Leadership

Director

Professor Andrew Blake

Professor Andrew Blake is Director of the Alan Turing Institute. Prior to joining the institute in 2015, Professor Blake held the position of Microsoft Distinguished Scientist and Laboratory Director of Microsoft Research Cambridge, England. He joined Microsoft in 1999 as a Senior Researcher to found the Computer Vision group. In 2008 he became a Deputy Managing Director at the lab, before assuming the directorship in 2010. Before joining Microsoft Andrew trained in mathematics and electrical engineering in Cambridge England, and studied for a doctorate in Artificial Intelligence in Edinburgh. He was an academic for 18 years, latterly on the faculty at Oxford University, where he was a pioneer in the development of the theory and algorithms that can make it possible for computers to behave as seeing machines.

Professor Blake has published several books including “Visual Reconstruction” with A.Zisserman (MIT press), “Active Vision” with A. Yuille (MIT Press) and “Active Contours” with M. Isard(Springer-Verlag). He has twice won the prize of the European Conference on Computer Vision, with R. Cipolla in 1992 and with M. Isard in 1996, and was awarded the IEEE David Marr Prize (jointly with K. Toyama) in 2001.

In 2006 the Royal Academy of Engineering awarded him its Silver Medal and in 2007 the Institution of Engineering and Technology presented him with the Mountbatten Medal(previously awarded to computer pioneers Maurice Wilkes and Tim Berners-Lee, amongst others.) He was elected Fellow of the Royal Academy of Engineering in 1998, Fellow of the IEEE in 2008, and Fellow of the Royal Society in 2005. In 2010, Andrew was elected to the council of the Royal Society. In 2011, he and colleagues at Microsoft Research received the Royal Academy of EngineeringMacRobert Award for their machine learning contribution to Microsoft Kinect human motion-capture. In 2012 Andrew was elected to the board of the EPSRC and also received an honorary degree of Doctor of Science from the University of Edinburgh. In 2013 Andrew was awarded an honorary degree of Doctor of Engineering from the University of Sheffield. In 2014, Andrew gave the prestigious Gibbs lecture at the Joint Mathematics Meetings (transcript available here). Professor Andrew Blake has been named as the recipient of the 2016 BCS Lovelace Medal, the top award in computing in the UK, awarded by BCS, The Chartered Institute for IT. The award is presented annually to individuals who, in the opinion of BCS, have made a significant contribution to the advancement of Information Systems.

