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CISA undertakes basic and applied research and development in knowledge representation and reasoning. Through its Artificial Intelligence Applications Institute (AIAI) it works with others to deploy the technologies associated with this research.

- D K Arvind - Integration of Asynchronous Concurrent Systems, Parallel and Distributed Computation.
- Malcolm Atkinson
- Alan Bundy - Understanding the processes of mathematical reasoning and discovery, including the processes of inference, learning, analogy, proof analysis and problem formalisation. In particular, the development of "proof plans".
- Jacques Fleuriot - Mechanical geometry theorem proving, mechanization of standard and nonstandard analysis, interactive and automatic proofs.
- Michael Fourman - Formal models of digital systems, system-design tools, proof assistants, categorical semantics, propositional planning.
- Igor Goryanin - Systems biology and systems medicine including human biochemical network reconstruction, modelling of complex biological systems, microbial fuel cells and other biotechnology and bioinformatics applications
- Ewan Klein - Computational approaches to natural language semantics, syntax, prosody and phonology; spoken language processing; communicating with mobile robots and embodied devices; the Semantic Web and ontologies.
- Dave Robertson - Design and deployment of multi-agent systems; large-scale, automated design and transformation of knowledge bases and problem solvers; agent-oriented software engineering.
- Michael Rovatsos - Intelligent agents and multiagent systems: reasoning about interaction, multiagent planning, collaborative learning, combination of knowledge-based and game-theoretic techniques, social computing.
- Alan Smaill - Constructive logics and non-realist semantics; reflection principles and their application within automated reasoning systems; theorem proving in relation to programming.
- Austin Tate - Research, development and use of planning and activity management systems.
- Bonnie Webber - Question answering, bioinformatics, discourse, Natural Language semantics, knowledge representation and inference.

The Institute studies brain processes and artificial learning systems, theoretically and empirically, drawing on the disciplines of neuroscience, cognitive science, computer science, computational science, mathematics and statistics.

- Douglas Armstrong - Bioinformatics, Neuroinformatics, Behaviour Genetics and Astrobiology and Gravitational Biology
- James Bednar - Computational neuroscience of vision and other sensory systems, large-scale modelling of cortical development and function, neural simulators
- Chris Bishop - Neural networks, probabilistic graphical models.
- Nigel Goddard - Parallel computation, medical image interpretation.
- Iain Murray - Hierarchical probabilistic models, Bayesian inference, Machine Learning
- Guido Sanguinetti
- Peggy Series - Computational neuroscience, Bayesian models for perception and decision-making, neural coding, computational psychiatry.
- Richard Shillcock - Psycholinguistics, cognitive neuropsychology and cognitive modelling.
- Amos Storkey - Bayesian inference, probabilistic graphical models, machine learning,Gaussian process models, probabilistic image models, image processing/computer vision techniques, scientific data mining, machine learning methods in astronomy, bioinformatics, probabilistic methods in functional magnetic resonance imaging, e-science.
- Charles Sutton - Statistical machine learning, graphical models, probabilistic inference. Applications in natural language processing, processing of programming languages, and probabilistic models of computer system performance
- Tom Thorne
- Mark Van Rossum - Computational neuroscience. Theory and simulation of learning and memory in biological systems, coding of sensory information, effects of noise
- Chris Williams - Theoretical and practical issues in neural networks, statistical pattern recognition, probabilistic graphical models and computer vision. Prediction with Gaussian processes, image interpretation.

The ICSA is primarily concerned with the architecture and engineering of future computing systems. Its fundamental research aims are: to extend the understanding of the performance and scalability of existing computational systems; to improve the characteristics of current systems through innovations in algorithms, architectures, compilers, languages and protocols; to develop new and novel architectures and to develop new engineering methods by which future systems can be created and maintained.

- Murray Cole - Parallel algorithms, skeletal parallel programming.
- Christophe Dubach
- Bjoern Franke - Advanced automatic parallelisation, extraction of coarse-grained parallelism, dynamic methods, mapping to heterogeneous multi-core platforms. Fast instruction set simulation, just-in-time dynamic binary translation. Statistical performance prediction. Code generation for embedded processors.
- Boris Grot - Computer architecture, memory systems and interconnection networks. Architectural support for large-scale data processing. Systems with quality-of-service guarantees.
- Paul Jackson - Mechanical theorem proving, reactive systems, linear temporal logic.
- Hugh Leather
- Myungjin Lee - Computer networks, network measurement and monitoring, data centres, cloud computing
- Mahesh Marina - Wireless and mobile networking,performance evaluation, distributed systems and algorithms.
- Vijay Nagarajan - General research interests: Software/hardware collaborative techniques for enhancing performance, programmability, reliability and security of parallel architectures. Current research topics: Memory consistency, cache coherence and synchronization for scalable parallel architectures.
- Michael O'Boyle - Auto-parallelising compilers, adaptive compilation, linear program transformation.
- Paul Patras
- Ajitha Rajan
- Nigel Topham - Design and analysis of high-performance computing systems, architecture simulation tools.
- Stratis Viglas - Data management and database systems, code generation, parallel and distributed processing, storage systems.
- Philip Wadler - Programming languages, functional programming, type systems, web programming, query languages for databases, hybrid and gradual typing, Haskell, Erlang, Java, XML.

