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Cognitive Systems Engineering
University of Cambridge >  Engineering Department >  Cognitive Systems Engineering
Cognitive Systems at Cambridge University
Cognitive Systems

What are Cognitive Systems?

"Cognitive systems are natural or artificial information processing systems, including those responsible for perception, learning, reasoning, decision-making, communication and action." [1]

Research Themes within the Department

Cognitive Systems Engineering is one of the major emerging themes within the Engineering Department at the University of Cambridge. Cognitive Systems Engineering is a highly interdisciplinary field, drawing from disciplines as diverse as computer science, statistics, neuroscience, engineering, and psychology. Within this theme, research in the Department encompasses a variety of topics, including:

Relevant Links



Bill Byrne
My research interests are in statistical modeling for speech and language processing. The goal of this work is to develop modeling techniques needed for human-to-machine communication and for the processing of speech and written text by autonomous systems. Specific areas of interest are novel search and estimation algorithms for speech recognition, all aspects of statistical machine translation, and applications to problems such as spoken document retrieval and speech translation.

Roberto Cipolla
Current interests include 3D shape recovery from uncalibrated images; machine learning for object detection, segmentation and recognition; computer vision for novel man-machine interfaces and the visual guidance of robots.

Tom Drummond
Research interests include applications of real-time computer vision in two areas:

i) Human Computer Interfaces (HCI) - with a particular focus on Augmented Reality (AR)

ii) Visually Guided Robotics - including flexible automation for manufacturing and control of autonomous Miniature Aerial Vehicles (MAVs).


Zoubin Ghahramani
Current research interests include Bayesian approaches to machine learning, artificial intelligence, statistics, information retrieval, bioinformatics, and computational motor control. Statistics provides the mathematical foundations for handling uncertainty, making decisions, and designing learning systems. I have recently worked on Gaussian processes, non-parametric Bayesian methods, clustering, approximate inference algorithms, graphical models, Monte Carlo methods, and semi-supervised learning.

Albert Guillén i Fàbregas
Current research interests are in the area of communication theory, information theory, coding theory, digital modulation, multiple-input multiple-output detection and signal processing techniques for communications.

Nick Kingsbury
Current research interests relate to multi-scale image and video processing methods and their application to tasks such as object recognition, image classification, object tracking and scene interpretation. In particular we are developing techniques based on dual-tree complex wavelets, which provides a multi-scale directional filter bank that closely imitates the behaviour of the human V1 visual cortex and is highly efficient to compute. We are studying what processes need to follow this front-end in order to perform reliable feature-point detection and description, and hence good robust methods of object recognition in the presence of all common sources of variation of views and lighting of an object. Various new local phase-based signal processing methods are being developed to achieve this within acceptable levels of computational complexity.

Máté Lengyel
The brain has a remarkable capacity to learn continuously about the environment and to use this knowledge flexibly to make predictions and guide its future decisions. I study learning and memory from computational, algorithmic/representational and neurobiological viewpoints. Computationally and algorithmically, I use ideas from Bayesian approaches to statistical inference to characterize the goals and mechanisms of learning in terms of normative principles and behavioral results. I also perform dynamical systems analyses of reduced biophysical models to understand the mapping of these mechanisms into cellular and network models. I collaborate very closely with experimental neuroscience groups, doing in vitro intracellular recordings, multi-unit recordings in behaving animals, in both cases associated with the hippocampus, and human psychophysical experiments on forms of structure learning in perception.

Duncan McFarlane
Current research interests [relevant to cognitive systems] include manufacturing & service applications of software agents and distributed decision making, RFID system, Bayesian approaches to valueing industrial information, probablistic methods for locating and tracking industrial objects, distributed industrial information architectures.

Ajith Kumar Parlikad
My research interests primarily lie in understanding how information associated with products can/should be managed and used throughout their lifecycle so as to improve the effectiveness with which decisions associated with them are made. In particular, I am investigating the use of Bayesian Networks as a means to model product lifecycle decisions and to quantify the value of information. The value of information problem is of particular interest since it dictates the feasibility of using emerging technologies such as RFID in product management.

Carl Edward Rasmussen
I work broadly on probabilistic inference in machine learning, including supervised, unsupervised and reinforcement learning. I'm particularly interested in solving computationally challenging problems of representing and manipulating uncertain knowledge in complex models using approximation techniques including MCMC. Recently, I have worked extensively with Gaussian process models, see also my recent book Gaussian Processes for Machine Learning (with Chris Williams).

Gerhard Reitmayr
Current research interests include the development of user interfaces for augmented reality , wearable computing, ubiquitous computing environments and the integration of these. Current directions of work are computer vision techniques for localisation and tracking of mobile augmented reality devices and for interaction methods in intelligent room environments.

Daniel Wolpert
We use theoretical, computational and experimental studies to investigate the computational principles underlying the brains control of movement. We have developed a research programme that uses computational techniques from machine learning, control theory and signal processing together with novel experimental techniques that include robotic interfaces and virtual reality systems that allow for precise experimental control over sensory inputs and task variables.

Steve Young
Interested in man-machine interfaces and machine learning; with applications in speech understanding and dialogue management ( Also interested in user simulation, emotion detection and generation, speech synthesis, and voice conversion.