Rob studied physics and computer science at the University of Chicago, and has worked for Red Hat as a data science intern for the past two years.
PhD title
Auto generation of optimal deep learning networks
This project seeks to discover easier and more efficient methods of generating neural networks.
It is tremendously expensive and/or unfeasibly time consuming to develop state-of-the-art networks. One notable method developed by Google Brain takes 4 days to train on a dedicated, multi-million pound setup. With such resource intensity, innovation is limited to massive companies like Google, Facebook, and Microsoft. These groups dominate the publication space in this field.
This project seeks to bring such methods to a 'human scale'. This means to a scale wherein someone could feasibly run these methods on their own personal machine. This would bring the power of state-of-the-art deep learning to the everyday user.
