Department of Computer Science, AAU

WE MAKE KNOWLEDGE MATTER

TURNING QUESTIONS INTO ANSWERS

LEADING IN COMPUTER SCIENCE

At the Department of Computer Science at Aalborg University, we strive to be internationally recognised as a leader in computer science - a department with coherent activities within research, education, development and industry collaboration. And we have come far.

We conduct world-class research in areas ranging from computers and programming to software and computer systems. The department consists of four research groups, all delivering outstanding contributions to their field of research.

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The department offers a broad range of educational programmes within computer science at both undergraduate and postgraduate levels, as well as continued professional development. The research approach encompasses the formally logical, the experimentally constructive, and the empirically descriptive.

In 2020 we launched our first educational programme in Copenhagen, and we continue to expand both research and education out of AAU Sydhavn.

BACHELOR’S PROGRAMMES

Computer Science

MASTER’S PROGRAMMES

Computer Science

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STUDENTS SCIENTIFIC STAFF SUPPORT STAFF

Data Science and Machine Learning

Information Technology

Interaction Design

Software

Data Science and Machine Learning

Interaction Design

Digitalisation and Application Development

Software

RESEARCH EDUCATION COLLABORATION

At the Department of Computer Science, leading researchers work to address major scientific challenges and develop solutions that contribute to a more sustainable society. This is done across four research groups that together create knowledge in mutually binding collaborations with both internal and external partners.

We provide high-quality, problembased education in the broader area of computer science. We are recognised for producing highly skilled graduates with very high employability, who are aware of their competencies and experienced in project work solving real-world problems.

We are acknowledged and respected for close collaboration with industry in applying research to create impact and value for society, in particular within the area of sustainable development. We are recognized as an institution capable of engaging in agile and interdisciplinary projects, creating and building on synergies between applied and foundational research and problem-based education.

OUR MOST IMPORTANT OBSERVATION IS THAT THE DEPARTMENT OF COMPUTER SCIENCE PERFORMS EXCELLENTLY IN DENMARK, EUROPE AND WORLD-WIDE IN TERMS OF PUBLICATIONS, VISIBILITY, INDUSTRIAL COLLABORATION, START-UPS, AND SOCIETAL IMPACT WITH RESPECT TO THE SELECTED SDGS.

CORE RESEARCH AREAS

• Embedded Software Systems

• Cyber-physical systems

• Artificial Intelligence and machine learning

• Data-intensive Systems

• Big data

• Analytics for spatio-temporal and multi-dimensional data

• System development

• Human-AI interaction

• Human-centred computing

• Interaction design

• Internet of Things (IoT)

RESEARCH GROUPS AND INTERDISCIPLINARY TEAMS

DISTRIBUTED, EMBEDDED AND INTELLIGENT SYSTEMS

The Distributed, Embedded and Intelligent Systems research group covers mathematical foundation, verification tools, validation methodologies, probabilistic graphical models and machine learning focusing on distributed embedded and intelligent systems.

HUMAN-CENTERED COMPUTING

The Human-Centered Computing group focuses on research in designing, developing, and using interactive computer systems. The research approach is empirical and experimental.

DATA ENGINEERING, SCIENCE AND SYSTEMS

The Data Engineering, Science and Systems group advances value creation from data by conducting high-quality research in data engineering, data science, and data systems.

DATA, KNOWLEDGE AND WEB ENGINEERING

The Data, Knowledge and Web Engineering group covers data and knowledge engineering, data science, advanced machine learning methods, and web science.

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

The team Artificial Intelligence and Machine Learning (AI∙ML) gathers researchers from four research units at the Department of Computer Science, Aalborg University, all working with different aspects of AI and machine learning.

COMPUTER SCIENCE, CPH

CS∙CPH - Computer Science, Copenhagen is responsible for implementing and running the department’s new software education on Aalborg University’s Copenhagen Campus. The team comprises members from all research groups.

DIGITAL ENERGY DEPARTMENT OF COMPUTER SCIENCE

Digital solutions are essential to provide an efficient and flexible energy system that increases the use of renewable energy.

Key research areas at the Department of Computer Science at Aalborg University (CS) offer a wide range of foundational technologies and methods that – in close collaboration with domain experts and colleagues from other scientific disciplines – support the development of such a sustainable energy system.

