 
Lab members

  Richard Turner holds a Lectureship (equivalent to US Assistant Professor) in Computer Vision and Machine Learning in the Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, UK. He is a Fellow of Christ's College Cambridge. Previously, he held an EPSRC Postdoctoral research fellowship which he spent at both the University of Cambridge and the Laboratory for Computational Vision, NYU, USA. He has a PhD degree in Computational Neuroscience and Machine Learning from the Gatsby Computational Neuroscience Unit, UCL, UK and a M.Sci. degree in Natural Sciences (specialism Physics) from the University of Cambridge, UK.
His research interests include machine learning, signal processing and developing probabilistic models of perception.

 
 
 
 
 
Yingzhen Li graduated with a B.S. in mathematics from Sun Yatsen University, Canton, China.
The goal of her PhD is to develop approximate inference algorithms that are both accurate and efficient (in time and space complexities). She has broad interests in topics including deep learning, transfer learning, information theory, and optimization. She is a member of Darwin College and holds a FFTF fellowship from the Schlumberger Foundation.

 
 
 
Thang Bui graduated with an engineering degree from Adelaide University, Australia. He spent two years working for the same university before moving to Cambridge.
His current research interests are approximation inference and Gaussian process probabilistic models.

 
 
 
Shixiang (Shane) Gu received a B.ASc. in Engineering Science from the University of Toronto, where he did his undergraduate thesis with Professor Geoffrey Hinton on distributed training of neural networks. He then worked as an R&D Engineer in Panasonic Silicon Valley Lab before joining the CambridgeTübingen PhD programme. He is funded by the CambridgeTübingen PhD Fellowship, KennethSutherland Memorial Scholarship from Jesus College, the ALTA Institute, and donation from Facebook.

 
 
 
Mateo RojasCarulla graduated with an MSc in Mathematical Engineering and Computer Science from Ecole des Ponts ParisTech and a Master of Advanced Study in Mathematics from Cambridge. He went on to work as an equity quantitative analyst at Credit Suisse, then as an engineer in Cantab Research, where he developed recurrent neural networks for language modelling. He has now joined the CambridgeTübingen PhD program in machine learning funded by a CambridgeTübingen fellowship and the EPSRC.

 
 
 
Mark Rowland graduated from Cambridge University with a BA and MMath, having specialised in Algebra and Probability.
He has previously worked in consultancy as an actuarial analyst and as a quant researcher intern at a systematic trading firm.
He is broadly interested in machine learning, probabiltiy and statistics, with particular focus on Monte Carlo sampling.
His PhD research is funded by an EPSRC studentship from the Cambridge Centre of Analysis.

 
 
James Requeima graduated with an MPhil in Machine Learning, Speech and Language Technology from the University of Cambridge.

 
 
 
 
Cuong V. Nguyen received the B.Comp. and PhD degrees in Computer Science both from the School of Computing, National University of Singapore. Before joining the University of Cambridge, he was a Research Fellow at the Faculty of Engineering, National University of Singapore. His main research interests include both theoretical and practical aspects of machine learning, especially active learning and statistical learning theory..

 
 
 
 
 
John Bronskill is a PhD student in the Machine Learning Group at the University of Cambridge supervised by Richard Turner and Sebastian Nowozin. His research interests include machine learning, signal processing, computer vision, and digital imaging. He holds a Bachelor’s degree and a Master’s degree in Electrical Engineering from the University of Toronto. In a previous lifetime, he cofounded ImageWare? Research & Development which built special effects software for digital photos and video. The startup was subsequently acquired by Microsoft where he spent a lengthy career in various technical leadership and engineering management roles.

 
 
 
Siddharth Swaroop graduated with an MEng from the University of Cambridge, having specialised in Information Engineering. He is currently funded by an EPSRC Doctoral Training Award. His research interests within machine learning are broad, but his current focus is approximate inference techniques.

 
 
 
Will Tebbutt holds an M.Phil. in Machine Learning, Speech and Language Techology from the University of Cambridge and an M.Eng. in Engineering Mathematics from the University of Bristol. He previously worked as a researcher in machine learning at Invenia Labs in Cambridge, and is a member of Darwin College.
His research interests include Gaussian processes, automating Bayesian inference, the application machine learning to climate science, and probabilistic numerics.