The Institute is dedicated to the computational study of language, communication, and cognition, in both humans and machines. Research areas include automatic speech and language processing, dialogue systems, models of human communication and language processing, information retrieval and presentation, and assistive technology.

- Julian Bradfield - Modal and temporal logics, model-checking, concurrency, independence logics, descriptive complexity and set theory, UML and modelling languages.
- Shay Cohen
- Michael Fourman - Formal models of digital systems, system-design tools, proof assistants, categorical semantics, propositional planning.
- Sharon Goldwater - Unsupervised learning of language (human language acquisition and machine learning), Bayesian models, cognitive modeling, morphological and phonological processing.
- Frank Keller - Probilistic models of cognition, parsing, language production, language acquisition, language vision interface, eyetracking
- Simon King - new acoustic models such as Linear Dynamical Systems for speech recognition and automatically finding suitable units to model with them; integrating speech recognition with other tasks like information extraction or dialogue move detection; the use of articulatory information for both recognition and synthesis, including applications such as join cost calculation and join smoothing for unit selection synthesis;extending my earlier work on the use of phonological/accoustic/articulatory features for ASR; applying methodsdeveloped for speech processing to the singing voice.
- Ewan Klein - Computational approaches to natural language semantics, syntax, prosody and phonology; spoken language processing; communicating with mobile robots and embodied devices; the Semantic Web and ontologies.
- Philipp Koehn - Statistical machine translation, machine learning methods to natural language texts, large-scale text processing.
- Mirella Lapata
- Alex Lascarides - Computational semantics and pragmatics, probabilistic modelling of semantics, discourse processing and lexical processing.
- Victor Lavrenko
- John Lee - Multimodal dialogue, graphics in reasoning and learning, computing and cognition in design.
- Adam Lopez
- Christopher Lucas
- Jon Oberlander - Automatic discourse generation diagrammatic reasoning and communication individual differences in interaction
- Helen Pain - Use of artificial intelligence in education: developing tools to support learning; managing tutorial skills, educational dialogue, user modelling; second language learning (ICALL); accessibility and special needs.
- Steve Renals - Speech technology, multimodal interaction, spoken language processing
- Paul Schweizer - Philosophical logic, the computational paradigm and conceptual foundations of cognitive science and AI, philosophy of mind and language
- Hiroshi Shimodaira - Trainable lifelike conversational agents, Acoustic models for automatic speech recognition, Handwriting recognition.
- Mark Steedman - Computational linguistics, artificial intelligence, formal grammar, spoken intonation, statistical parsing, spoken language processing, animated conversational agents, computational musical analysis.
- Charles Sutton - Statistical machine learning, graphical models, probabilistic inference. Applications in natural language processing, processing of programming languages, and probabilistic models of computer system performance
- Henry Thompson - Markup languages (XML, SGML) and architectures (Standoff markup, Schema languages, pipelines); Web Architecture; Philosophy of the Web.
- Bonnie Webber - Question answering, bioinformatics, discourse, Natural Language semantics, knowledge representation and inference.

Linking computational action, perception, representation, transformation and generation processes to real or virtual worlds: statistical machine learning, computer vision, mobile and humanoid robotics, motor control, graphics and visualization.

- Margarita Chli
- Maurice Fallon
- Vittorio Ferrari
- Bob Fisher - Automatics acquisition of models of: architecture, industrial parts and people, using 3D data. Range data acquisition and interpretation. Cognititve vision, including visual attention, shape model acquisition and feature extracted from log-polar images. 3D scene understanding. Computational vision.
- Michael Herrmann
- Taku Komura - Computer Animation, Computer Graphics, Character Control, Physically-based Animation, Human Modeling
- Ram Ramamoorthy
- Sethu Vijayakumar - Statistical Machine Learning, Robotics, Motor Control, Multimodal Sensory-Motor Integration and Computational Neuroscience.
- Barbara Webb - Perceptual systems for the control of behaviour, Robot models of animals. Simulation of neural circuits.

