Computational Perception Group

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Lab members

Rich photo 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. Before taking up this position, 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 for signal processing and developing probabilistic models of perception.

Felipe photo

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, and is waiting for his thesis examination. Felipe is currently a Research Assistant at CBL and his research interests lie within the interface between signal processing and machine learning, including high-dimensional kernel regression, Bayesian system identification, and nonlinear adaptive filtering.

Alex photo Alexandre Navarro has worked as an engineer at Oxiteno and has received engineering and masters degrees from the University of Campinas (UNICAMP).

Alexandre is currently a PhD student supervised by Dr. Richard Turner and Dr. Carl Rasmussen. He is funded by CAPES and the Cambridge Overseas Trust and his current research interests are in the area of Bayesian changepoint detection and control.

Yingzhen photo

Yingzhen Li graduated with a B.S. in mathematics from Sun Yat-sen University, Canton, China.

In her undergrad years she worked on projects of data mining, including social network analysis and short-text analysis.

Thang photo

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 interest is probabilistic methods for signal processing.

Shane photo

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 is funded by the Cambridge-Tübingen PhD Fellowship, Kenneth-Sutherland Memorial Scholarship from Jesus College, the ALTA Institute, and donation from Facebook.

Shane photo

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 has now joined the Cambridge-Tübingen PhD program in machine learning funded by a Cambridge-Tübingen fellowship and the EPSRC.

Former members

Rosy photo

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. She is now working as a Research Assistant, developing auditory tests to assess cochlear implants, in collaboration with Bob Carlyon at the Cognition and Brain Sciences Unit.


Bob Carlyon, Cognition and Brain Sciences Unit, Cambridge
Auditory psychophysics and research into cochlear implants.

Maneesh Sahani, Gatsby Computational Neuroscience Unit, University College London
Machine learning and computational auditory scene analysis.

Jozsef Fiser, Department of Cognitive Science, Central European University, Hungary
Probabilistic models for neural computation.

Eero Simoncelli, Center for Neural Science, New York University
Computer vision and visual neuroscience.

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