Artificial Intelligence Group
The work of the Artificial Intelligence Group is multi-disciplinary,
spanning genomics and bio-informatics, computational learning theory,
computer vision, and informal reasoning. A unifying theme is
understanding multi-scale pattern recognition problems, seeking
powerful (often statistical) algorithms for modeling and solving them,
and for learning from data. The AI Group seeks to find synergies amongst
ideas based in statistics, mechanised reasoning, cognitive science,
biology, and engineering, and to develop practical applications from
Members of the AI Group engaging with the final position of the famous chess match between IBM Deep Blue and world champion Gary Kasparov. In that chess game the world changed: Artificial Intelligence finally delivered on a long-awaited promise and earned its name.
John Daugman OBE FREng
John Daugman received his degrees at Harvard University and then taught at Harvard before coming to Cambridge University, where he is Professor of Computer Vision and Pattern Recognition. He has held the Johann Bernoulli Chair of Mathematics and Informatics at the University of Groningen, and the Toshiba Endowed Chair at the Tokyo Institute of Technology. His areas of research and teaching at Cambridge include computer vision, information theory, neural computing and statistical pattern recognition. Awards for his work in science and technology include the Information Technology Award and Medal of the British Computer Society, the "Time 100" Innovators Award, and the OBE, Order of the British Empire. He has been elected to Fellowships of the Royal Academy of Engineering, the Institute of Mathematics and its Applications, the International Association for Pattern Recognition, the US National Academy of Inventors, and the British Computer Society. He has been inducted into the US National Inventors Hall of Fame.
At Cambridge he currently teaches courses in Computer Vision, Information Theory, and Mathematical Methods for Computer Science.
Research interests: computer vision, statistical pattern recognition, information theory, wavelets, and chess algorithms.
One outgrowth of his work has been iris recognition, an automatic and fast method for determining personal identity with very high confidence, by mathematical analysis of the random patterns that are visible in the iris of a person's eye from some distance. Professor Daugman's algorithms are the basis of all currently deployed iris recognition systems and have been licensed internationally, particularly for use in airports where governments allow the process to substitute for a passport. Currently the Government of India is using these algorithms to enroll and cross-compare the iris patterns of all of India's 1.3 billion citizens in a national entitlements and benefits ID system. With more than 1 billion persons registered so far and a further million enrolled each day, hundreds of trillions of iris comparisons for ID de-duplication are performed every day in India using these algorithms.
(Professor Daugman is currently unavailable to supervise PhD students.)
Sean Holden is University Senior Lecturer in Machine Learning and Fellow and Director of Studies in Computer Science at Trinity College Cambridge. He obtained his BSc in Electronic Systems Engineering from the University of East Anglia and his PhD in Information Engineering from Cambridge University. He was postdoctoral researcher at King's College London and Cambridge University Engineering Department before taking up a Lectureship in Computer Science at University College London, where he set up and ran the MSc programme in Intelligent Systems. He was appointed Lecturer in Machine Learning in Cambridge in 2002.
He currently teaches the courses Artificial Intelligence I and Artificial Intelligence II to second and third year students respectively.
Research interests: machine learning algorithms, computational learning theory, Bayesian inference and Bayes networks, planning algorithms, functional languages for machine learning, probabilistic programming languages, application of machine learning in theorem proving, drug design, retinal opthalmology and organelle proteomics.
Mateja Jamnik is University Lecturer and an EPSRC Advanced Research Fellow. Prior to this she was guest researcher in Prof. Joerg Siekmann's OMEGA Group, affiliated with the Collaborative Research Center "Resource-adaptive Cognitive Processes" at the University of Saarland, Sarbruecken, Germany, and Research Fellow in the School of Computer Science at the University of Birmingham. She completed her PhD in Prof. Alan Bundy's Mathematical Reasoning Group at the Department of Artificial Intelligence of the University of Edinburgh.
Research interests: computational modeling of human reasoning, in particular in mathematics. Artificial intelligence, automated reasoning, diagrammatic reasoning, theorem proving, proof planning, cognitive science, machine learning, human-computer interaction, knowledge representation, agent technology.
Dr Jamnik co-organises the UK network for women in computing research Women@Cl network grant funded by EPSRC: www.cl.cam.ac.uk/women
Pietro Liò is a Lecturer in Computational Biology and Director of Studies and Fellow in Computing at Fitzwilliam College. He currently teaches Bioinformatics (Algorithms in Bioinformatics), Genome Informatics II (Phylogenetic methods + Comparative Genomics) for the MPhil in Computational Biology (Department of Mathematics), and 4M8 Tripos (Cambridge-MIT initiative) Department of Engineering.
Research interests: computational and statistical modelling of molecular systems, analysis of molecular biology data (DNA and protein sequences, gene expression data, evolutionary information, proteomics), computational approaches to multiscale problems in molecular biology systems, computational molecular evolution.
See also: Natural Language Processing Group