SELECTED TOPICS WITHIN DIGITAL ENERGY

CORE AREAS

• Data Engineering, Science and Systems

• Data, Knowledge and Web Engineering

• Distributed, Embedded and Intelligent Systems

• Human-Centered Computing

• AI and Machine Learning

ECOSYSTEM FOR A WORLD CLASS DIGITALIZED ENERGY SYSTEM

Digitalization provides solutions for many challenges that energy systems and industries are facing and makes energy systems more intelligent, resilient, and sustainable. Although digital adoption in the Danish energy companies is increasing, it is not increasing fast enough. This poses a risk to the companies’ ability to compete in the global economy.

Researchers from CS are part of the national Digital Energy Hub, which aims to provide Danish companies with skills, methods, and tools to utilize big data, AI, digital twins and IoT in their products, services, and processes.

This is done through a series of activities matching companies with similar needs and relevant technological expertise.

KEYWORDS: BIG DATA, AI, DIGITAL TWINS AND IOT

Project: Digital Energy Hub

DIGITAL TRANSFORMATION OF DISTRICT HEATING

Many public organisations are taking action to reduce their carbon footprint by, for example, transitioning to more sustainable energy forms.

In this process, digital transformation is reshaping the way citizens interact with the public sector and its underlying physical, digital and human infrastructure. Researchers from CS have been part of a project studying the transformation of district heating in Aalborg municipality.

This included a two-year action case study of a district heating provider and their ongoing work to make consumers engage in and change their consumption to better support sustainable and renewable heating sources.

KEYWORDS: DIGITAL TRANSFORMATION AND ACTION CASE STUDIES

Project: Synchronizing energy consumption with energy production

DIGITALISATION TO TURN BUILDINGS ENERGY-SMART

Society is undergoing comprehensive digitalisation. But digitalisation has not yet made a significant entry into our buildings. In the European project domOS, researchers from CS develop a joint platform to ensure that devices and sensors in our homes can be controlled and interact intelligently using common standards.

This means developing models of data and devices (ontologies) and safe methods for publishing data without compromising privacy. The researchers also design, implement and evaluate a series of services, incl. the FlexOffer concept, developed at AAU, which models, aggregates, and optimises energy demand and flexibility for all types of processes and devices.

KEYWORDS: DATA SCIENCE, BIG DATA, ENERGY FLEXIBILITY AND IOT

Project: domOS – Operating system for Smart Services in Buildings

Project: EXPLAIN-ME: Learning to collaborate via explainable AI in medical education

PROJECTS WITHIN DIGITAL ENERGY

Bus driving support system

Kim Guldstrand Larsen, Mikael B. Skov and Anders R. Bruun with the Department of the Built Environment (AAU), NT, AKK Kollektiv Trafik, AKK Plusbus, MultiQ and Keolis

Cost Efficient heat pumps using predictive DigitAltwins and Reinforcement learning (CEDAR)

Peter Gjøl Jensen

Digital Energy Hub

Kim Guldstrand Larsen, Torben Bach Pedersen and Peter Gjøl Jensen with Center Denmark, Energy Cluster Denmark, DigitalLead, Innovation Centre Denmark, SDU, DTU Compute and AU plus strategic partners: Energinet, EWII and KMD

domOS: Operating System for Smart Services in Buildings

Arne Skou, Torben Bach Pedersen, Brian Nielsen and Christian Thomsen with HES-SO, CSEM, FENIX TNT, EDF, Aliunid, Neogrid Technologies, Suntherm, Oiken, Inea and Aalborg Forsyning

DREAMS: Digitally supported Environmental Assessment for Sustainable Development Goals

Peter Axel Nielsen, Katja Hose, Ashna Mahmood Zada, Johannes Bjerva and Nicolai Brodersen Hansen with DCEA, Danish Environmental Portal, DTU Compute, SDU, The Ministry of Environment and Food of Denmark, Danish EPA, BaneDanmark, Cowi, Rambøll, DinGeo, Instituto Superior Tecnico, The Danish Road Directorate, EnergiNet and The Copenhagen Metro

FEVER: Flexible Energy Production, Demand and Storage-based Virtual Power Plants for Electricity Markets and Resilient DSO Operation

Arne Skou, Torben Bach Pedersen, Mikael B. Skov, Rikke Hagensby Jensen and Dimitrios Raptis with B.A.U.M, CERTH/ITI, Es-Geht, Estabanell y Pahisa Energia, Estabanell y Pahisa Mercator, FOSS, FlexShape, HEnEx, INEA,

