Associate Professor
Dept. of Computer Science
University of Oxford

Program Director
Samsung AI Center, Cambridge

Fellow
Kellogg College
University of Oxford

nicholas.lane@cs.ox.ac.uk

@niclane7
LinkedIn
Google Scholar

I am an Associate Professor in the Computer Science department at the University of Oxford, where I lead the Machine Learning Systems lab. This lab is part of the Cyber-Physical Systems group.

I am also a Fellow of Kellogg College, and teach machine learning on the Professional Masters Program. 

At the Samsung AI Center, I am a Program Director for on-device and distributed machine learning.

[ Extended Bio ]

NEWS

09/2018:   Samsung funding for our Lab

We are pleased to announce £590k in funding for our Machine Learning Systems lab for post-docs, students and equipment in 2018/19. 

06/2018:   EPSRC Fellowship Awarded

I have been awarded a fellowship for my proposal “MOA: High Efficiency Deep Learning for Embedded and Mobile Platforms”.

05/2018:   Three Post-doc Positions Open 

We are advertising for three post-docs in my lab (one 30-month, two 12-month), the start time is flexible. Please apply.

04/2018:   IJCAI 2018 Accepted

Our paper ‘Deterministic Binary Filters for Convolutional Neural Networks’ has been accepted to IJCAI 2018. 

04/2018:   £1.6M OPERA Project funded by EPSRC

Our grant to investigate passive radar technology for contextual sensing has been funded by EPSRC. Looking forward to working with UCL, Bristol, Coventry on this important research. 

Associate Professor
Department of Computer Science
University of Oxford

Program Director
Samsung AI Center, Cambridge

Fellow
Kellogg College
University of Oxford

nicholas.lane@cs.ox.ac.uk

@niclane7
LinkedIn
Google Scholar

LATEST NEWS

11/2017:   New Research Grant

My team is looking for a new member.

08/2016:  Paper Accepted for SenSys 2016

Our paper ‘Sparsifying Deep Learning Layers for Constrained Resource Inference on Wearables’ has been (conditionally) accepted to SenSys 2016.

08/2016:  Paper Accepted for SenSys 2016

Our paper ‘Sparsifying Deep Learning Layers for Constrained Resource Inference on Wearables’ has been (conditionally) accepted to SenSys 2016.