Achieving a foundational understanding of problems and issues arising in computation and communication through the development of appropriate and applicable formal models and mathematical theories.

- Stuart Anderson - Design and analysis of dependable systems, formal proof in systems development.
- Myrto Arapinis
- David Aspinall - Computer security (particularly proof-carrying code), type systems for specification and programming languages, and proof development environments.
- Malcolm Atkinson
- Julian Bradfield - Modal and temporal logics, model-checking, concurrency, independence logics, descriptive complexity and set theory, UML and modelling languages.
- Peter Buneman - Databases: data models, query languages, semistructured data, data provenance, databases and programming languages. Programming languages: functional programming and type systems. Bioinformatics and scientific databases. Mathematical phylogeny.
- Mary Cryan - Randomized algorithms, especially algorithms for sampling and counting; learning theory; algorithms for computational biology.
- Vincent Danos - Foundational approaches to quantitative biology; syntaxes for representing, modelling, and understanding large protein networks; concurrent and stochastic systems.
- Ilias Diakonikolas - Algorithms, Complexity, Learning, Game Theory.
- Kousha Etessami - In general, theoretical computer science. More specifically: automated verification, logic, algorithms and computational complexity theory, algorithmic game theory, equilibrium computation, analysis of probabilistic systems, Markov decision processes, stochastic games, automata theory, model checking, analysis of infinite-state systems, finite model theory and descriptive complexity.
- Wenfei Fan - Database theory and systems: data integration, data quality, query languages, database security, XML, distributed query processing, integrity constraints, and applications; Web services: models, verification, composition and aggregation; logic and computation.
- Michael Fourman - Formal models of digital systems, system-design tools, proof assistants, categorical semantics, propositional planning.
- Stephen Gilmore - Formal methods of program development, formal specifications, software engineering, concurrent systems.
- Andrew D. Gordon - Computer security; programming languages and their semantics and logics; probabilistic programming for machine learning
- Jane Hillston - Stochastic process algebras, Markov processes and performance modelling.
- Paul Jackson - Mechanical theorem proving, reactive systems, linear temporal logic.
- K Kalorkoti - Computational complexity, computer algebra, decision problems in group theory.
- Elham Kashefi - Models of quantum computing and their structural relations, exploring new applications, algorithms and cryptographic protocols for quantum information processing device.
- Leonid Libkin - Databases and Applications of logic in computer science.
- John Longley - Higher type computability, semantics of programming languages, program verification.
- Sebastian Maneth
- Richard Mayr - Automated verification, automata and temporal logic, model-checking and semantic equivalence checking, formal verification of real-time and probabilistic systems, infinite-state Markov chains and stochastic games.
- Gordon Plotkin - Applications of logic, especially: the denotational and operational semantics of programming languages; type-theory; domain-theoretic and categorical analyses of computation; general proof theory; the semantics of natural language; process calculi and computational biology.
- Ajitha Rajan
- Don Sannella - Algebraic specification and formal software development; correctness of modular systems; types and functional programming; resource certification for mobile code.
- Rahul Santhanam - Computational complexity theory, and applications to cryptography, game theory and learning theory; algorithms.
- Rik Sarkar - networks, protocols, algorithms, mobile computing, sensor networks, geometry.
- Alex Simpson - Category theory, domain theory, logic, type theory.
- Alan Smaill - Constructive logics and non-realist semantics; reflection principles and their application within automated reasoning systems; theorem proving in relation to programming.
- Ian Stark - Semantics of computation, functional programming, concurrency, category theory and domain theory.
- Perdita Stevens - Software engineering, concurrency, logic, verification.
- Colin Stirling - Models of concurrent computation, modal and temporal logics, verification of program properties.
- Philip Wadler - Programming languages, functional programming, type systems, web programming, query languages for databases, hybrid and gradual typing, Haskell, Erlang, Java, XML.

These staff are currently not affiliated with an Institute, but also supervise research students.

- Allan Clark
- Murray Cole - Parallel algorithms, skeletal parallel programming.
- Johanna Moore - Computational linguistics (natural language generation, dialogue, and discourse), intelligent systems for education, personalised information presentation, multi-modal interaction, user modeling, knowledge representation.
- Michael Rovatsos - Intelligent agents and multiagent systems: reasoning about interaction, multiagent planning, collaborative learning, combination of knowledge-based and game-theoretic techniques, social computing.

This article was published on Jan 6, 2014