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Research groups

The Informatics Institute is structured in twelve research groups

  • Amsterdam Machine Learning Lab

    The Amsterdam Machine Learning Lab (AMLab) conducts research in the area of large scale modelling of complex data sources. This includes the development of new methods for probabilistic graphical models and non-parametric Bayesian models, the development of faster (approximate) inference and learning methods, deep learning, causal inference, reinforcement learning and multi-agent systems and the application of all of the above to large scale data domains in science and industry ('Big Data problems').

    AMLab is co-directed by Max Welling and Joris Mooij. Other faculty in AMLab include Ben Kröse (professor at the Hogeschool Amsterdam) doing research in ambient robotics, Dariu Gavrila (Daimler) known for his research in human aware intelligence and Zeynep Akata (scientific co-director of Delta Lab and co-affiliated with Max Planck Institute for Informatics) doing research on machine learning applied to the intersection of vision and language.

    AMLab positions itself in the AI research theme, and also with clear links to the Data Science theme of the Informatics Institute.

    Prof. dr. M. (Max) Welling

    Faculty of Science

    Informatics Institute

  • Complex Cyber Infrastructure

    Market places to share data in a trustable and transparent way.
    The Complex Cyber Infrastructure (CCI) group is part of the Informatics Institute at the University of Amsterdam. CCI focuses on the complexity of man-made systems on all scales. This scale can be small, like the devices that you carry with you, or the apps they are running, or the communication protocols these apps use to interact. It can be also comprehensive, as in large systems such as data centres or multi-domain networks.

    The complexity of these systems is caused by the fact that more and more cyber infrastructure - e.g. routers, switches, the cloud - is reprogrammable nowadays. This offers many possibilities, but it also makes the equipment more difficult to operate and less transparent. Further, there is the complexity of mapping in computational terms the data sharing requirements which are defined at societal level, through legislation, organizational policies, private data-sharing agreements, and consents.

    CCI positions itself primarily in the Systems & Networking and Data Science research themes of the Informatics Institute.

    The market places for sharing data that we develop are similar to Netflix. You rent a movie, but you can’t just pass the movie on to someone else. Cees de Laat, chair of the group
    Prof. dr. ir. C.T.A.M. (Cees) de Laat

    Group leader Complex Cyber Infrastructure (CCI)

  • Computational Science Lab

    The Computational Science Lab, led by Alfons Hoekstra, tries to understand how information is processed in natural settings through the study of a large variety of dynamic multi-scale complex systems with a focus on – but not limited to – biomedicine.

    We study this 'natural information processing' in complex systems by computational modelling and simulation. An example is the spreading of the HIV virus: many processes on a large range of spatiotemporal scales play a role, from the molecular scale (e.g. the details of the entry of the virus into a cell) to the organism level (the sequence of events leading from an initial infection to the development of AIDS, and medication to keep the infection under control), and even to the population level (the actual spreading of the virus).

    We rely on a variety of modelling approaches (such as Agent Based models, Cellular Automata, Dynamic Complex Networks, particle methods, and models based on differential equations), on multiscale modelling methods that capture the transmission and transformation of information up – and down the scales, on formal methods (theories of natural information processing) and on Problem Solving Environments (workflows, visualisation, multiscale coupling libraries and e-science infrastructures for distributed multiscale computing).

    Dr. M.H. (Mike) Lees

    Group leader Computational Science Lab

  • Computer Vision research group

    The mission of the Computer Vision research group is to study core computer vision technologies and in particular colour processing, 3D reconstruction, object recognition, and human-behaviour analysis.

    The aim is to provide theories, representation models and computational methods which are essential for image and video understanding. Research ranges from image processing (filtering, feature extraction, reflection modeling, and photometry), invariants (color, descriptors, scene), image understanding (physics‐based, probabilistic), object recognition (classification and detection) to activity recognition with a focus on human‐behavior (eye tracking, facial expression, head pose, age and gender).

    Prof. dr. T. (Theo) Gevers

    Group leader Computer Vision

  • Information Retrieval Lab

    Working on search engines with a modern artificial intelligence perspective.
    IRLab focuses on bringing the right information to the right people in a fair and transparent way.

    IRLab works on data-driven methods to understand content, to analyse and predict user behaviour and to make sense of context and information, all in the setting of search engines, recommender systems and conversational assistants. Applications are ubiquitous: tools to find documents on the web, recommend products, discover music and much more.

    IRLab positions itself in the AI and Data Science research themes of the Informatics Institute.

    We develop machine intelligence and augment it with human intelligence to help people, locate, and act on, the information they need. Evangelos Kanoulas, chair of the group
    Prof. dr. E. (Evangelos) Kanoulas

    Group leader Information Retrieval Lab (IRLab)

  • INtelligent Data Engineering Lab

    The INtelligent Data Engineering Lab (INDElab), led by Prof. Paul Groth, investigates intelligent systems that support people in their work with data and information from diverse sources. This includes addressing problems related to the preparation, management, integration and reuse of data.

    We perform both applied and fundamental research informed by empirical insights into data science practice. Topics of interest include: data supply chains, data provenance, transparency, information integration, automated knowledge base population, knowledge graph construction, and data semantics.