Intracom Telecom, Stadtwerk Haßfurt, SWW Wunsiedel, Universitat de Girona, CitCea, UCLouvain and University of Patras

Light-AI for Cognitive Power Electronics

Bin Yang with AAU Energy

RACE - Realtids AI Computing i Energisektoren

Kim Guldstrand Larsen and Peter Gjøl Jensen with AAU Energy, Energy Cluster Denmark, Develco, Agerkranz Controls and Brønderslev Forsyning

S4OS: Scalable analysis and Synthesis of Safe, Secure and Optimal Strategies for Cyber-Physical Systems

Kim Guldstrand Larsen, Martijn Goorden, Martin Kristjansen, Andreas Holck Høeg-Petersen, Asger Horn Brorholt, Jonas Hansen, Mathias Claus Jensen, Muhammed Naeem, Nikolaj Jensen Ulrik, Rasmus Tollund, Sean Kristian Remond Harbo, Thomas Grosen, Falke Carlsen, Imran Riaz Hasrat and Nick Østergaard

Synchronizing energy consumption with energy production

John Stouby Persson and Peter Axel Nielsen with the Department of Sustainability and Planning (AAU), the Department of Architecture Design and Media Technology (AAU), Watts, and Aalborg Forsyning

TECH4CE: TECH Centre for Circular Economy

Bent Thomsen with the Department of Electronic Systems (AAU), the Department of Planning (AAU) and the Department of Architecture, Design and Media Technology (AAU)

Want to know more? Contact research leaders below

DATA ENGINEERING, SCIENCE AND SYSTEMS (DESS)

Christian S. Jensen csj@cs.aau.dk

Torben B. Pedersen tbp@cs.aau.dk

Bin Yang byang@cs.aau.dk

DISTRIBUTED, EMBEDDED AND INTELLIGENT SYSTEMS (DEIS)

Kim Guldstrand Larsen kgl@cs.aau.dk

Jiri Srba srba@cs.aau.dk

DATA, KNOWLEDGE AND WEB ENGINEERING (DKW)

Katja Hose khose@cs.aau.dk

Torben Larsen tola@cs.aau.dk

HUMAN-CENTERED COMPUTING (HCC)

John Stouby Persson

john@cs.aau.dk

Niels van Berkel

nielsvanberkel@cs.aau.dk

DIGITAL HEALTH DEPARTMENT OF COMPUTER SCIENCE

Digitalisation and the increasing amount of health data offer huge opportunities within the prevention, detection, monitoring and treatment of health conditions.

Key research areas at the Department of Computer Science at Aalborg University (CS) provide a wide range of technologies and methods that support the development of more precise diagnostics and better treatment.

SELECTED TOPICS WITHIN DIGITAL HEALTH

CORE AREAS

• Data Engineering, Science and Systems

• Data, Knowledge and Web Engineering

• Distributed, Embedded and Intelligent Systems

• Human-Centered Computing

• AI and Machine Learning

PLAYING AGAINST COVID-19 USING AGENT-BASED MODELS

What happens when cities experience COVID-19 flare-ups? Would closing schools and directing parents to work from home have any effect?

Agent-based models allow for easy capturing and analysis of different scenarios on a very detailed level.

With the world-renowned tool UPPAAL, researchers from CS have modelled, predicted, and controlled the spread of COVID-19 based on, among other things, evaluation and comparison of various lock-down measures, the risk of viral exposure, the impact of super-spreader events and the use of tracing apps.

DEEP LEARNING TO SUPERCHARGE GENOME RECOVERY

Microbial communities play a vital role in most processes in the biosphere and are essential for solving numerous challenges, incl. developing new antibiotics.

Together with colleagues, researchers from CS have demonstrated how the integration of long-read DNA sequencing and graph-based deep learning can go beyond the current state of the art in bacterial genome recovery and metagenomic binning.

The researchers will continue this work and develop technologies to integrate external data, provide novel analyses, and support microbial genome data and metagenomic binning at an unprecedented scale.

EXPLAINABLE AI IN MEDICAL EDUCATION

Medical errors can lead to fatalities - often due to clinicians working alone with minimal supervision.

Researchers from CS are part of a project aiming to support human-AI collaboration in surgery training.

Part of this work is assessing and developing robotic surgeons’ skills in combination with AI-based decision support including how to present AI suggestions in a timely manner and handle disagreement between the human expert and the AI-system.

