| title | class |
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Projects and labs |
projects |
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CLARIAH (Common Lab Research Infrastructure for the Arts and Humanities) is a distributed research infrastructure for the humanities and social sciences. The L&R group has been involved since its conception, working together with humanities researchers and social scientists to build critical infrastructure tools.
The Discovery Lab—a collaboration between the Vrije Universiteit, the University of Amsterdam and Elsevier—seeks to advance the ability to construct, use and study a large-scale knowledge graph that integrates knowledge across heterogeneous scientific content and data. The knowledge graph will allow for a deeper, richer use of content and data across a larger span of domains than possible thus far.
Discovery lab researchers at L&R have been involved with developing new query embedding methods, representation learning of multimodal data, and building large scale biomedical knowledge graphs.
Graph-Massivizer researches and develops a high-performance, scalable, and sustainable platform for graph processing and reasoning. In the project, the KRR group works on graph machine learning for large graphs, using techniques like distributed graph sampling and summarization. We also contribute to graph querying and reasoning, where some correcteness is traded for a large gain in scalability. Besides, we investigates ways to work learn on graph streams without re-training the machine learning model. All of this is part of a larger whole in which performance modelling, and environmental sustainability tradeoffs are considered across the computing continuum.
The Humane AI project aims to shape the AI revolution in a direction that is beneficial to humans both individually and societally, and that adheres to European ethical values and social, cultural, legal, and political norms. It is an international project including industrial partners such as Volkswagen and Airbus, as well as academic institutions from almost all European nations.
The Hybrid Intelligence centre brings together researchers from 6 Dutch universities with the common task of using artificial intelligence to complement rather than replace human intelligence. The Hybrid Intelligence Center is funded by a 10 year Zwaartekracht grant from the Dutch Ministry of Education, Culture and Science.
The LEAPfROG project aims for a scientific breakthrough in Artificial Intelligence (AI) application in healthcare by combining machine learning and knowledge representation methods to deliver novel AI-powered tools, methods, models, and a prototype for AI-based decision support system. All to support effective and safe pharmacotherapy in people with multimorbidity. The value of LEAPfROG’s approach will be demonstrated via a clinically relevant and urgent use case of drug-induced kidney diseases (DIKD) in patients with chronic kidney disease (CKD).
MUHAI (meaning and understanding in human-centric IA), is an international project that brings together researchers from France, Germany, Belgium and the Netherlands, with the aim of understanding meaning in the context of modern AI methods.
The PersOn project, in collaboration with partners from academia and industry, is focused on developing explainable, maintainable, and trustworthy Clinical Decision Support Systems (CDSS) for personalised care in oncology. We aim to put Hybrid Intelligence challenges into practice. This involves achieving explainable Hybrid Intelligence by creating comprehensive explanations for the responses generated by the CDSSs. Additionally, we are committed to ensuring responsible Hybrid Intelligence by providing guarantees for the accuracy and safety of CDSSs responses.
The DReAMS lab is a collaboration between the Vrije Universiteit, the University of Amsterdam and Huawei. Its aim is to provide the next generation in search technology, taking into account different languages and cultures in Europe.
The mission of the lab is to ensure that the next generation of search technology should be ubiquitous, accurate, flexible, relevant, intelligent, humane and safe.
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