The Machine Learning and Perception group is one of the major research groups at Microsoft Research Cambridge. Its research broadly aims at structure and exploiting structure in real-world data sets, with applications such as matchmaking in online gaming, intelligent image processing, web search and analysis of DNA data.
The Computer Vision group has an exciting research agenda around the topics of human body tracking, image and video editing, object class recognition, medical image analysis, and the theory of large-scale optimisation. An example of our recent success is the award-winning work on skeletal tracking in Microsoft’s Kinect.
The group has embedded researchers and developers from a number of Microsoft product groups in the area of online services. Collaborative research areas include information retrieval for Bing, SharePoint, Yammer, auction design and optimisation for adCenter, and recommendation systems for Xbox Live.
- Dan Alistarh and Rati Gelashvili, Polylogarithmic-Time Leader Election in Population Protocols, Springer, July 2015.
- Cecily Morrison, Marcus D'Souza, Kit Huckvale, Jonas F Dorn, Jessica Burggraaff, Christian Philipp Kamm, Saskia Marie Steinheimer, Peter Kontschieder, Antonio Criminisi, Bernard Uitdehaag, Frank Dahlke, Ludwig Kappos, and Abigail Sellen, Usability and acceptability of ASSESS MS: a system to support the assessment of motor dysfunction in Multiple Sclerosis using depth-sensing computer vision, in Journal of Medical Internet Research (Human Factors), May 2015.
- Toby Sharp, Cem Keskin, Duncan Robertson, Jonathan Taylor, Jamie Shotton, David Kim, Christoph Rhemann, Ido Leichter, Alon Vinnikov, Yichen Wei, Daniel Freedman, Pushmeet Kohli, Eyal Krupka, Andrew Fitzgibbon, and Shahram Izadi, Accurate, Robust, and Flexible Real-time Hand Tracking, CHI, April 2015.
- Bo Zong, Christos Gkantsidis, and Milan Vojnovic, Herding "small" streaming queries, no. MSR-TR-2015-26, 20 March 2015.
- Kevin Schelten, Sebastian Nowozin, Jeremy Jancsary, Carsten Rother, and Stefan Roth, Interleaved Regression Tree Field Cascades for Blind Image Deconvolution, IEEE – Institute of Electrical and Electronics Engineers, 6 January 2015.
Please refer to the research area pages and the homepages of individual group members.
- The Machine Learning and Perception Group has vacancies from time to time. We are always interested to hear from outstanding scientists, especially recent PhDs, but also established researchers. For further information please contact Chris Bishop.
Every year, we welcome several interns in the Machine Learning and Perception group. To apply for an internship, please refer to the Cambridge Lab Intership Program page.
What former interns have said: “An inspiring place to work — lots of clever people and exciting research!”
— Timothy Hospedales, Intern, Microsoft Research Cambridge
- Machine Learning and Perception
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