KEYWORDS: MODELLING, ANALYSIS AND CONTROL OF DYNAMIC SYSTEMS

Project: Agent-based models for forecasting and for assessment of interventions

KEYWORDS: DATA ENGINERING, GRAPH ANALYTICS AND MACHINE LEARNING

Projects: Illuminating Microbial Dark Matter through Data Science and Data Science meets Microbial Dark Matter

KEYWORDS: HUMAN-CENTERED AI AND EXPLAINABLE AI

Project: EXPLAIN-ME: Learning to collaborate via explainable AI in medical education

Project: EXPLAIN-ME: Learning to collaborate via explainable AI in medical education

PROJECTS WITHIN DIGITAL HEALTH

Agent-based models for forecasting and for assessment of interventions

Kim Guldstrand Larsen, Peter Gjøl Jensen, Danny Poulsen, Marco Muniz and Kenneth Yrke with the Department of Electronic Systems (AAU)

Biochemical Reaction Networks

Max Tchaikowsky with Oxford University

Data engineering and artificial intelligence for personalized predictions in health care (ARISTOTELES)

Katja Hose and Tomer Sagi with Aalborg University Hospital, Università degli Studi di Modena e Reggio Emilia, The University of Liverpool, Liverpool John Moores University, and many more

DarkMatter: Data Science meets Microbial Dark Matter

Katja Hose and Thomas Dyhre Nielsen with Centre for Microbial Communities (AAU)

EXPLAIN-ME: Learning to collaborate via explainable AI in medical education

Mikael B. Skov, Niels van Berkel and Naja K. Kollerup Als with NordSim AUH, CAMES at Rigshospitalet, Zealand University Hospital, DTU, KU and RUC

Finding causalities and temporal patterns in temporal health data

Nguyen Ho, Van Long Ho and Torben Bach Pedersen with Stockholm University

Forensic age assessment of porcine granulation tissue

Kristian G. Olesen with Kristiane Barington (KU)

Identification of Home Care Recipients in Risc of Hospitalization

Kristian G. Olesen with HUGIN Expert, Hjørring Municipality, Treat Systems and Data Proces

Illuminating Microbial Dark Matter through Data Science

Katja Hose and Thomas Dyhre Nielsen with Centre for Microbial Communities (AAU)

Improved utilization of the health care system’s capacity in the North Jutland region

Emil Riis Hansen and Thomas Dyhre Nielsen with BI og analyse and The North Denmark Region

Improving Sleep Quality Using Sound Intervention

Anders Bruun and Shagen Djanian with SoundFocus

ISOBEL: Interactive Sound Zones for Better Living

Jesper Kjeldskov, Peter A. Nielsen, Mikael B. Skov, Rune M. Jacobsen and Kasper F. Skov with Bang&Olufsen, Wavecare ApS, SoundFocus ApS and Department of Electronic Systems (AAU)

Knowledge discovery in evolving biomedical ontologies

Daniele Dell’Aglio with VU Amsterdam and UZH

Mining a graph representation of COVID patients and infections for emerging patterns

Katja Hose and Tomer Sagi with Centre for Microbial Communities (AAU), the Danish Covid-19 Genome Consortium (DCGC) incl. Aalborg University Hospital, Hvidovre Hospital and Statens Serum Institut

Patient risk stratification based on pre-hospital data

Katja Hose, Tomer Sagi and Emil Riis Hansen with Aalborg University Hospital, Forskningens Hus and The North Denmark Region

Predicting Waiting Time at the Emergency Department of Aalborg University Hospital

Kristian G. Olesen with HUGIN Expert, Aalborg University Hospital and Data Proces

Privacy-preserving publication of health data using differential privacy and knowledge graph technologies

Daniele Dell’Aglio with Hong Kong University and UZH

Privacy-preserving synthetic data generation of multimodal patient data

Katja Hose, Daniele Dell’Aglio and Antheas Kapenekakis with Aalborg University Hospital and Athena Research Centre

Private and secure data exchange between health care insurances

Daniele Dell’Aglio with UZH

S4OS: Scalable analysis and Synthesis of Safe, Secure and Optimal Strategies for Cyber-Physical Systems

Kim Guldstrand Larsen, Martijn Goorden, Martin Kristjansen, Andreas Holck Høeg-Petersen, Asger Horn Brorholt, Jonas Hansen, Mathias Claus Jensen, Muhammed Naeem, Nikolaj Jensen Ulrik, Rasmus Tollund, Sean Kristian Remond Harbo, Thomas Grosen, Falke Carlsen, Imran Riaz Hasrat and Nick Østergaard

