Computer and Data Science
Computer and Data Science (CDS) conducts research on novel approaches, algorithms, techniques, and tools in AI (Artificial Intelligence) and machine learning, optimization, heterogeneous data from various sensing devices, software and hardware for robots, brain-like computing, as well as formal methods and static program analysis.
Our models and algorithms can make predictions about physical and environmental phenomena and improve the systems’ ability to adapt and refine their behavior in real time, with a high degree of predictability assurance. The research ranges from theoretical contributions to applied ones in close collaboration with industry.
CDS is active in research and education on the following fronts:
- Artificial intelligence, machine learning and optimization,
- Formal methods and static analysis of programs, and
- Robotics and data analysis.
Research areas
Artificial Intelligence och Intelligent Systems
Foundational and applied research in Artificial Intelligence and Machine Learning for Intelligent Systems for both industry, medical and business applications. The research focuses on methods and techniques enabling learning, reasoning, experience reuse, and experience sharing. We work with both autonomous AI applications as well as decision support systems.
Read more about Artificial Intelligence och Intelligent SystemsAutomated Software language and Software engineering
The ASSO research group focuses on automating the engineering of software languages and software by applying advanced computation and data manipulation techniques.
Read more about Automated Software language and Software engineeringFormal Modelling and Analysis of Embedded Systems
Focusing on formal modelling, analysis, and verification techniques for real-time embedded systems. In particular, formal syntax and semantics of componentbased and service oriented models with extra-functional properties such as time or resources.
Read more about Formal Modelling and Analysis of Embedded SystemsHeterogeneous systems - hardware software co-design
The group aims to boost exploitation of heterogeneous systems in terms of predictability, effective development and efficient software-hardware integration for next-generation intelligent embedded systems.
Read more about Heterogeneous systems - hardware software co-designIndustrial Software Engineering
Focusing on engineering of complex software-intensive embedded systems, covering the entire lifecycle and including technologies, methods and processes. Particular emphasis on component- and model-based software engineering for embedded systems.
Read more about Industrial Software EngineeringLearning and Optimisation
The group aims to explore the synergy between machine learning and optimization to achieve collaborative effects in building highly efficient and smart systems.
Read more about Learning and OptimisationProgramming Languages
Worst-case execution time analysis, as well as design and analysis of languages for real-time and embedded systems. Focusing on static program analysis for embedded systems, specializing in Worst-Case Execution Time analysis.
Read more about Programming LanguagesSoftware Testing Laboratory
Testing of embedded software, empirical studies of software testing, test automation and model-based testing.
Read more about Software Testing LaboratoryRobotics
The robotics research group is mainly conducting research in the area of autonomous collaborating systems.
Read more about RoboticsUbiquitous Computing
Computing as environmental process and environment as computing devices.
Read more about Ubiquitous ComputingContact
Fore more information, please contact Cristina Seceleanu.
Ongoing research projects
CPMXai will develop a digital twin for cognitive predictive maintenance through automatic data labelling, AI/ML and Explainable AI (XAI) to reduce unwanted situations and enhance maintenance in manufacturing and production processes.
Project manager at MDU: Shahina Begum
Main financing: Vinnova, Product2030
The project's overall aim is to develop an autonomous reconnaissance capability for unmanned aerial systems, by further developing the results that have been achieved in previous joint projects.
Project manager at MDU: Peter Funk
Main financing: Vinnova
Data-driven development methods show great promise in producing accurate models for perception functions such as object detection and semantic segmentation, however most of them lack a holistic view for being implemented in dependable systems. This project proposal aims at producing Machine Learning (ML) models of robust nature to meet and stay ahead of emerging certification requirements.
Project manager at MDU: Masoud Daneshtalab
Main financing: Stiftelsen för strategisk forskning
In this joint project, we aim at decreasing the power consumption and computation load of the current image processing platform by employing the concept of computation reuse.
Project manager at MDU: Masoud Daneshtalab
Main financing: STINT - The Swedish Foundation for International Cooperation in Research and Higher Education
FASTER AI addresses emergent needs to embed machine learning (ML) inference capabilities within hardware infrastructure of critical importance and use.
Project manager at MDU: Masoud Daneshtalab
Main financing: Vinnova
GreenDL aims to provide novel theoretical foundations and practical algorithms to automatically design scalable energy-efficient DL models with low energy footprint and facilitate fast deployment of complicated DL models for various Edge devices satisfying given hardware constraints This project will remarkably investigate performance analysis and modeling, optimization, and learning algorithms followed by extensive experiments.
Project manager at MDU: Masoud Daneshtalab
Main financing: Vetenskapsrådet
The INDTECH project is an industrial graduate school at MDU, focusing on the implementation of Industry 4.0 and applied AI in production systems in collaboration with 12 partner organizations consisting of leading industrial companies, research institutes and technology centers and supporting organizations such as AI Sweden, PiiA, Automation Region and Blue Institute. INDTECH Industrial Technology Graduate School offers advanced training in the field of industrial digitization, a new and emerging field of technology that revolutionizes all aspects of the manufacturing and process industry
Project manager at MDU: Markus Bohlin
Main financing: KK-stiftelsen
The aim of determining fitness to drive is to achieve a balance between minimising any driving-related road safety risks for the individual and the community and maintaining the driver’s lifestyle and employment-related mobility independence.
Project manager at MDU: Mobyen Uddin Ahmed
Main financing: European Union’s Horizon 2020
Q-Test will enable STL and VCE to reach a common research agenda through various startup activities, enabling both parties to apply for a joint research project in near future, targeting quality increment in embedded electronic system testing.
Project manager at MDU: Wasif Afzal
Main financing: Volvo Construction Equipment
Detta projekt kommer att utveckla självövervakande och kontinuerliga inlärningsmetoder för att främja en bredare tillgänglighet av datadrivet prediktivt underhåll i kraftnät.Kontinuerligt (och livslångt) lärande har hög potential att stödja mer grundade och exakta underhållsbeslut genom att hantera förändrade förhållanden i kraftnät, som t.ex. åldrande av elektriska komponenter. Fallstudier med data som samlats in från kraftnät kommer att utföras för att utvärdera effektiviteten i de föreslagna metoderna.
Project manager at MDU: Ning Xiong
Main financing: SSF
The goal of the TRUSTY project is to provide adaptation in the level of transparency to enhance the trustworthiness of AI-powered decisions in the context of remote digital towers (RDTs).
Project manager at MDU: Mobyen Uddin Ahmed
Main financing: EU, Sesear3 joint undertaking
URBAN TECH will support the acceleration of competitive success of European SMEs through market launch of new or significantly improved products and services with higher value (with higher quality, increased access, ecoinnovative, resource efficient and internationally scalable). In long term perspective (strategic objective), URBAN TECH will innovate and increase the competitiveness of European Health Tech, Smart City and Greentech industries globally.
Project manager at MDU: Fredrik Ekstrand
Main financing: EU
Worst-Case Execution Time (WCET) analysis tries to find an upper bound for the time needed to execute a program. Such WCET bounds are very important when designing and verifying real-time systems. Current industrial practice is to estimate these bounds from measurements, something often complicated and error-prone.
Project manager at MDU: Björn Lisper