The Informatics Institute is structured in eight research groups
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.
The Computational Science Lab, led by Peter Sloot, 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).
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).
The Federated Collaborative Networks (FCN) research group, led by Prof. Dr. Hamideh Afsarmanesh, develops high-level models to study collaborative interactions within distributed Collaborative Adaptive Systems (CAS).
We address design, development, and operating principles of CAS, with challenges including collaboration process definition based on competencies, reference modeling and analysis, engineering agent and collective behavior based on exchanged commitments and agreements, data/service composition infrastructures, intelligent decision support, and consensus on common perceptions for valuation and reward/sanction.
Today’s society has a socio-technical structure, operated by systems that primarily consist of collectives of heterogeneous technical components and autonomous actors. To compete and survive, these collectives engage their components in flexible evolving networks wherein they function and collaborate toward achieving their common goals. Units such as individuals, organizations, and intelligent devices constitute the actors in such Collaborative Adaptive Systems (CAS), which may involve different temporal and spatial scales. Typically the boundaries of a CAS are fluid, though in specific cases a CAS may involve only tightly-tangled nodes and may choose to operate as a closed-border network. Furthermore, the life time of a CAS relies on achieving its common goals, and thus while CASs are usually temporary, in certain cases they may become permanent.
Research at the FCN group is strategically concentrated on four themes:
Led by Maarten de Rijke, the Information and Language Processing Systems research group combines research on information retrieval, language technology, semi-structured data and result presentation in order to identify semantically meaningful information in large volumes of online content.
Our research is aimed at intelligent information access, especially in the face of massive amounts of information. We work on finding and analyzing content (information retrieval, machine translation, language technology), the analysis of structural information (social networks, linked data) and the analysis of user behavior (self-learning search, log analysis, user studies).
We combine fundamental, experimental and applied research, and we do so using a broad range of textual data, data from the web or enterprises, edited or user generated, or obtained from (automatic) transcriptions of audio or video. We are involved in a large number of projects with other groups, both within and outside academia. Our research is funded by NWO, KNAW, the EU and through a range of public-private partnerships.
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.
The Intelligent Sensory Information Systems research group, led by Cees Snoek, considers visual information in its many aspects. Research ranges from the perceptual and cognitive processes involved in understanding visual information, methods for learning and indexing the semantics of visual information automatically, methods for the presentation of and interaction with large visual collections and computer vision methods for human computer interaction.
The world is full of digital images and videos. In this deluge of visual information, the grand challenge is to unlock its content. This quest is the central research aim of the Intelligent Sensory Information Systems group. We address the complete knowledge chain of image and video retrieval by machine and human.
Topics of study are semantic understanding, image and video mining, interactive picture analytics, and scalability. Our research strives for automation that matches human visual cognition, interaction surpassing man and machine intelligence, visualization blending it all in interfaces giving instant insight, and database architectures for extreme sized visual collections.
Our research culminates in state-of-the-art image and video search engines which we evaluate in leading benchmarks, often as the best performer, in user studies, and in challenging applications.
The Systems and Networking Lab, chaired by Cees de Laat, conducts research on leading-edge computer systems of all scales, ranging from global-scale systems and networks to embedded devices.
Across these multiple scales our particular interest is on extra-functional properties of systems, such as performance, programmability, productivity, security, trust, sustainability and, last but not least, the societal impact of emerging systems-related technologies. Our approach to research is a practical and engineering-oriented one that regularly involves the design, implementation and maintenance of prototypical tools and proof-of-concept applications that demonstrate and promote our research results.
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.
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.