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:

  1. Artificial intelligence, machine learning and optimization,
  2. Formal methods and static analysis of programs, and
  3. 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.

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Automated 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.

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Formal 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.

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Heterogeneous 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.

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Learning 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.

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Programming 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 Languages

Software Testing Laboratory

Testing of embedded software, empirical studies of software testing, test automation and model-based testing.

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The robotics research group is mainly conducting research in the area of autonomous collaborating systems.

Read more about Robotics

Ubiquitous Computing

Computing as environmental process and environment as computing devices.

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Fore more information, please contact Cristina Seceleanu.

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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