  • Publications

    • J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman, A. Blake (2013). Efficient Human Pose Estimation from Single Depth Images. Decision Forests for Computer Vision and Medical Image Analysis. PDF
    • D.J. Holland, J. Mitchell, A. Blake, and L. F. Gladden (2013). Grain Sizing in Porous Media using Bayesian Magnetic Resonance. Physical Review Letters. PDF
    • A.A. Muryy, A.E. Welchman, A. Blake, R.W. Fleming (2013). Specular Reflections and the Estimation of Shape from Binocular Disparity. Proceedings of the National Academy of Sciences of the United States. PNAS
    • D.J. Holland, A. Blake, A.B. Tayler, A.J. Sederman, L.F. Gladden (2012). Bubble Size Measurement Using Bayesian Magnetic Resonance. Chemical Engineering Science. PDF
    • J.G. Rossa, D.J. Holland, A. Blake, A.J. Sederman, L.F. Gladden (2012). Extending the use of Earth’s Field NMR using Bayesian methodology: Application to Particle Sizing. Journal of Magnetic Resonance. PDF
    • D.J. Holland, A. Blake, A.B. Tayler, A.J. Sederman, L.F. Gladden (2011). A Bayesian Approach to Characterising Multi-phase Flows Using Magnetic Resonance: Application to Bubble Flows. Journal of Magnetic Resonance. PDF Science Direct
    • J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake (2011), Real-Time Human Pose Recognition in Parts from a Single Depth Image, in CVPR (Best Paper), IEEE. PDF
    • D.J. Holland, A. Blake, A.B. Tayler, A.J. Sederman, L.F. Gladden. (2011). A Bayesian Approach to Characterising Multi-phase Flows Using Magnetic Resonance: Application to Bubble Flows. Journal of Magnetic Resonance. 209(1), 83-7.Download
    • V. Gulshan, C. Rother, A. Criminisi, A. Blake, and A. Zisserman (2010). Geodesic Star Convexity for Interactive Image Segmentation, Proc. IEEE Conf. Computer Vision and Pattern Recognition. PDF
    • D.J. Holland, D.M. Malioutov, A. Blake, A.J. Sederman and L.F. Gladden (2010). Reducing Data Acquisition Times in Phase-encoded Velocity Imaging Using Compressed Sensing. Journal of Magnetic Resonance. 203, 2, 236-246. In press.
    • D. Cremers, Y. Boykov, A. Blake, F. R. Schmidt:, eds (2009). Energy Minimization Methods in Computer Vision and Pattern Recognition, in EMMCVPR. PDF
    • P. Parasoglou, D. Malioutov, A.J. Sederman, J. Rasburn, H. Powell, L.F. Gladden, A. Blake and M.L. Johns (2009). Quantitative Single Point Imaging with Compressed Sensing. Journal of Magnetic Resonance. 201, 1, 72-80. PDF
    • B. Amberg, A. Blake, T. Vetter (2009). On Compositional Image Alignment with an Application to Active Appearance Models. IEEE Conf. Computer Vision and Pattern Recognition. PDF
    • V. S. Lempitsky, M.Verhoek, J. A. Noble, A. Blake (2009). Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D Echocardiography. Functional Imaging and Modeling of the Heart. 447-456. PDF
    • Z. Yi, A. Criminisi, J. Shotton, and A. Blake (2009). Discriminative, Semantic Segmentation of Brain Tissue in MR Images. Medical Image Computing and Computer Assisted Intervention. PDF
    • V. Lempitsky, C. Rother, S. Roth, and A. Blake (2009). Fusion Moves for Markov Random Field Optimization. Microsoft Research Technical Report no. MSR-TR-2009-60, May 2009. Also IEEE Trans. PAMI, in press. PDF
    • P. V. Gehler, C. Rother, A. Blake, T. P. Minka, T. Sharp (2008). Bayesian Color Constancy Revisited. Proc. IEEE Conf. Computer Vision and Pattern Recognition. PDF
    • A. Criminisi, T. Sharp, A. Blake (2008). GeoS: Geodesic Image Segmentation. Proc. Eur. Conf. on Computer Vision, 99-112. PDF
    • V. S. Lempitsky, A. Blake, C. Rother(2008). Image Segmentation by Branch-and-Mincut. Proc. Eur. Conf. on Computer Vision, 15-29. PDF
    • J. Shotton, A. Blake, R. Cipolla (2008). Multiscale Categorical Object Recognition Using Contour Fragments. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1270-1281. PDF
    • J. Shotton, A. Blake, R. Cipolla (2008). Efficiently Combining Contour and Texture Cues for Object Recognition.Proc. British Machine Vision Conf. PDF
    • A. W. Fitzgibbon, D.P. Robertson, A. Criminisi, S. Ramalingam, A. Blake (2007). Learning priors for calibrating families of stereo cameras. Proc. Int. Conf. Computer Vision, 1-8. PDF
    • V. S. Lempitsky, C. Rother, A. Blake (2007). LogCut – Efficient Graph Cut Optimization for Markov Random Fields. Proc. Int. Conf. Computer Vision, 1-8. PDF
    • B. Amberg, A. Blake, A.W. Fitzgibbon, S. Romdhani, T. Vetter (2007). Reconstructing High Quality Face-Surfaces using Model Based Stereo. Proc. Int. Conf. Computer Vision, 1-8. PDF
    • A. Agarwal, S. Izadi, M. Chandraker, A. Blake (2007). High Precision Multi-touch Sensing on Surfaces using Overhead Cameras. Tabletop, 197-200.
    • S. Izadi, A. Agarwal, A. Criminisi, J.M. Winn, A. Blake, A.W. Fitzgibbon (2007). C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces. Tabletop, 3-10. PDF
    • A. Criminisi, A. Blake, C. Rother, J. Shotton, P.H.S. Torr (2007). Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming. International Journal of Computer Vision 71(1), 89-110. PDF
    • V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, C. Rother (2006). Probabilistic Fusion of Stereo with Color and Contrast for Bi-Layer Segmentation. Int. Journal of Computer Vision 76(2), 107. PDF
    • V. Kolmogorov, A. Criminisi, A. Blake, G. Cross and C. Rother (2006). Probabilistic Fusion of Stereo with Colour and Contrast for Bi-layer Segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, 480-1492. PDF
    • C. Rother, L. Bordeaux, Y. Hamadi and A. Blake (2006). AutoCollage. ACM Trans. Graphics (SIGGRAPH), 847-852. PDF
    • A. Criminisi, J. Shotton, A. Blake, C Rother and PHS Torr (2006). Efficient Dense Stereo with Occlusions for New View-synthesis by Four-state Dynamic Programming. Int. J. Computer Vision, 0920-5691. PDF
    • A. Agarwal and A. Blake (2006). The Panum Proxy Algorithm for Dense Stereo Matching Over a Volume of Interest. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2339-2346. PDF
    • A. Criminisi, G. Cross and A. Blake (2006). Bilayer Segmentation of Live Video. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 53-60. PDF
    • O. Williams, A. Blake and R Cipolla (2006). Sparse and Semi-supervised Visual Mapping with S3GP. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 230-237. PDF
    • C. Rother, V. Kolmogorov, T. Minka and A. Blake (2006). Cosegmentation of Image Pairs by Histogram Matching Incorporating a Global Constraint into MRFs. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 993-1000. PDF
    • O. Williams, A. Blake, and R. Cipolla (2005). Sparse Bayesian regression for efficient visual tracking. IEEE Trans Pattern Analysis and Machine Intelligence, 27, 8, 12921304. PDF
    • A. Blake (2005). Visual tracking: a short research roadmap. In Mathematical Models of Computer Vision: The Handbook, eds. O. Faugeras, Y. Chen and N. Paragios, Springer, in press. PDF
    • J. Shotton, A. Blake and R. Cipolla (2005). Contour-based learning for object recognition. Proc. Int. Conf. Computer Vision, 1, 503510. PDF
    • C. Rother, S. Kumar, V. Kolmogorov and A. Blake (2005). Digital Tapestry. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2, 589596. PDF
    • V. Kolmogorov, A. Criminisi, A. Blake, G. Cross and C. Rother (2005). Bi-layer segmentation of binocular stereo. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2,407414. PDF
    • O. Williams, A. Blake, and R. Cipolla (2004). The Variational Ising Classifier (VIC) algorithm for coherently contaminated data. Proc Neural Information Processing Systems, 17. PDF
    • J. Winn and A. Blake (2004). Generative affine localisation and tracking. Proc Neural Information Processing Systems, 17. PDF
    • S.Romdhani, P. Torr, B. Schoelkopf and A. Blake (2004). Efficient face detection by a cascaded support-vector machine expansion. Proc. Roy. Soc. A, 460, 2501, 32833297. PDF
    • C. Rother, V. Kolmogorov and A. Blake (2004). GrabCut interactive foreground extraction using iterated graph cuts. ACM Trans. Graphics (SIGGRAPH), 309314. PDF
    • A. Blake, C. Rother, M. Brown, P. Perez, and P. Torr (2004). Interactive image segmentation using an adaptive GMMRF model. Proc. Eur. Conf. on Computer Vision (ECCV), Springer-Verlag, 428441. PDF
    • P. Perez, J. Vermaak and A. Blake (2004). Data fusion for visual tracking with particles. Proc. IEEE, 92, 3, 495513. PDF
    • P. Perez, M. Gangnet and A. Blake (2003). Poisson image editing. ACM Trans. Graphics (SIGGRAPH), 2(3):313-318. PDF
    • A. Criminisi, J. Shotton, A. Blake and P.H.S. Torr (2003). Gaze manipulation for one to one teleconferencing. Proc. Int. Conf. on Computer Vision (ICCV), 191198. PDF
    • O. Williams, A. Blake and R. Cipolla (2003). A sparse probabilistic learning algorithm for realtime tracking. Proc. Int. Conf. on Computer Vision (ICCV), 353360. PDF
