Integrated Systems at Vanderbilt University. He is one of the architects of the Generic Modeling Environment, a widely used metaprogrammable visual modeling tool, and WebGME - its modern web-based variant. Mr. Volgyesi had a leading role in developing the real-time signal processing algorithms in PinPtr, a low cost, low power countersniper system. He also participated in the development of the Radio Interferometric Positioning System (RIPS), a patented technology for accurate low-power node localization. As PI on two NSF funded projects Mr. Volgyesi and his team developed a low-power software-defined radio platform (MarmotE) for wireless cyber-physical systems. His team won the Preliminary Tournament of the DARPA Spectrum Challenge in September, 2013 and the first phase of the DAPRA Spectrum Collaboration Challenge in December, 2017.

ABSTRACT

DeepForge is an open source platform for deep learning designed for promoting reproducibility, simplicity and rapid development within diverse scientific domains. It leverages the strengths of Model-Integrated Computing to design a development environment for deep learning using WebGME, provides collaborative editing capabilities with integrated version control of code and data and distributes jobs over connected computational resources.