Department of Computer Science

PhD Programmes in Computer Science and Engineering

The programme covers research on software, use and performance of software, as well as information and data. In particular there is research in use of software in organisations, software engineering, management of software engineering, human-computer interaction, programming and languages, data management, data analysis and data mining, techniques for decision support, machine learning, autonomous agents, networks and protocols, embedded software, techniques and models for distributed and parallel software, and software testing.

The research approach is fundamentally constructive and embraces analytical mathematical research, experimental research with algorithms, systems, techniques and methodologies, as well as analytical empirical research.

Database and programming Technologies (DPT)

The research concerns general-purpose programming languages as well as special-purpose languages, e.g., for embedded, real-time, mobile, parallel, distributed, and data-intensive systems. Studies also cover environments and tool support for program development and analysis. In the area of data management, or data-intensive systems, substantial research concerns aspects of Big Data. Central research topics include temporal, spatial, and spatio-temporal data management, web data management, and mobile data management; additional prominent topics include business intelligence, data analytics, data warehousing, data integration, OLAP, multidimensional databases, data mining, and open data.

Within these topics, the research covers modeling and database design, data models, query processing, indexing, and applications. The research approach is primarily constructive: theoretically well-founded, purposeful artefacts such as frameworks, data structures, indexes, algorithms, languages, tools, and systems are prototyped and subjected to empirical study. Further, the research is mostly driven by perceived real-world applications, with primary application areas being intelligent transport systems, web querying, logistics, energy grids, and healthcare.

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Distributed and Embedded Systems (DES)

The research concerns modelling, analysis and realization of computer programmes, with an emphasis on distributed and embedded systems. This includes the following areas: 1) Semantic theories for modelling the behaviour of computer programmes and systems; 2) Design, implementation and models for analysis of distributed systems and networks; 3) Algorithms, methods and tools for verification and validation of programmes and systems. The emphasis is on integrating these three areas. These disciplines are in particular applied to embedded software systems.

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Information Systems (S+I)

The research concerns the development and use of software systems in particular and information technology in general. The research field is development and use of software-based systems at two levels: humans and organisations:

  1. Human-computer interaction (HCI): design and evaluation of the interaction between a user and a software-based system;
  2. Systems development (SD): development and use of software-based systems in organisations.

The HCI research deals with design and evaluation of the specific interaction between a user and an interactive computerised system. This involves design of user interfaces of interactive systems for specific applications. It also involves usability evaluations, and studies in real world settings, of specific interactive systems in order to provide a basis for understanding their impact on human activity, and for improving the design of these systems. The SD research deals with systems development, the organisational use of computerised systems, software engineering management, and development and use of IT in innovation and change. The target of the research is the professional practitioner engaged in the development and use of software and information systems in the broadest sense, e.g., developers, project managers, and IT managers.

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Machine Intelligence (MI)

The research concerns intelligent reasoning and decision making under uncertainty, as well as statistical methods for machine learning and data mining. A basis for many of the research activities is graphical probabilistic models, especially Bayesian networks and influence diagrams, which allow compact representations and efficient inference algorithms for probabilistic and decision theoretic models. Bayesian networks find application in several areas, for example, as diagnostic models in technical and medical domains, for modelling genetic relationships in bioinformatics, and for modelling unknown environments in robot navigation. The research covers three main areas:

  1. probabilistic graphical models: developing efficient design and inference methods for graphical models;
  2. machine learning and data mining: statistical methods for learning, especially learning of graphical models, and methods for solving data mining problems like clustering and classification;
  3. autonomous agents: using graphical models for programming intelligent behaviour in autonomous agents, e.g., autonomous agents in computer games.
     

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Contact

 

 

 



 


Peter Axel Nielsen, Professor
Selma Lagerlöfs Vej 300, room 5-2-12
9220 Aalborg East, Denmark

Phone: 45 99 40 8912
Fax: 45 99 40 97 98

 pan@cs.aau.dk