    • K. Toyama and A. Blake (2002). Probabilistic tracking with exemplars in a metric space. Int. J. Computer Vision, 48, 919. PDF
    • J. Rittscher, T. Watanabe, J. Kato, S. Joga and A. Blake (2002). An HMM-based segmentation method for traffic monitoring. IEEE Trans. Pattern Analysis and Machine Intelligence, 24, 9. PDF
    • P. Prez, A. Blake, and M. Gangnet (2001). JetStream: Probabilistic contour extraction with particles. Proc. Int. Conf. on Computer Vision (ICCV), II:524-531. PDF
    • J. Vermaak, A. Blake, M. Gangnet, and P. Perez (2001). Sequential Monte Carlo fusion of sound and vision for speaker tracking. Proc. Int. Conf. on Computer Vision (ICCV), I:741-746. PDF
    • J. Sullivan, A. Blake, M. Isard and J. MacCormick (2001). Bayesian Object Localisation in Images. Int. J. Computer Vision, 44, 2, 111-136. PDF
    • J. Deutscher, A. Blake and I. Reid (2000). Articulated Body Motion Capture by Annealed Particle Filtering Proc. Conf. Computer Vision and Pattern Recognition, 2, 126 133. Abstract and .ps.gz and movies
    • B. North, A. Blake, M. Isard and J. Rittscher. (2000). Learning and Classification of Complex Dynamics. IEEE Trans. Pattern Analysis and Machine Intelligence, 22, 9, 10161034. PDF
    • J. MacCormick and A. Blake (2000). Probabilistic exclusion and partitioned sampling for multiple object tracking. Int. J. Computer Vision, 39(1), 57-71, 2000. PDF
    • R. A. Eagle, M.A. Hogervorst and A. Blake. (1999). Does the visual system exploit projective geometry to help solve the motion correspondence problem? Vision Research, 39, 373-385.
    • E. Rimon and A. Blake (1999). Caging planar bodies by 1-parameter, two-fingered gripping systems. Int. J. Robotics Research, 18, 3, 299-318.
    • A. Blake and M. Isard (1998). Active Contours. Springer-Verlag.
    • A. Blake, B. Bascle, M. Isard and J. McCormick (1998). Statistical models of visual shape and motion. Phil. Trans. Roy. Soc. B, 356, 1283-1302. Abstract and .ps.gz
    • M. Isard and A. Blake (1998). CONDENSATION – Conditional density propagation for visual tracking. Int. J. Computer Vision, 29, 1, 5-28. Abstract and .ps.gz
    • M. Isard and A. Blake (1998). A mixed-state Condensation tracker with automatic model switching. Proc 6th Int. Conf. Computer Vision, 107-112. Abstract and .ps.gz
    • B. Bascle and A. Blake (1998). Separability of pose and expression in facial tracking and animation. Proc 6th Int. Conf. Computer Vision, 323-328. Abstract and .ps.gz
    • J. MacCormick and A. Blake (1998). A probabilistic contour discriminant for object localisation. Proc 6th Int. Conf. Computer Vision, 390-395. Abstract and .ps.gz
    • R. Kaucic and A. Blake (1998). Accurate, Real-Time, Unadorned Lip Tracking. Proc 6th Int. Conf. Computer Vision, 370-375. Abstract and .ps.gz
    • C. Davidson and A. Blake (1998). Caging planar objects with a three-finger, one-parameter gripper Proc IEEE Int. Conf. Robotics and Automation, 2722-2727. Abstract and .ps.gz
    • M. Isard and A. Blake (1998). A smoothing filter for Condensation model switching. Proc European Conf. Computer Vision, 768-781. Abstract and .ps.gz
    • M. Isard and A. Blake (1998). ICondensation: Unifying low-level and high-level tracking in a stochastic framework Proc European Conf. Computer Vision, 893-908. Abstract and .ps.gz
    • R. Cipolla and A. Blake (1997). Image divergence and deformation from closed curves. Int. J. Robotics Research, 16, 1, 77-96. Abstract and .ps.gz
    • D. Buckley, J.P. Frisby and A. Blake (1996). Does the human visual system implement an ideal observer theory of slant from texture? Vision Research, 36, 8, 1163-1176.
    • D. Sinclair and A. Blake. (1996) Quantitative planar region detection. International Journal of Computer Vision, 18, 1, 77-91.
    • S. Rowe and A. Blake (1996). Statistical mosaics for tracking. J. Image and Vision Computing, 14, 8, 549-564. Abstract, .ps.gz
    • M. Isard and A. Blake (1996). Contour tracking by stochastic propagation of conditional density. Proc European Conf. Computer Vision, Cambridge, UK, 343-356. Abstract and .ps.gz
    • B. Bascle, A. Blake and A. Zisserman (1996). Motion deblurring and super-resolution from an image sequence. Proc European Conf. Computer Vision, Cambridge, UK, 573-582. Abstract and .ps.gz
    • D. Reynard, A. Wildenberg, A. Blake and J. Marchant (1996). Learning Dynamics of Complex Motions from Image Sequences. Proc European Conf. Computer Vision, Cambridge, UK, 357-368. Abstract and .ps.gz
    • E. Rimon and A. Blake (1996). Caging 2D bodies by one-parameter, two-fingered gripping systems. Proc Int. Conf. Robotics and Automation, 1458-1464, IEEE Press. PDF
    • A. Blake (1995). A symmetry theory of planar grasp. Int. J. Robotics Research, 14, 5, 425-444. Abstract and .ps.gz
    • A. Blake, M. Isard and D. Reynard (1995). Learning to track the visual motion of contours. Artificial Intelligence, 78, 101-134. Abstract and .ps.gz
    • R. A. Eagle and A. Blake. (1995). Two-dimensional constraints on three-dimensional structure from motion tasks. Vision Research, 35, 20, 2927-2941. PDF
    • A. Blake- (1994). Uncertain views. Nature, 368, 498-499.
    • D. Sinclair, A. Blake and D. Murray. (1994). Robust estimation of ego-motion from normal flow. Int. J. Computer Vision, 13, 1, 57-69.
    • A. Blake and M. Isard (1994). 3D position, attitude and shape input using video tracking of hands and lips. Proc. ACM SIGGRAPH Conference, Orlando, USA, 185-192. Abstract and .ps.gz
    • R. Brockett and A. Blake (1994). Estimating the shape of a moving planar curve. Proc. IEEE Int. Conf. Decision Theory and Control, 3247-3252. PDF
    • A. Blake, D. McCowen, H.R. Lo, P.J. Lindsey (1993). Trinocular active range sensing. IEEE Trans. Pattern Analysis and Machine Intelligence, 15, 5, 477-483. PDF
    • A. Blake, H.H. B-ulthoff and D. Sheinberg (1993). Shape from Texture: Ideal Observers and Human Psychophysics Vision Research, 33, 12, 1723-1737. PDF
    • A. Blake, R. Curwen and A. Zisserman (1993). A framework for spatio-temporal control in the tracking of visual contours. Int. J. Computer Vision, 11, 2, 127-145. Abstract and .ps.gz
    • A. Blake, M. Taylor and A. Cox (1993). Grasping visual symmetry. Proc 4th Int. Conf. Computer Vision, Berlin, 724-733. PDF
    • -A. Blake (1992). Computational modelling of hand-eye coordination. Phil. Trans. Roy. Soc. B, 337, 351-360.
    • R. Cipolla and A. Blake (1992). Surface shape from the deformation of apparent contours. Int. J. Computer Vision, 9, 2, 83-112. PDF
    • R. Cipolla and A. Blake (1992). Surface orientation and time to contact from image divergence and deformation. Proc. 2nd European Conf. Computer Vision, Springer-Verlag, 465 474
    • A. Blake and A. Yuille, editors, (1992). Active Vision. MIT Press.
    • A. Blake and H.H. B-ulthoff (1991). Shape from specularities: computation and psychophysics. Phil. Trans. Roy. Soc. B, 331, 237-252. PDF
    • L. Tarassenko and A. Blake (1991). Analogue computation of collision-free paths. Proc. IEEE Int. Conf. Robotics and Automation, 540-545. PDF
    • A. Blake and C. Marinos (1990). Shape from texture: estimation, isotropy and moments. J. Artificial Intelligence, 45(3), 323-380. PDF
    • A. Blake and H. B-ulthoff (1990). Does the brain know the physics of specular reflection? Nature, 343(6254):165-168. PDF
    • A. Blake and R. Cipolla (1990). Robust estimation of surface curvature from deformation of apparent contours. Proc. 1st European Conf. on Computer Vision, Springer-Verlag, 465-474.
    • R. Cipolla and A. Blake. (1990). The Dynamic Analysis of Apparent Contours. Proc. 3rd Int. Conf. Computer Vision, Osaka, Japan, 616-625. PDF
    • C. Marinos and A. Blake (1990). Shape from texture: the homogeneity hypothesis. Proc. 3rd Int. Conf. Computer Vision, Osaka, Japan, 350-334. PDF
    • A. Blake (1989). Comparison of the efficiency of deterministic and stochastic algorithms for visual reconstruction. IEEE Trans. Pattern Analysis and Machine Intelligence, 11, 1, 2-12. PDF
    • A.Blake and A. Zisserman. (1987). Visual Reconstruction. MIT Press, Cambridge, USA. PDF
    • A. Blake (1985). Boundary conditions for lightness computation in Mondrian World. Computer Vision, Graphics and Image Processing, 32, 314-327. PDF
    • A. Blake (1985). Specular stereo. Proc. Int. Joint Conf. on Artificial Intelligence 973-976.
    • A. Blake (1984). Reconstructing a visible surface. Proc. Conf. American Association for Artificial Intelligence, 23-26.
    • A. Blake (1983). The least-disturbance principle and weak constraints. Pattern Recognition Letters, 1, 393-399. PDF
    • A. Blake (1982). Fixed point solutions of recursive operations on boolean arrays. Computer Journal, 25, 2, 231-233.J.
  • Books

