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Research groups

The mission of the Informatics Institute is to perform curiosity-driven and use-inspired fundamental research.

Our research involves complex information systems focusing on collaborative, data driven, computational and intelligent systems in four research themes: Artificial Intelligence, Computational Science, Data Science, and Systems and Networking.

The Informatics Institute has a growing research portfolio with a significant and increasing contribution from public-private partnerships. We will continue to develop its research portfolio, further rolling out the ICAI lab model, also to other research themes.

The Informatics Institute is structured in sixteen research groups

  • AI4Science Lab

    Artificial Intelligence for scientific discovery.
    How can we detect, classify, and predict relevant patterns in scientific data if they are hidden within large amount of non-relevant data?

    A wealth of data is continuously produced from all sorts of experimental sources in the different academic research labs. Thanks to automatization, parallelisation, high-throughput setups, high-resolution instruments, and fast networks, “big data” has become a practical issue in a wide range of experimental research projects. Analysis of these large data streams is often a grand challenge. The current surge of AI machinery provides therefore a compelling opportunity to help with the analysis of these scientific data streams.

    AI4Science Lab develops and uses machine learning techniques to discover patterns in data streams produced by experiments in a wide variety of scientific fields, ranging from ecology to molecular biology and from chemistry to astrophysics.

    AI4Science Lab connects computer science and AI experts with scientists from the different academic domains within the Faculty of Science at the University of Amsterdam.The AI4Science Lab is also connected to AMLAB, the Amsterdam Machine Learning Lab.

    Dr. ir. B. (Bernd) Ensing

    Director AI4Science Lab (HIMS)

    Dr. P.D. (Patrick) Forré

    Lab manager AI4Science Lab (Informatics Institute)

  • Amsterdam Machine Learning Lab (AMLab)

    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 Jan-Willem van de Meent. 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

    Group leader Amsterdam Machine Learning Lab (AMLab)

    Dr. J.W. (Jan-Willem) van de Meent

    Group leader Amsterdam Machine Learning Lab (AMLab)

  • Complex Cyber Infrastructure (CCI)

    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 (CSL)

    Gaining insight into complex natural phenomena
    The Computational Science Lab (CSL) is a research group within the Informatics Institute at the University of Amsterdam. CSL focuses on the information processing of complex and dynamic natural systems. Complex natural system systems consist of individual entities that interact with each other and the environment. They have mechanisms and rules by which they operate, but it is very hard to predict their behaviour. You need computational techniques to simulate the process and understand future behaviour.

    The predominant question the group tries to answer: How can you use computational techniques to make complex natural systems tractable?

    Traditional means of analysis, like mathematics, will only get you so far. If you really want to understand why the financial market will crash or a crowd will stampede, you also need computational modelling techniques. Mike Lees, chaor of the group
    Dr. M.H. (Mike) Lees

    Group leader Computational Science Lab (SCL)

  • Computer Vision research group (CV)

    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 (CV)

  • Digital Interactions Lab

    This lab is in the process of being set up.

    Digital Interactions Labwill focus on developing impactful technologies which start from a true understanding of users and their needs, and make use of the latest advances in Artificial Intelligence and the Internet of Things, etc., but which aim to be deployable in real settings because of their ease of use and low cost.

    As such, the Digital Interactions Lab will play a bridging role between the four research themes of the Informatics Institute: Artificial Intelligence, Computational Science, Data Science, and Systems and Networking. 

    Prof. dr. J.A. (Judith) Good PhD

    Group leader Digital Interactions Lab

  • Information Retrieval Lab (IRLab)

    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 (INDElab)

    Helping people manage large amounts of data
    The Intelligent Data Engineering Lab (INDElab) is a research group at the Informatics Institute of the University of Amsterdam (UvA). INDElab works on intelligent systems that help people with the preparation, management, integration, and reuse of data.

    In today’s society people are confronted with complex information all the time. INDElab tries to help people manage all this data and make sure that it is correct, transparent and usable. The predominant question that follows: How can we design and build systems to help people understand and work with data?

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

    You get really exciting problems by seeing what people in the real world are struggling with. Paul Groth, chair of the group
    Prof. P.T. (Paul) Groth

    Group leader INtelligent Data Engineering Lab (INDElab)

  • Language Technology Lab (LTL)

    Breaking down language barriers with language technology
    The Language Technology Lab (LTL) is a research group within the Informatics Institute at the University of Amsterdam. LTL focuses on information access from natural language data. Natural language is, simply put, the way humans communicate with each other in speech and text. The group’s unique angle is that they work on language independent technology.

    There are thousands of languages in the world. Analysing and translating all those languages in an automated way, would break down language barriers. The predominant question the group tries to answer: How can we represent meaning of texts and how can that be exploited for applications?

    LTL positions itself primarily in the AI research theme, with some links to the Data Science theme of the Informatics Institute.

    We strive for actual understanding of language. Not just word or pattern matching, but technology which will allow us to interact with machines far beyond small talk. Christof Monz, chair of the group
    Prof. dr. C. (Christof) Monz

    Group leader Language Technology Lab (LTL)

  • Multimedia Analytics Lab Amsterdam (MultiX)

    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 (MNS)

    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 (PCS)

    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. dr. A.D. (Andy) Pimentel

    Group leader Parallel Computing Systems (PCS)

  • Quantitative Healthcare Analysis (qurAI) group

    Designing and enabling responsible AI solutions for data analysis challenges in healthcare.
    The mission of the Quantitative Healthcare Analysis (qurAI) group is to enhance patient care by designing and enabling leading edge AI technologies in healthcare.

    The aims are:

    • Bring fundamental AI research and clinical research closer and facilitate the cross-fertilization of these fields.
    • Facilitate interdisciplinary collaboration across UvA to strengthen research and implementation of socially responsible AI in healthcare.
    • Educate the next generation of AI researchers in healthcare and the next generation of doctors that will use AI.
    • Enable responsible use of key resources (data, computational power, algorithms, clinical knowledge, clinical workflows) for the development and translation of healthcare innovations.

    QurAI is an interfaculty group embedded in the Science (Institute of Informatics) and the Faculties of Medicine (AMC, Department of Biomedical Engineering and Physics) of the University of Amsterdam.

    Prof. dr. ir. C.I. (Clarisa) Sánchez Gutiérrez

    Group leader Clarisa Sánchez Gutiérrez (Informatics Institute) group

  • Socially Intelligent Artificial Systems (SIAS) 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 (TCS)

    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 (TCS)

    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 (VIS)

    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 (VIS)