Members < Public/Turner

Computational Perception Group

		Lab members
		Richard Turner is Professor of Machine Learning in the Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, UK. He is also a Bye Fellow of Christ's College and until recently was also a Visiting Researcher at Microsoft Research Cambridge. Dr. Turner is Course Director of the Machine Learning and Machine Intelligence MPhil programme. He is also Co-Director of the UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER CDT). Over the last two years his work has been presented in oral presentations at top machine learning conferences including AAAI, AIStats, ICLR, ICML and NeurIPS and he has given keynote lectures and tutorials at the Machine Learning and Signal Processing Summer School, the International Conference on Machine Learning, Optimization & Data Science, and the Machine Learning Summer School. He has been the lead supervisor for 13 PhD students (6 now graduated) and three RAs. He has received over £5M of industrial funding from Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable and over £9M of funding from the EPSRC. Dr. Turner is on the Steering Committee for the Cambridge Centre for Data Driven Discovery (C2D3). He has been awarded the Cambridge Students' Union Teaching Award for Lecturing. His work has featured on BBC Radio 5 Live’s The Naked Scientist, BBC World Service’s Click and in Wired Magazine. His CV is available here (updated Oct 2020) and full contact details are here.


		James Requeima graduated with an MPhil in Machine Learning, Speech and Language Technology from the University of Cambridge. He is interested in Bayesian optimization, Gaussian processes, meta-learning, reinforcement learning, approximate inference methods, and deep generative models.




		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 co-founded ImageWare? Research & Development which built special effects software for digital photos and video. The start-up 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.


		Wessel Bruinsma holds an MPhil in Machine Learning, Speech, and Language Technology from the University of Cambridge and a BSc in Electrical Engineering from the University of Delft. He was previously employed as a machine learning researcher at Invenia Labs in Cambridge, and was part of the TU Delft Solar Boat Team in Delft. His research interests include probabilistic modelling, with a focus on Gaussian processes, approximate inference, and signal processing. Wessel is a member of Christs's College, and is currently funded by an International Doctoral Scholars (IDS) Grant from the EPSRC.


		Andrew (Yue Kwang) Foong graduated from the Cambridge University Engineering Department with a BA and MEng in Information and Computer Engineering in 2018. His Masters project was on designing message-passing codes to achieve the rate-distortion limit in lossy compression problems. He is currently funded by the Trinity Hall Research Studentship and the George and Lilian Schiff Studentship. His research interests are broadly at the intersection of probabilistic modelling and deep learning.


		Elre Oldewage holds an MSc in Computer Science, a BSc (Hons) in Computer Science and a BSc (Hons) in Mathematics from the University of Pretoria, South Africa. She is a member of Churchill College and is currently funded by a Schlumberger Cambridge Scholarship. Her interests are in machine learning, specifically within the area of adversarial attacks and model robustness.



		Marcin Tomczak graduated with an MPhil in Machine Learning, Speech and Language Technology from University of Cambridge. Prior to this he obtained MSc in Mathematics from Adam Mickiewicz University. Before joining CBL in October 2019 he spent three years working as a machine learning engineer. He is broadly interested in machine learning with the focus on probabilistic modeling applied to reinforcement learning and approximate inference. He is a member of Clare College and he is funded by Vice-Chancellor’s Award administered by Cambridge Trust.




		Jonathan So is interested in probabilistic machine learning broadly, with particular interest in representation learning, recognition models, and connections between probabilistic inference and perception in the brain. After graduating from the University of Kent with a BSc in Computer Science, Jonathan spent several years working in finance as both a systems programmer and trader. He later studied for the MSc Computational Statistics and Machine Learning at UCL, following which he spent several months working with Maneesh Sahani in the Gatsby Unit, before joining the CBL in October 2019. He is a member of Darwin College and is funded by the Harding Distinguished Scholarship Programme.

		Former members
		Mateo Rojas-Carulla 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 then joined the Cambridge-Tübingen PhD program in machine learning funded by a Cambridge-Tübingen fellowship and the EPSRC. He is now a Research Scientist at Facebook.



		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 Cambridge-Tübingen PhD programme. He was funded by the Cambridge-Tübingen PhD Fellowship, Kenneth-Sutherland Memorial Scholarship from Jesus College, the ALTA Institute, and donations from Facebook and Google. Shane is now a Research Scientist at Google Brain.



		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, continual learning and statistical learning theory. Cuong is now a Research Scientist at Amazon.


		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 was funded by an EPSRC studentship from the Cambridge Centre of Analysis. Mark is now a Research Scientist at DeepMind.


		Yingzhen Li graduated with a B.S. in mathematics from Sun Yat-sen University, Canton, China. Her PhD developed a suite of approximate inference algorithms that are both accurate and efficient (in time and space complexities). These were applied to a broad set of tasks across machine learning. She was a member of Darwin College and held a FFTF fellowship from the Schlumberger Foundation. Yingzhen is now a Researcher at Microsoft Research Cambridge.


		Thang Bui graduated with an engineering degree from Adelaide University, Australia. He spent two years working for the same university before moving to Cambridge. He completed a PhD on Gaussian Processes, but also worked on a range of probabilistic modelling approaches including Bayesian Neural Networks and general approximate inference methods. He is now a Lecturer in Machine Learning and Data Science at the University of Sydney.


		Alexandre Navarro has worked as an engineer at Oxiteno and has received engineering and masters degrees from the University of Campinas (UNICAMP). Alexandre was a PhD student supervised by Dr. Richard Turner. He was funded by CAPES and the Cambridge Overseas Trust and his research interests were in the area of machine learning for circular variables. He has taken up machine learning positions at AstraZeneka and Babylon Health.
		Felipe A. Tobar received the B.Sc. (2008) and M.Sc. (2010) degrees in Electrical and Electronic Engineering from Universidad de Chile. He did his Ph.D. Signal Processing at Imperial College London. Felipe was a Research Assistant at CBL with research interests at the interface between signal processing and machine learning, including high-dimensional kernel regression, Bayesian system identification, and nonlinear adaptive filtering. He now holds a CONICYT Research Fellowship in Machine Learning and Signal Processing working in the Center for Mathematical Modeling at Universidad de Chile.


		Rosy Southwell recently graduated with an MSci in Natural Sciences (Biological) from the University of Cambridge. Her undergraduate research included vision and network analysis of the human brain. Worked in the Computational Perception Group as a Research Assistant, developing auditory tests to assess cochlear implants, in collaboration with Bob Carlyon at the Cognition and Brain Sciences Unit. She is now studying for a PhD at UCL.

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Computational Perception Group

Lab members

Former members