    Markov Random Fields for Vision and Image Processing (2011)

    Markov Random Fields for Vision and Image Processing (2011)

    Edited by Andrew Blake, Pushmeet Kohli and Carsten Rother

    Markov Random Fields for Vision and Image Processing demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications.

    Markov Random Fields for Vision and Image Processing MIT Press


    Active Contours (1998)

    Active Contours (1998)

    By Andrew Blake and Michael Isard

    Active Contours is about the computer analysis of moving video images. It develops geometric and probabilistic models for shapes and their dynamics. The models are applied to the real-time analysis of shapes in motion, and addresses issues of learning, temporal filtering and the problems of visual clutter. Numerous applications are illustrated from computer graphics animation, user-interface design, medical imaging, automated surveillance and robotics.

    Active Contours Springer-Verlag


    Active Vision (1992)

    Active Vision (1992)

    Edited by Andrew Blake and Alan Yuille

    Active Vision explores important themes emerging from the active vision paradigm at the time of publication, which had only recently become an established area of machine vision. In four parts the contributions look in turn at tracking, control of vision heads, geometric and task planning, and architectures and applications, presenting research that marked a turning point for both the tasks and the processes of computer vision.


    Visual Reconstruction (1987)

    Visual Reconstruction (1987)

    By Andrew Blake and Andrew Zisserman

    Visual Reconstruction presents a unified and highly original approach to the treatment of continuity in vision. Two concepts, which were new at the time of publication, are introduced, analyzed, and illustrated.