Statistical Model Checking for Biological Systems

Kim Guldstrand Larsen, Danny Poulsen and Marius Mikučionis

Using machine learning to profile patients from their medication history

Katja Hose, Tomer Sagi and Emil Riis Hansen with Aalborg University Hospital and the University of Liverpool

Using probabilistic machine learning for risk stratification and prognosis of patients with Hospital-Acquired Infections

Thomas Dyhre Nielsen with The Centre for Clinical Research, North Denmark Regional Hospital

DATA ENGINEERING, SCIENCE AND SYSTEMS (DESS)

Christian S. Jensen csj@cs.aau.dk

Torben B. Pedersen tbp@cs.aau.dk

Bin Yang byang@cs.aau.dk

DISTRIBUTED, EMBEDDED AND INTELLIGENT SYSTEMS (DEIS)

Kim Guldstrand Larsen kgl@cs.aau.dk

Jiri Srba srba@cs.aau.dk

DATA, KNOWLEDGE AND WEB ENGINEERING (DKW)

Katja Hose

khose@cs.aau.dk

Torben Larsen tola@cs.aau.dk

HUMAN-CENTERED COMPUTING (HCC)

John Stouby Persson john@cs.aau.dk

Niels van Berkel nielsvanberkel@cs.aau.dk

GREEN TRANSITION DEPARTMENT OF COMPUTER SCIENCE

The green transition depends on solutions that involve many different scientific disciplines. The research at the Department of Computer Science at Aalborg University (CS) is collaborative and interdisciplinary.

It contributes foundational technologies and methods within areas like data science, data engineering, machine learning, advanced modelling, and interaction design that support the green transition within sectors such as transport and energy.

CORE AREAS

• Data Engineering, Science and Systems

• Data, Knowledge and Web Engineering

• Distributed, Embedded and Intelligent Systems

• Human-Centered Computing

• AI and Machine Learning

SELECTED TOPICS WITHIN GREEN TRANSITION

BUILDING A DATA WAREHOUSE TO INCREASE THE POSSIBILITIES WITH GEOLOCATION DATA

Geolocation data, e.g., GPS data, hold the potential to enable safer, greener and more cost-effective and predictable mobility.

However, incomplete, noisy and poorly structured data render it challenging to analyse and extract value from the data. Researchers from CS are leading a collaboration aiming at building data foundations and a data warehouse to better enable value extraction.

One goal is to develop a data analysis software stack that allows users, e.g., municipalities, to enter and analyse their own transportation-related data.

KEYWORDS: DATA MANAGEMENT AND AI

Project:Mobility Analytics using Sparse Mobility Data and Open Spatial Data

DIGITAL CHANGE AND TRANSFORMATION OF ENVIRONMENTAL ASSESSMENTS

Environmental assessments (EAs) are applied worldwide as a decision support tool in developing projects and plans. However, current EA practices have shortcomings.

Researchers from CS are part of the DREAMS project, working to promote SDGs by digitally transforming how society accesses and communicates information about environmental impacts of projects and plans.

CS contributes expertise in knowledge management, extracting information from documents, search along with knowledge of digitalisation, diverse users’ needs and tool requirements.

KEYWORDS: KNOWLEDGE MANAGEMENT, NLP, INFORMATION RETRIEVAL AND DIGITALIZATION

Project: DREAMS: Digitally supported Environmental Assessment for SDGs

REDUCING THE COSTS AND ENERGY USED FOR HEAT PUMPS

Heat pumps are gaining popularity as a cost-effective alternative to oil and gas furnaces in homes.

However many heat pump systems are limited in their ability to automatically adjust their operation to accommodate for changes in e.g. weather and fluctuating energy prices.

The CEDAR project aims to cut homeowner costs through efficient control of heat pumps and shifting energy use to low-cost, green periods. Using digital twin, simulation, and AI tech, CEDAR offers an innovative system that responds to weather, electricity prices, and residential behavior.

