The Amsterdam Machine Learning Lab (AMLab) does research in machine learning, artificial intelligence, and its applications to large scale data domains in science and industry. In the area of large scale modelling of complex data sources, they develop deep generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning.

The predominant question is ‘How to improve generalizability of machine learning algorithms.’ 
The results from AMLab are widely applicable, particularly in the field of AI-assisted sciences. And make a concrete impact in domains such as computational material science, chemistry and physics.

AMLab positions itself in the research theme AI with clear links to Computational Science and Data Science.

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 in the research theme Systems & Networking with clear links to AI, Computational Science, Data Science and People, Science & Technology.

Gaining insight into complex natural phenomena through computational modelling and simulation
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?

CSL positions itself in the research theme Computational Science with clear links to AI and People, Science & Technology.

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).

CV positions itself in the research theme AI with clear links to Data Science and People, Science & Technology.

Shaping the Future of Digital Life

The Digital Interactions Lab (DIL) is a research group within the Informatics Institute at the University of Amsterdam. There is a lot of potentially very exciting research taking place in areas such as AI, machine learning, natural language understanding and the Internet of Things. However, the advances in these areas are not making the impact that they might in fields such as education and healthcare. Our research bridges the gap between the technology-oriented and market-led formulation of the smart agenda with a sociological and psychological understanding of what people need artificial intelligence to be, and how data science might enhance our societies.

The predominant questions the Digital Interactions Lab tries to answer is ‘How can we ensure that advances in artificial intelligence and data science lead to concomitant advances in human values, dignity, wellbeing, and flourishing? How can interactive digital technologies address the pressing societal challenges of today, and the ones of the future, in ways which lead to real impact?’

DIL positions itself primarily in the research theme People, Science & Technology with clear links to Data Science and AI from a human-centred standpoint.

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 research theme AI with clear links to Data Science and People, Science & Technology.

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 in the research theme Data Science with clear links to AI and People, Science & Technology.

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 in the research theme AI with clear links to Data Science and People, Science & Technology.

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 research theme AI with clear links to Data Science and People, Science & Technology.

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 research theme Systems & Networking with clear links to Data Science.

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 research theme Systems & Networking with clear links to AI and Data Science.

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.

qurAI positions itself primarily in the research theme AI with clear links to Computational Science, Data Science and People, Science & Technology.

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 primarily in the research theme People, Science & Technology with clear links to AI, Computational Science, Data Science, Systems & Networking.

The Theory of Computer Science group, led by Christian Schaffner, 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.

TCS has clear links with the research themes AI, Computational Science, Data Science, and People, Science & Technology, and Systems & Networking.

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.

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 research theme AI with clear links to Data Science and People, Science & Technology.