  • Short CV

    1974-77 Scholar in mathematics, Trinity College Cambridge.
    1977 BA Electrical Sciences, 1st class with distinction.
    1977-78 Kennedy Fellow at Massachusetts Institute of Technology, Cambridge, USA.
    1978-80 Scientist with Ferranti Edinburgh, Electro-optics group.
    1980-83 Research Associate and Ph.D. student at Edinburgh University. Awarded Ph.D. in Computer Vision, 1983.
    1983-87 Lecturer in Computer Science, Edinburgh University.
    1984-87 Royal Society Research Fellow.
    1987-96 Lecturer in Image Processing, Dept. Engineering Science, University of Oxford.
    1987-99 Fellow of Exeter College, Oxford.
    1996-9 Professor of Engineering Science, University of Oxford.
    1998-9 Royal Society Senior Research Fellow.
    1999-02 Senior Research Scientist, Microsoft Research, Cambridge.
    1999 Visiting Professor of Engineering Science, University of Oxford.
    2000 Fellow of Clare Hall, University of Cambridge.
    2002 Principal Research Scientist, Microsoft Research.
    2005 Partner, Microsoft Corporation.
    2006 Visiting Professor of Informatics University of Edinburgh.
    2007 Visiting Professor of Machine Intelligence, University of Cambridge.
    2008 Deputy Laboratory Director Microsoft Research Cambridge
    2010 Microsoft Distinguished Scientist & Laboratory Director, Microsoft Research Cambridge
    1977 Lamb prize of the University of Cambridge for Electrical Sciences.
    1977 ver Heyden der Lancey prize, Trinity College, Cambridge.
    1977-78 John F. Kennedy Fellow, MIT, USA.
    1984 Nominated for publisher-s prize at the American Association for Artificial Intelligence (AAAI) conference, in Austin, Texas.
    1984-87 Awarded the Royal Society IBM Research Fellowship.
    1986 Commendation for paper presented to the IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA.
    1987 Appointed Associate Editor of J. Image and Vision Computing, Butterworth.
    1991-92 Associate Editor of Transactions on Pattern Analysis and Machine Intelligence of the IEEE.
    1991-90 Editorial board, Vision Research, Pergamon Press.
    1991 Appointed joint organiser (with Professors D.Mumford, Harvard and B.Ripley, Oxford) of a 6 month programme in Computer Vision for 1993, at the Isaac Newton Institute, Cambridge, UK.
    1992 Winner (with R. Cipolla) of the biennial prize of the European Vision Society.
    1993 Visiting Fellowship at Clare Hall, Cambridge, July-December.
    1993 Appointed to the Editorial board, International Journal Computer Vision, Kluwer.
    1994 Elected Fellow of the Institute of Electrical Engineers.
    1994-5 Program Chairman, IEEE International Conference on Computer Vision, Cambridge, USA.
    1996 Winner (with M. Isard) of the biennial prize of the European Vision Society.
    1996 Paper (with E. Rimon) nominated for the annual prize of the IEEE Robotics and Automation society.
    1997-02 Editorial board member for Computer Vision and Image Understanding, Academic Press.
    1998 Elected Fellow of the Royal Academy of Engineering.
    1998-9 Program Chairman, IEEE International Conference on Computer Vision, Greece.
    1998-9 Royal Society Senior Research Fellow (Amersham).
    2000 Elected Fellow of Clare Hall College, Cambridge.
    2000-5 Appointed to the Technical Advisory Board of the Max Planck Institute for Biological Cybernetics, T-ubingen.
    2001-4 Appointed to the Governing Body of the BBSRC Silsoe Research Institute, UK.
    2001 Awarded (with K. Toyama) the David Marr Prize of the IEEE, for Computer Vision.
    2004 Elected the Distinguished Fellow of the British Machine Vision Association for 2004.
    2005 Awarded (with V. Kolmogorov, A. Criminisi, G. Cross, C. Rother) honourable mention for best paper at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    2005 Promoted to Partner, Microsoft Corporation.
    2005 Elected Fellow of the Royal Society.
    2006 Awarded Silver Medal of Royal Academy of Engineers.
    2007 Awarded Mountbatten Medal of the Institution of Engieering and Technology.
    2008 Elected fellow of the IEEE.
    2010 Elected to the Council of the Royal Society
    2010 Appointed Laboratory Director, Microsoft Research Cambridge
    2011 Recipient of Royal Academy of Engineering MacRobert Award
    2012 Elected to the Board of the EPSRC
  • Favourite Publications

    • A. Blake (1985). Boundary conditions for lightness computation in Mondrian World. Computer Vision, Graphics and Image Processing, 32, 314-327. PDF
    • A. Blake (1989). Comparison of the efficiency of deterministic and stochastic algorithms for visual reconstruction. IEEE Trans. Pattern Analysis and Machine Intelligence, 11, 1, 2-12. PDF
    • A. Blake and C. Marinos (1990). Shape from texture: estimation, isotropy and moments. J. Artificial Intelligence, 45(3), 323-380. PDF
    • A. Blake and H. B-ulthoff (1990). Does the brain know the physics of specular reflection? Nature, 343(6254):165-168. PDF
    • A. Blake and H.H. B-ulthoff (1991). Shape from specularities: computation and psychophysics. Phil. Trans. Roy. Soc. B, 331, 237-252. PDF
    • L. Tarassenko and A. Blake (1991). Analogue computation of collision-free paths. Proc. IEEE Int. Conf. Robotics and Automation, 540-545. PDF
    • R. Cipolla and A. Blake (1992). Surface shape from the deformation of apparent contours. Int. J. Computer Vision, 9, 2, 83-112. PDF
    • A. Blake, H.H. B-ulthoff and D. Sheinberg (1993). Shape from Texture: Ideal Observers and Human Psychophysics Vision Research, 33, 12, 1723-1737. PDF
    • A. Blake, R. Curwen and A. Zisserman (1993). A framework for spatio-temporal control in the tracking of visual contours. Int. J. Computer Vision, 11, 2, 127-145. Abstract and .ps.gz
    • A. Blake and M. Isard (1994). 3D position, attitude and shape input using video tracking of hands and lips. Proc. ACM SIGGRAPH Conference, Orlando, USA, 185-192. Abstract and.ps.gz
    • R. Brockett and A. Blake (1994). Estimating the shape of a moving planar curve. Proc. IEEE Int. Conf. Decision Theory and Control, 3247-3252. PDF
    • A. Blake (1995). A symmetry theory of planar grasp. Int. J. Robotics Research, 14, 5, 425-444. Abstract and .ps.gz
    • A. Blake, M. Isard and D. Reynard (1995). Learning to track the visual motion of contours. Artificial Intelligence, 78, 101-134. Abstract and .ps.gz
    • R. A. Eagle and A. Blake. (1995). Two-dimensional constraints on three-dimensional structure from motion tasks. Vision Research, 35, 20, 2927-2941. PDF
    • M. Isard and A. Blake (1996). Contour tracking by stochastic propagation of conditional density. Proc European Conf. Computer Vision, Cambridge, UK, 343-356. Abstract and .ps.gz
    • D. Reynard, A. Wildenberg, A. Blake and J. Marchant (1996). Learning Dynamics of Complex Motions from Image Sequences. Proc European Conf. Computer Vision, Cambridge, UK, 357-368. Abstract and .ps.gz
    • E. Rimon and A. Blake (1996). Caging 2D bodies by one-parameter, two-fingered gripping systems. Proc Int. Conf. Robotics and Automation, 1458-1464, IEEE Press. PDF
    • R. Cipolla and A. Blake (1997). Image divergence and deformation from closed curves. Int. J. Robotics Research, 16, 1, 77-96. Abstract and .ps.gz
    • M. Isard and A. Blake (1998). ICondensation: Unifying low-level and high-level tracking in a stochastic framework Proc European Conf. Computer Vision, 893-908. Abstract and .ps.gz
    • M. Isard and A. Blake (1998). A mixed-state Condensation tracker with automatic model switching. Proc 6th Int. Conf. Computer Vision, 107-112. Abstract and .ps.gz
    • J. MacCormick and A. Blake (2000). Probabilistic exclusion and partitioned sampling for multiple object tracking. Int. J. Computer Vision, 39(1), 57-71, 2000. PDF
    • B. North, A. Blake, M. Isard and J. Rittscher. (2000). Learning and Classification of Complex Dynamics. IEEE Trans. Pattern Analysis and Machine Intelligence, 22, 9, 1016-1034. PDF
    • J. Deutscher, A. Blake and I. Reid (2000). Articulated Body Motion Capture by Annealed Particle Filtering Proc. Conf. Computer Vision and Pattern Recognition, 2, 126- 133. Abstract and.ps.gz and movies
    • J. Sullivan, A. Blake, M. Isard and J. MacCormick (2001). Bayesian Object Localisation in Images. Int. J. Computer Vision, 44, 2, 111-136. PDF
    • J. Vermaak, A. Blake, M. Gangnet, and P. P-erez (2001). Sequential Monte Carlo fusion of sound and vision for speaker tracking. Proc. Int. Conf. on Computer Vision (ICCV), I:741-746. PDF
    • P. P-erez, A. Blake, and M. Gangnet (2001). JetStream: Probabilistic contour extraction with particles. Proc. Int. Conf. on Computer Vision (ICCV), II:524-531. PDF
    • K. Toyama and A. Blake (2002). Probabilistic tracking with exemplars in a metric space. Int. J. Computer Vision, 48, 9-19. PDF
    • O. Williams, A. Blake and R. Cipolla (2003). A sparse probabilistic learning algorithm for realtime tracking. Proc. Int. Conf. on Computer Vision (ICCV), 353-360. PDF
    • P. Perez, M. Gangnet and A. Blake (2003). Poisson image editing. ACM Trans. Graphics (SIGGRAPH), 2(3):313-318. PDF
    • C. Rother, V. Kolmogorov and A. Blake (2004). GrabCut – interactive foreground extraction using iterated graph cuts. ACM Trans. Graphics (SIGGRAPH), 309-314. PDF
    • C. Rother, L. Bordeaux, Y. Hamadi and A. Blake (2006). AutoCollage. ACM Trans. Graphics (SIGGRAPH), in press. PDF
    • V. Kolmogorov, A. Criminisi, A. Blake, G. Cross and C. Rother (2006). Probabilistic fusion of stereo with colour and contrast for bi-layer segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, in press. PDF
    • V. S. Lempitsky, A. Blake, C. Rother(2008).Image Segmentation by Branch-and-Mincut. Proc. Eur. Conf. on Computer Vision, 15-29. PDF
    • J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake (2011), Real-Time Human Pose Recognition in Parts from a Single Depth Image. In Proc. IEEE CVPR (Best Paper). PDF
    • D.J. Holland, A. Blake, A.B. Tayler, A.J. Sederman, L.F. Gladden (2011), A Bayesian approach to characterising multi-phase flows using magnetic resonance: Application to bubble flows. J. Magnetic Resonance, 209, 1, 83–87. PDF Download