KEYWORDS: REINFORCEMENT LEARNING AND MODEL CHECKING

Project: Cost Efficient heat pumps using predictive DigitAltwins and Reinforcement learning (CEDAR)

Project: EXPLAIN-ME: Learning to collaborate via explainable AI in medical education

PROJECTS WITHIN GREEN TRANSITION

ABRA: Artificial Biology, Robotics and Art

Timothy Robert Merritt with the Research Laboratory for Art and Technology (AAU), the Department of Architecture, Design and Media Technology (AAU), the Department of Materials and Production (AAU), Aalto University, Trento University and ADES

Bus driving support system

Kim Guldstrand Larsen, Mikael B. Skov and Anders R. Bruun with the Department of the Built Environment (AAU), NT, AKK Kollektiv Trafik, AKK Plusbus, MultiQ and Keolis

Cost Efficient heat pumps using predictive DigitAltwins and Reinforcement learning (CEDAR)

Peter Gjøl Jensen

CLAIRE: ControLing wAter In an uRban Environment

Kim Guldstrand Larsen, Thomas Dyhre Nielsen, Jiri Srba and Martijn Goorden with the Department of the Built Environment, AAU

Data Science meets Microbial Dark Matter

Katja Hose and Thomas Dyhre Nielsen with Centre for Microbial Communities (AAU)

Data and sustainable food: An HCI perspective

Martin Lindrup, Mikael B. Skov and Dimitrios Raptis

DREAMS: Digitally supported Environmental Assessment for Sustainable Development Goals

Peter Axel Nielsen, Katja Hose, Ashna Mahmood Zada, Johannes Bjerva and Nicolai Brodersen Hansen with DCEA, The Danish Environmental Portal, DTU Compute, SDU, The Ministry of Environment and Food of Denmark, Danish EPA, BaneDanmark, Cowi, Rambøll, DinGeo, Instituto Superior Tecnico, The Danish Road Directorate, EnergiNet and The Copenhagen Metro

FEVER: Flexible Energy Production, Demand and Storage-based Virtual Power Plants for Electricity Markets and Resilient DSO Operation

Arne Skou, Torben Bach Pedersen, MIkael B. Skov, Rikke Hagensby Jensen and Dimitrios Raptis with B.A.U.M, CERTH/ ITI, Es-Geht, Estabanell y Pahisa Energia, Estabanell y Pahisa Mercator, FOSS, FlexShape, HEnEx, INEA, Intracom Telecom, Stadtwerk Haßfurt, SWW Wunsiedel, Universitat de Girona, CitCea, UCLouvain, and University of Patras

Illuminating Microbial Dark Matter through Data Science

Katja Hose and Thomas Dyhre Nielsen with Centre for Microbial Communities (AAU)

Mobility Analytics using Sparse Mobility Data and Open Spatial Data

Christian S. Jensen, Kristian Torp, Kasper Fromm Pedersen and Bin Yang with the Department of Computer Science (AU), The Alexandra Institute and The Maersk Mc-Kinney Moller Institute, SDU

MobiSpaces - New Data Spaces for Green Mobility

Kristian Torp, Christian S. Jensen and Tianyi Li with AIT, AMT, Atos, Bosch, COMMpla, Danish Geodata Agency, DigiSys4eU, emisia, ENGINEERING, FREQUENTIS, Fujitsu, GFT,

DATA ENGINEERING, SCIENCE AND SYSTEMS (DESS)

Christian S. Jensen csj@cs.aau.dk

Torben B. Pedersen tbp@cs.aau.dk

Bin Yang byang@cs.aau.dk

DISTRIBUTED, EMBEDDED AND INTELLIGENT SYSTEMS (DEIS)

Kim Guldstrand Larsen kgl@cs.aau.dk

Jiri Srba srba@cs.aau.dk

LeanXcale, MarineTraffic, Netcompany intrasoft, NetU, OKYS, Siemens, Trust-IT Services, UBITECH. ULB, University of Piraeus Research Cener, UNPARALLEL and White Label Consultancy.

Multimodal Data Processing of Earth Observation Data

Kristian Torp and Christian S. Jensen with The Alexandra Institute and The Maersk Mc-Kinney Moller Institute, SDU, Danish Environmental Protection Agency, GEO and The Danish Geodata Agency

ODA: Open Data for Sustainability Assessment

Katja Hose, Christian Thomsen, Emil Riis Hansen and Matteo Lissandrini with the Department of Planning

optiTruck

Kristian Torp and Kasper Fromm Pedersen with Ertico, Ford Otosan, IAV, Eliadis Transport, Codognotto, CERTH/HIT, ICOOR, ISBM, University of Leeds and OKAN

RACE - Realtids AI Computing i Energisektoren

Kim Guldstrand Larsen and Peter Gjøl Jensen with AAU Energy, Energy Cluster Denmark, Develco, Agerkranz Controls and Brønderslev Forsyning

