Justin is currently a postdoctoral researcher at BrainGate(Brown University) and an appointed Research Fellow at Massachusetts General Hospital and Harvard Medical School. Justin is primarily concerned with decoding speech from neural data associated with chronically implanted multi-electrode arrays of human participants in the BrainGate2 clinical trial. Justin is also interested in improving the long term stability of real-time neural decoders of movement and speech.

Previously he was a PhD student in the Institute for Adaptive and Neural Computation at the University of Edinburgh where he worked with Matthias Hennig on latent variable modelling of neural data using sequential autoencoder networks.

Justin also looked at hippocampal representations in neural networks and how these form throughout training on maze tasks. He found that these formed representations in neural networks are strikingly similar to their neuronal counterparts. Additionally, he explored the potential and variety of biologically plausible neural networks, namely on training methods for Spiking Neural Networks and the applications and performance of such networks when compared with traditional neural networks.

Before starting a PhD he was a Machine Learning Engineer at PaperCup, working on Deep Generative Models such as WaveNet in the realm of Neural Speech Synthesis and Machine Translation.

Justin received my integrated Masters from University College London where he worked with Simone Severini and Edward Grant on Deep Generative Models such as Variational Autoencoders for Quantum Computing States.

His undergraduate degree was also at University College London in Mathematics and Computer Science before focusing on Machine Learning, Deep Learning, Reinforcement Learning and Machine Vision during his integrated masters. Over the course of his undergraduate degree he squeezed in three internships at Santander, BAE Systems and Amazon.

Website: http://justinjude.co.uk/