CEO

Sir Alan Wilson

Sir Alan Wilson FBA FAcSS FRS is CEO of The Alan Turing Institute and Professor of Urban and Regional Systems in the Centre for Advanced Spatial Analysis at University College London. He is Chair of the Home Office Science Advisory Council.

He is a Cambridge Mathematics graduate and began his research career in elementary particle physics at the Rutherford Laboratory. He turned to the social sciences, working on cities, with posts in Oxford and London before becoming Professor of Urban and Regional Geography in Leeds in 1970. He was a member of Oxford City Council from 1964-1967. In the late 1980s, he was the co-founder of GMAP Ltd, a University spin-out company. He was Vice-Chancellor of the University of Leeds from 1991 to 2004 when he became Director-General for Higher Education in the then DfES. After a brief spell in Cambridge, he joined UCL in 2007. From 2007-2013 he was Chair of the Arts and Humanities Research Council; and from 2013-2015, he was Chair of the Lead Expert Group for the Government Office for Science Foresight Project on The Future of Cities. He is a Member of Academia Europaea, an FBA, an FAcSS and an FRS. He was knighted in 2001.

His research field covers many aspects of mathematical modelling of cities and the use of these models in planning. These techniques are now in common use internationally – including the use of the concept of entropy in building spatial interaction models – summarised in Entropy in urban and regional modelling (re-issued in 2011 by Routledge). These models have been widely used in areas such as transport planning, demography and economic modelling. His recent research is on the applications of dynamical systems theory in relation to modelling the evolution of urban structure in both historical and contemporary settings. This led to the laying of the foundations of a comprehensive theory of urban dynamics described in Complex spatial systems (2000). He has published over 200 papers and his recent books include The science of cities and regions (2012), his five volume Urban modelling (2012, edited), Explorations in urban and regional dynamics (2015, with Joel Dearden), Global dynamics (2016, edited) and Geo-mathematical modelling (2016, edited). He has a particular interest in interdisciplinarity and published Knowledge power in 2010; he writes the quaestio blog (www.quaestio.blogweb.casa.ucl.ac.uk).

Chair

Howard Covington

Howard is a graduate of St John’s College, Cambridge. He has a double first in theoretical physics and a distinction in post-graduate maths.

His first career was in the City as an investment banker and asset manager. He became a director of SG Warburg and then European chief executive of Wasserstein Perella, a US investment bank. He co-founded New Star Asset Management and was its chief executive until it was sold to Henderson in 2009. In 2014 he cofounded Greenrock Homes, a property developer.
His second career is in promoting mathematical sciences and action to reduce climate change.

He has been a trustee of the Science Museum, is chair of the Isaac Newton Institute for Mathematical Sciences, the UK’s national maths research institute at Cambridge, and is the inaugural chair of The Alan Turing Institute. He is vice-chair of ClientEarth, an environmental law firm, and an adviser to Preventable Surprises, a think-tank. He has written on the response of the investment industry to climate change for the Wall Street Journal, the Financial Times and Nature.

Board of Trustees

Professor John Aston

Professor of Statistics, University of Cambridge

John Aston is Professor of Statistics in the Statistical Laboratory, part of the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge. He is also Co-Director of the EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging Data (CMIH) and on the management board of the Cantab Capital Institute for the Mathematics of Information. John’s interests include all areas of Applied Statistics but particularly Statistical Neuroimaging and Statistical Linguistics. He also has an active collaboration with the Office for National Statistics. John has methodological interests amongst other things in Functional Data Analysis, Time Series Analysis, Image Analysis, Changepoint Analysis, and Spatial-Temporal Statistics. Prior to being at Cambridge, he held academic positions at the University of Warwick and at Academia Sinica. He was elected as a Fellow of the American Statistical Association in 2015.

