In the AI∙ML team, we have a strong focus on collaboration - both internally and externally. The team is involved in numerous national and international research projects in the field of artificial intelligence and machine learning.
Maintaining and fostering these collaborations enhances quality and impact of our research.
Below you find a list of both current and completed research projects, as well as networks and partnerships in which we are involved.
Flexible Energy Denmark (FED) is a Danish digitization project aimed at turning Danish electricity consumption flexible to enable excess power production from wind turbines and solar cells.
The European Research & Innovation project FEVER aims at demonstrating and implementing solutions that leverage the potential of flexibility in generation, consumption and storage of electricity.
In the project CLAIRE, researchers from Aalborg University attempt to improve the possibilities for understanding and controlling water in urban areas. The aim is to make certain the water does not end up in the wrong place at the wrong time.
In our daily lives, We are surrounded by cyber-physical systems. These systems comprise software and hardware components communicating with and controlling a physical reality. They include wind turbines, cars and objects as critical as pacemakers. In these systems, the use of machine learning is very widespread.
However, the use of machine learning also leads to challenges that may have fatal consequences. Professor Kim Guldstrand Larsen and research leader of DEIS has received a DKK 30 million Villum Investigator grant for a project aiming to ensure that cyber-physical systems meet requirements concerning reliability and safety to a much higher degree than is the case today.
The rapidly growing application of machine learning techniques in Cyber-Physical Systems leads to better solutions and products in terms of adaptability, performance, efficiency, functionality and usability. However, Cyber-Physical Systems are often safety critical, and the resulting need for verification against potentially fatal accidents is self-evident and of key importance.
The research aim of this project is to develop methods and tools that will enable industry to automatically synthesise correct-by-construction and near-optimal controllers for safety critical systems within a variety of domains.
Bacteria are present everywhere. They can make us ill. They can keep us healthy. In other words, bacteria have an endless range of applications. But if we are to utilize this potential to the full, we need to know a much larger part of the genome of bacteria. And therein lies the catch: Today, we know the genome of far less than one percent of bacteria in nature.
In this project researchers pool their knowledge on DNA sequencing, graph analysis and machine learning for mapping more bacterial genomes faster.
The vision of this project is to promote progress on Sustainable Development Goals (SDGs) by digitally transforming the way society accesses and communicates information about environmental impacts of projects and plans, in order to enable the best decisions towards green transition in a transparent and inclusive democratic process.
Renewable energy generation and electrification are among the top priorities all over the world. An essential part of renewable energy generation is power electronics (PE) that perform electrical energy conversion.
In this project, researchers combine AI and energy research to design and develop power electronics that can adapt to dynamic operation settings and predict failures.
Center for Data-Intensive Cyber-Physical Systems (DiCyPS) focuses on utilizing software and data from the IT management of complex physical systems for the development of smarter and more user-friendly solutions for society and individuals.
The competences of the center a.o. include experts within embedded software, Big Data and usability, who collaborate with researchers worldwide.
Since its inception 20 years ago, Business Intelligence (BI) has become a huge industrial domain and a major economic driver, and it encompasses many scientific and technological fields.
The Erasmus Mundus Joint Doctorate in Information Technologies for Business Intelligence – Doctoral College (IT4BI-DC) is designed to develop research excellence in this broad scope of fields. Its main objective is to train computer scientists who understand and help develop the strategies of modern enterprise decision makers.
Ever since the Internet came into existence, it has been a goal to make information available and accessible free of charge. In the past couple of years, we have witnessed the growing popularity of the Open Data movement and Semantic Web technologies, which have finally made large amounts of data available -- forming the Web of Data. But we are still missing technologies that actually make efficient use of it. Hence, the main goal of the now-completed project QWeb was to develop breakthrough technologies based on Semantic Web standards, such as RDF and SPARQL, that overcome this gap and answer user queries easily and efficiently over the Web of Data.
The GOFLEX project aimed at increasing the integration of renewables by applying smart grid technologies to make existing energy flexibilities usable for the grid.
The core element of the "GOFLEX technology” is an integrated platform of hard- and soft-ware building blocks, which are connected via open interfaces. The solution has been tested in a number of European demonstration sites, involving over 400 prosumers from industry, buildings and transport.
GOFLEX was the forerunner of the FEVER project
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Department of Computer Science, Aalborg UniversitySelma Lagerlöfs Vej 300
9220 Aalborg East, Denmark
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