S4OS: Scalable analysis and Synthesis of Safe, Secure and Optimal Strategies for Cyber-Physical Systems

Kim Guldstrand Larsen, Martijn Goorden, Martin Kristjansen, Andreas Holck Høeg-Petersen, Asger Horn Brorholt, Jonas Hansen, Mathias Claus Jensen, Muhammed Naeem, Nikolaj Jensen Ulrik, Rasmus Tollund, Sean Kristian Remond Harbo, Thomas Grosen, Falke Carlsen, Imran Riaz Hasrat and Nick Østergaard

Synchronizing energy consumption with energy production

John Stouby Persson and Peter Axel Nielsen with the Department of Sustainability and Planning (AAU), the Department of Architecture Design and Media Technology (AAU), Watts, and Aalborg Forsyning

TECH4CE: TECH Centre for Circular Economy

Bent Thomsen with the Department of Electronic Systems (AAU), the Department of Planning (AAU) and the Department of Architecture, Design and Media Technology (AAU)

Verifiable and Safe AI for autonomous systems

Kim Guldstrand Larsen, Thomas Dyhre Nielsen, Martijn Goorden, Esther Hehyeon Kim, Martin Zimmermann and Christian Schilling, with Aarhus Vand, Seluxit, Grundfos, Hofor and ITU

6G-XCEL: 6G Trans-Continental Edge Learning

Torben Bach Pedersen and Søren

Kejser Jensen with European and US partners

Want to know more? Contact research leaders below

DATA, KNOWLEDGE AND WEB ENGINEERING (DKW)

Katja Hose khose@cs.aau.dk

Torben Larsen tola@cs.aau.dk

HUMAN-CENTERED COMPUTING (HCC)

John Stouby Persson

john@cs.aau.dk

Niels van Berkel

nielsvanberkel@cs.aau.dk

SAFE AND SECURE SOCIETY DEPARTMENT OF COMPUTER SCIENCE

As IT and automated systems, not least AI-based systems, become an increasingly large part of our daily lives, the need for safety and security grows. We need to be able to trust that the systems both work as intended and are robust enough to withstand inappropriate or even malicious use.

Solutions towards a safe and secure society cut across a large number of domains, and the interdisciplinary nature of the research at the Department of Computer Science at Aalborg University (CS) is therefore well-positioned to contribute to these solutions.

CORE AREAS

• Data Engineering, Science and Systems

• Data, Knowledge and Web Engineering

• Distributed, Embedded and Intelligent Systems

• Human-Centered Computing

• AI and Machine Learning

SELECTED TOPICS WITHIN SAFE AND SECURE SOCIETY

SAFETY SHIELD FOR MACHINE LEARNING

We are surrounded by cyber-physical systems (CPS). These systems comprise software and hardware communicating with and controlling a physical reality, like wind turbines, cars or pacemakers.

In these systems, the use of machine learning is widespread. However, problems can arise when the system is subjected to a situation that has not been used in the training data.

Professor Kim Guldstrand Larsen has received a VILLUM Investigator grant for a project aiming to ensure that CPS meet requirements concerning reliability and safety to a much higher degree. This is done by combine machine learning with mathematically sound and comprehensible techniques capable of offering absolute guarantees.

KEYWORDS: MACHINE LEARNING AND CYBER-PHYSICAL SYSTEMS

Project: S4OS: Scalable analysis and Synthesis of Safe, Secure and Optimal Strategies for Cyber-Physical Systems

HELPING BUSINESSES ENHANCE CYBER SECURITY

More and more businesses are exposed to cyber-attacks and security breaches.

Researchers from CS are part of a project aiming at improving the security of software-based products and services in these companies.

This requires a combination of different tools - including model checking and static analysis of current systems for verification and validation, benchmarking companies and improving software development processes.

A RELIABLE WEB OF DATA

The World Wide Web is one of the most influential inventions and has radically changed our lives.

A part of it, however, is designed for machines and provides access to vast amounts of data: the Web of Data – a building block of the Semantic Web. Although the information on the Web of Data is freely available (Linked Open Data) and covers a broad range of topics, it remains mostly unexploited.

Researchers from CS are developing technologies that overcome current challenges and establish a reliable Web of Data, including the ability to explain answers by tracking information to the original source.