Professor Anthony Finkelstein CBE

Chief Scientific Adviser for National Security and Professor of Software Systems Engineering at University College London

Anthony Finkelstein was appointed Chief Scientific Adviser for National Security in December 2015. He holds a Chair in Software Systems Engineering at University College London (UCL). He is a visiting professor at Imperial College London, the National Institute of Informatics, Tokyo, Japan and the University of South Australia. He was appointed Commander of the Order of the British Empire (CBE) in the Queen’s Birthday Honours, 2016.

He is a Fellow of the Royal Academy of Engineering (FREng), an elected Member of Academia Europaea (MAE) and a Fellow of the City and Guilds of London Institute (FCGI). Prior to assuming his current role, he was Dean of the UCL Faculty of Engineering Sciences and Head of UCL Computer Science. His scientific work is in the broad area of software systems development.

Professor Peter Grindrod CBE

Professor of Mathematics at the University of Oxford’s Mathematical Institute

Peter Grindrod is a Professor of Mathematics at the Mathematical Institute at the University of Oxford. He was appointed a CBE in 2005 for services to mathematics R&D, is a former member of the EPSRC Council (2000–04) and chair of the EPSRC’s User Panel. He is a former president of the Institute of Mathematics and its Applications, the UK’s professional and learned society for mathematicians (2006–08). He is also former member of BBSRC Council (2009–13). He is an independent member of the MOD Defence Scientific Advisory Council (2008– ).

He obtained a degree in maths from the University of Bristol (1981) and a PhD from the University of Dundee (1983), after which followed a short period of post doctoral research in dynamical systems and nonlinear PDEs at Dundee. Between 1984 and 1989 he worked at the Mathematical Institute at the University of Oxford, largely on both applications of modelling within physiology and biology.

In 1989 he joined a commercial consulting company working in the environmental sciences, building up a mathematical modelling group on multidisciplinary projects in the UK, Europe, US and Japan.

In 1998 he was co-founder and Technical Director of a start–up  company, Numbercraft Limited, supplying services and software to retailers and consumer goods manufacturers. He worked with all of the major grocery retailers in the UK and their largest suppliers. Numbercraft, designed as a five-year project, was acquired by Lawson Software (St Paul, US) in 2003.

Professor Richard Kenway OBE

Tait Professor of Mathematical Physics and Vice-Principal High Performance Computing, University of Edinburgh

Richard Kenway was appointed to the Tait Chair of Mathematical Physics at the University of Edinburgh in 1994. His research explores theories of elementary particles using computer simulation. As Vice-Principal, Professor Kenway is responsible for the University’s provision of UK high-performance computing services and for promoting advanced computing technologies, computational and data science to benefit academia and industry. For ten years, until it closed in 2011, his responsibilities included the UK National e-Science Centre. In the Queen’s 2008 Birthday Honours, Professor Kenway was awarded an OBE for services to science. He led the establishment of the Scientific Steering Committee of the Partnership for Advanced Computing in Europe (PRACE) and chaired it from 2010 to 2012. He is a founder member of the UK e-Infrastructure Leadership Council and a member of the Science & Technology Facilities Council.

Dr Julie Maxton

Executive Director of the Royal Society

Julie Maxton is the Executive Director of the Royal Society, the first woman in 350 years to hold the post.  Before taking up her position at the Royal Society in 2011 Julie was Registrar at the University of Oxford, the first woman in 550 years in the role.  She is an Honorary Fellow of University College Oxford, a Bencher of the Middle Temple, a Freeman of the Goldsmith’s Company, and a Board member of Engineering UK, the Charities Aid Foundation, Haberdasher Aske’s School and of the International Advisory Board of the Blavatnik School of Governance at Oxford University.  Originally trained as a barrister at the Middle Temple, Julie combined a career as a practising lawyer with that of an academic, holding a number of senior academic positions, including those of Deputy Vice Chancellor, Professor and Dean of the Faculty of Law at the University of Auckland, New Zealand.  She is the author of numerous articles concerned with trusts, equity, commercial and property law.

Wendy Tan White

General Partner, Entrepreneur First

Wendy Tan White is a General Partner at Entrepreneur First, an organisation which invests in top technical individuals to help them build world-class deep technology start-ups from scratch in London and Singapore. She joined Entrepreneur First in 2015  to further support the growth of its companies with a £40m Next Stage Fund and programme. Since 2011, Entrepreneur First has created over 100 startups worth over $400m including Magic Pony Technology, Tractable, StackHut, Pi-Top, OpenCosmos, Status Today and Cloud NC.

Before Entrepreneur First, Wendy co-founded Moonfruit, the first software-as-a-service website builder, with Joe White who is also a General Partner at Entrepreneur First and Wendy’s husband. Wendy and Joe grew the business through the dotcom crash and financial crisis before selling to Yell Group for $37m. Wendy helped launch Zopa, the first peer-to-peer lending platform, and Egg, the first Internet bank in EU. She also supports the wider UK ecosystem on the boards of TechCityUK, Government Digital Advisory, Imperial College London Department of Computing DoC and School of Design Engineering. Recently awarded an MBE for services to technology, business and women entrepreneurs. Wendy also loves design with an MA Central St Martins as well as BEng Computer Science, Imperial College.

Professor Pam Thomas

Pro-Vice-Chancellor (Research), University of Warwick

Pam Thomas is the University of Warwick’s Pro Vice-Chancellor for Research and Professor of Physics.

Pam joined Warwick in 1990, becoming a Professor in the Department of Physics in 2005. She was appointed as the inaugural Director of the Science City Research Alliance for the Universities of Warwick and Birmingham in 2009, leading this complex interdisciplinary programme between the two universities and in the wider region. She is responsible for the inter-departmental X-ray diffraction facility and leads a research group in Ferroelectrics and Crystallography within the Condensed Matter Group. She has also held a number of key administrative research and teaching roles in the department and has previously served on the Board of Graduate Studies. Most recently, she has been the first female Chair of the Board of the Faculty of Science – a role to which she was appointed in 2011.

Pam was educated at Oxford University, where she took a DPhil in the Clarendon Laboratory and undertook post-doctoral research. She has served on many national and international committees for physics, materials and crystallography and particularly for research at large central facilities such as the Diamond Synchrotron Facility in Oxfordshire and the European Synchrotron Radiation Facility in Grenoble. She currently contributes to the work of the Science and Technology Facilities’ Council (STFC) in the UK as a member of their national Science Advisory Board.

