Andrew Trask, a 2015 Belmont graduate with a B.S. in Applied Discrete Mathematics and B.B.A. in Finance, recently shared co-authorship with researchers from NVIDIA, Intel, the National Institutes of Health, Vanderbilt Univeristy and other institutions on a paper involving data-driven machine learning, published in the Digital Nature Partner Journal (NPJ).”
The article, “The Future of Digital Health with Federated Learning,” explores how federated learning may provide a solution to the shortage of digital medical data available for Machine Learning applications. The paper discusses the potential of federated learning in supporting the future of digital health and highlights the challenges and considerations that need to be addressed. The journal can be found here: https://www.nature.com/articles/s41746-020-00323-1
Trask, currently a Google DeepMind Scholar and Ph.D. student at Oxford University, is the author of “Grokking Deep Learning,” which teaches readers to build deep learning neural networks from scratch. Trask is also the co-author of the Udacity Deep Learning curriculum and founder of the OpenMined project, an open-source framework that will allow developers to gain insight from users’ data without compromising their privacy.