KEYWORDS: MODEL CHECKING, STATIC ANALYSIS, VERIFICATION, AND DEVELOPMENT PROCESSES

Project: Sb3D: Security by Design in Digital Denmark

KEYWORDS: KNOWLEDGE GRAPHS, PROVENANCE, QUERYING AND MANAGING SEMANTIC DATA

Project: EXPLAIN-ME: Learning to collaborate via explainable AI in medical education

Project: RelWeb: A Reliable Web of Data

PROJECTS WITHIN SAFE AND SECURE SOCIETY

Algorithmic Explainability for Everyday Citizens

Niels van Berkel

CLAIRE: ControLing wAter In an uRban Environment

Kim Guldstrand Larsen, Thomas Dyhre Nielsen, Jiri Srba and Martijn Goorden with the Department of the Built Environment, AAU

HERD: Human-AI Collaboration: Engaging and Controlling Swarms Of Robots And Drones

Timothy Merritt and Niels van Berkel with SDU, CBS, Robotto, DTU and Agrointelli

Multimodal Data Processing of Earth Observation Data

Kristian Torp and Christian S. Jensen with The Alexandra Institute and The Maersk Mc-Kinney Moller Institute, SDU, Danish Environmental Protection Agency, GEO and The Danish Geodata Agency

Poul Due Jensen Professorate in Big Data and Artificial Intelligence

Katja Hose, Matteo Lissandrini, Tomer Sagi, Theis Erik Jendal, Martin Pekar Christensen with Concordia University and Singapore Management University

RelWeb: A Reliable Web of Data

Katja Hose, Gabriela Montoya, Matteo Lissandrini, Christian Aebeloe, Ghadeer Abuoda, Olivier Pelgrin and Kashif Rabbani with INRIA Rennes, WU Vienna, University of Ghent and ArangoDB

S4OS: Scalable analysis and Synthesis of Safe, Secure and Optimal Strategies for Cyber-Physical Systems

Kim Guldstrand Larsen, Martijn Goorden, Martin Kristjansen, Andreas Holck Høeg-Petersen, Asger Horn Brorholt, Jonas Hansen, Mathias Claus Jensen, Muhammed Naeem, Nikolaj Jensen Ulrik, Rasmus Tollund, Sean Kristian Remond Harbo, Thomas Grosen, Falke Carlsen, Imran Riaz Hasrat and Nick Østergaard

Sb3D: Security by Design in Digital Denmark

René Rydhof Hansen, Peter Axel Nielsen, David Kinnberg Hein and Rasmus Broholm with DTU Compute, The Alexandra Institute, The Danish Chamber of Commerce, Confederation of Danish Industry (DI), Business Hub Central Denmark and The Danish Industry Foundation

SIOT: Secure Internet of Things – risk analysis in design and operation

Kim Guldstrand Larsen and René Rydhof Hansen with AU, CBS, DTU Compute, The Alexandra Institute, Beumer Group, Develco Products, Grundfos, Logo Payment Solutions, Micro Technic, SecuriOT, Seluxit and Terma

The integration of AI powered tools in software development and UX practices

Victor Vadmand Jensen, Adam Alami, Anders Bruun, John Stouby Persson with Alka, Watts, Lyngsoe Systems and RebelDot

Unleashing the Potential of Open Data

Katja Hose, Gabriela Montoya, Tomer Sagi with Nordjyske Medier and Open Data DK

Verifiable and Safe AI for autonomous systems

Kim Guldstrand Larsen, Thomas Dyhre Nielsen, Martijn Goorden, Esther Hehyeon Kim, Martin Zimmermann and Christian Schilling with Aarhus Vand, Seluxit, Grundfos, Hofor and ITU

DATA ENGINEERING, SCIENCE AND SYSTEMS (DESS)

Christian S. Jensen csj@cs.aau.dk

Torben B. Pedersen tbp@cs.aau.dk

Bin Yang byang@cs.aau.dk

DISTRIBUTED, EMBEDDED AND INTELLIGENT SYSTEMS (DEIS)

Kim Guldstrand Larsen kgl@cs.aau.dk

Jiri Srba srba@cs.aau.dk

DATA, KNOWLEDGE AND WEB ENGINEERING (DKW)

Katja Hose khose@cs.aau.dk

Torben Larsen

tola@cs.aau.dk

Want to know more? Contact research leaders below

HUMAN-CENTERED COMPUTING (HCC)

John Stouby Persson john@cs.aau.dk

Niels van Berkel

nielsvanberkel@cs.aau.dk