Pam works across Warwick’s departments to maintain the University’s research excellence. Pam oversees departmental and inter-faculty developments, and heads the University’s efforts to generate the impact our research deserves. The Global Research Priorities programme, for which she provides leadership, has provided the University with significant international attention.

Her other responsibilities include support for early career research staff and research students, in addition to her academic leadership on equality and diversity matters.

Dr Neil Viner

Director, Programme Delivery at EPSRC

Neil Viner is Director of Programme Delivery at EPSRC.  He currently has oversight of a portfolio of projects investing over £800M to create a number of world leading research institutes and research infrastructure in partnership with universities.

Neil is a member of The Alan Turing Institute’s audit committee, having previously been Project Director leading the establishment of the Institute. This follows several years developing EPSRC’s People strategy, including the leading the establishment of a portfolio of Centres for Doctoral Training across the UK with investment totalling nearly £1B. Prior to this, he was head of the policy and analysis team and a specialist policy advisor at the Office of Science and Technology and has held a variety of strategy and operational roles within the Research Councils.

University Liaison Directors

Professor Graham Cormode, University of Warwick

Bio

Graham Cormode is a Professor in Computer Science at the University of Warwick and The Alan Turing Institute’s University Liaison Director for Warwick. He works on research topics in data management, privacy and big data analysis. Previously, he was a principal member of technical staff at AT&T Labs-Research in the USA.  He serves as an associate editor for ACM Transactions on Database Systems (TODS), and the Journal of Discrete Algorithms.

Research

Graham will work on a variety of topics at the Turing including:

  • Algorithms for scalable analytics & streaming, sketching, dimensionality reduction, compressed sensing, with applications to internet scale data, vehicle data.
  • Data Anonymization and privacy & statistical and cryptographic approaches to privacy, primarily differential privacy, with applications to telecommunications and social data.
  • Distributed algorithms & algorithms for large scale monitoring and logging of activities, drawing on ideas from approximation and distributed ledger technologies.

Professor Zoubin Ghahramani, University of Cambridge

Bio

Zoubin Ghahramani FRS is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group, and The Alan Turing Institute’s University Liaison Director for Cambridge. He is also the Deputy Academic Director of the Leverhulme Centre for the Future of Intelligence, and a Fellow of St John’s College Cambridge. He has worked and studied at the University of Pennsylvania, MIT, the University of Toronto, the Gatsby Unit at UCL, and CMU.  He is co-founder of Geometric Intelligence and advises a number of AI and machine learning companies.  He has served as programme and general chair of the leading international conferences in machine learning: AISTATS, ICML, and NIPS. In 2015 he was elected a Fellow of the Royal Society.

Research

Zoubin’s current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, deep learning, probabilistic programming, Bayesian optimisation, and automating data science. His Automatic Statistician project aims to automate the exploratory analysis and modelling of data, discovering good models for data and generating a human-interpretable natural language summary of the analysis. He and his group have also worked on automating inference (though probabilistic programming) and on automating the allocation of computational resources.

Professor David Pym, University College London

Bio

David Pym is Professor of Information, Logic, and Security at UCL and is The Alan Turing Institute’s University Liaison
Director for UCL. Heholds a PhD in logic and theoretical computer science from Edinburgh,and an MA and an ScD in mathematics from Cambridge. He is a Fellow of the IMA and the BCS. David spent many years with Hewlett-Packard’s Research Laboratories, where he developed interests in systems, security, and economics.

Research

David will work on a range of topics in security and privacy in distributed information-processing systems. He is interested in questions about access control in distributed systens, security and privacy policy, and the economics of security management. He is also interested in understanding basic questions in distributed systems architecture and behaviour, such as consistency and the relationship between systems management policies and systems architecture. David addresses these issues using ideas and techniques from logic, theoretical computer science, probability theory, and economics. He aims to build both conceptual and implemented tools to support decision-making in systems and policy design.

Professor Jared Tanner, University of Oxford

Bio

Jared Tanner is Professor of the Mathematics of Information at the University of Oxford and The Alan Turing Institute’s University Liaison Director for Oxford. He obtained his PhD (2002) in applied mathematics at the University of California at Los Angeles, and was a postdoctoral fellow at the University of California at Davis (Maths) and Stanford University (Stats.) where he worked with David L. Donoho.  Prior to joining the University of Oxford in 2012 he was Professor of the Mathematics of Information at the University of Edinburgh (2007-2012).  He is founding editor-in-chief of Information and Inference: A Journal of the IMA, whose mission is to publish high quality mathematically oriented articles furthering the understanding of the theory, methods of analysis, and algorithms for information and data.  He is also on the editorial board for Applied and Computational Harmonic Analysis, Multiscale modelling and simulation A SIAM Interdisciplinary Journal, and was an associate editor for the Princeton Companion to Applied Mathematics.  His research has appeared in the Proc Natl Acad Sci USA, Phil Trans Royal Soc A, and other leading journals.

Research

Jared Tanner’s research concerns extracting models of high dimensional date which reveal of the essential information in the data.  Specific contributions include the derivation of sampling theorems in compressed sensing using techniques from stochastic geometry and the design and analysis of efficient algorithms for matrix completion which minimise over higher dimensional subspaces as the reliability of the data warrants.  These techniques allow more efficient information acquisition as well as the ability to cope with missing data.

Recent interests include new models for low dimensional structure in heterogeneous data and topological data analysis.

Professor Chris Williams, University of Edinburgh

Bio

Chris Williams is Professor of Machine Learning in the School of Informatics, University of Edinburgh, and is The Alan Turing Institute’s University Liaison Director for Edinburgh. He obtained his MSc (1990) and PhD (1994) at the University of Toronto, under the supervision of Geoff Hinton. He was a member of the Neural Computing Research Group at Aston University from 1994 to 1998, and has been at the University of Edinburgh since 1998.

Research

Chris is interested in a wide range of theoretical and practical issues in machine learning, statistical pattern recognition, probabilistic graphical models and computer vision. This includes theoretical foundations, the development of new models and algorithms, and applications. His main areas of research are in models for understanding time-series, visual object recognition and image understanding, unsupervised learning, and Gaussian processes. At the Turing he also has interests in improving the data analytics process, looking to address the issues of data understanding and preparation that are widely quoted as taking around 80% of the time in a typical data mining project.