    Prof. P.T. (Paul) Groth

    Group leader INtelligent Data Engineering Lab

  • Multimedia Analytics Lab Amsterdam

    Tackling multimedia data with AI techniques.
    Multimedia Analytics Lab Amsterdam (MultiX) is a research group within the Informatics Institute at the University of Amsterdam. The group develops artificial intelligence (AI) techniques that help people understand large collections of multimedia data. Multimedia data can be imagery, text, video, graphs, but also other informational context like geocoordinates.

    The predominant question the group tries to answer: How can you bring together all this information in a way that users get a better understanding of it? How do you combine them in a proper way? And how can you improve machine intelligence by learning from the user?

    MultiX positions itself in the Data Science and AI research themes of the Informatics Institute.

    Our group brings multimedia research together in a unique way in the Netherlands and beyond. Marel Worring, chair of the group
    Prof. dr. M. (Marcel) Worring

    Group leader Multimedia Analytics Lab Amsterdam (MultiX)

  • MultiScale Networked Systems

    Multiscale systems that make a difference.
    The MultiScale Networked Systems (MNS) group is part of the Informatics Institute at the University of Amsterdam. The group focusses its research on multiscale systems e.g. cloud systems or clusters that define themselves by their dynamic size and scale, and on the network connecting them. The MNS group explores the emerging architectures that can support emerging applications across the future internet.

    The predominant question that the group tries to answer: How can these distributed systems work as efficiently as possible? And how do these systems need to evolve to satisfy the constantly new application requirements?

    MNS positions itself primarily in the Systems & Networking research theme, and also with clear links to the Data Science theme of the Informatics Institute.

    Our digital future will encompass even larger data flows and more complex applications then what we have today. How will the systems and the internet of tomorrow look like? This is what drives our research. Paola Grosso, chair of the group
    Dr. P. (Paola) Grosso

    Group leader MultiScale Networked Systems (MNS)

  • Parallel Computing Systems

    Extra-functional behaviour of computer systems in full glory.
    The Parallel Computing Systems (PCS) group is part of the Informatics Institute at the University of Amsterdam. It is the foremost research group in The Netherlands in the field of system optimization of multi-core and multi-processor computer systems. The PCS group looks at system performance, power/energy consumption, reliability, security & safety, but also the degree of productivity to design and program these systems: the extra-functional behaviour of computer systems in full glory.

    The top research of the PCS group is indispensable for developments within, for example, Artificial Intelligence. In order to be able to cope with the increasingly demanding calculations in computer science, it is essential that computer systems become faster and more efficient. Without the skills of researchers within computer systems, AI, amongst others, was certainly not where it is today.

    PCS positions itself primarily in the Systems & Networking research theme, and also with clear links to the AI and Computational Science themes of the Informatics Institute.

    Our research in the combination of extra-functional behaviour and parallel systems on one chip or one machine is unique in The Netherlands, and even beyond. Andy Pimentel, chair of the group
    Prof. A.D. (Andy) Pimentel

    Group leader Parallel Computing Systems (PCS)

  • Socially Intelligent Artificial Systems group

    Advancing society through inclusive AI technology.
    The Socially Intelligent Artificial Systems (SIAS) group is part of the Informatics Institute at the University of Amsterdam. The group focuses on civic-centered and community-minded artificial intelligence (AI) that aims to reduce inequality and promote equal opportunity in society.

    SIAS arose out of the concern that AI is increasing inequality in society. The predominant question the group tries to answer: How can we use AI, and in particular learning systems, to advance society? And how can we do that in such a way that people from all corners of society benefit from it?

    SIAS positions itself in the AI and Data Science research themes of the Informatics Institute, with clear links to the Computational Science and Systems & Networking themes.

    AI should be working for people, instead of people working for AI. Sennay Ghebreab, chair of the group
    Dr. S. (Sennay) Ghebreab

    Group leader Socially Intelligent Artificial Systems (SIAS) group

  • Theory of Computer Science

    The Theory of Computer Science group, led by Alban Ponse, is concerned with the development of theoretical foundations of computer science, based on logic and mathematics.

    The aim is to seek greater understanding of fundamental computational techniques and their inherent limitations. The emphasis is not only on the abstract aspects of computing, but also on the application of theory in the field of computer science.

    The focus is on developing theory and tools in the field of algebraic specification which can be used to specify, analyse, and verify concurrent communicating and programmed systems.

    Dr. A. (Alban) Ponse

    Group leader Theory of Computer Science

    Qualitative Reasoning group

    Bert Bredeweg leads a subgroup on Qualitative Reasoning, that focuses on the development tools and expertise that supports the acquisition of a conceptual understanding of dynamic systems through conceptual modelling and simulation. The two most notable applications of this technology are in science education and science.

    Dr. B. (Bert) Bredeweg

    Group leader

  • Video & Image Sense Lab

    We make sense of video and images with artificial and human intelligence. The lab studies computer vision, deep learning and cognitive science. We are based at the Informatics Institute of the University of Amsterdam.

    The VIS Lab embeds four public-private AI labs. QUVA Lab with Qualcomm, Delta Lab with Bosch, Atlas Lab with TomTom and AIM Lab with the Inception Institute of Artificial Intelligence. Spin-off's from the lab include Kepler Vision Technologies and Ellogon.ai.

    VIS positions itself in the AI research theme, and also with clear links to the Data Science theme of the Informatics Institute.

    Prof. dr. C.G.M. (Cees) Snoek

    Group leader Video & Image Sense Lab