Computer science and engineering
Computer science and engineering
The research in computer science and engineering covers a broad area and BTH focuses on two areas:
- Big data and AI:
The researchers are studying different techniques for handling large amounts of data from a technical perspective with regard to, for example, storage and database systems as well as how AI, machine learning and information mining can be used for pattern recognition and trends in large amounts of data.
- Parallel computer systems:
The research focuses on parallel computer systems, cloud-based systems and security. The research covers both practical and theoretical aspects of data processing with applications and implementations of different systems.
Big data and AI
In the field of big data and AI, we study and develop methods and techniques for collecting, storing, processing and analysing large amounts of data as well as how data can be used for, for example, prediction, classification and pattern recognition. As these methods require a great deal of computational power, there is a close link to research in parallel computer systems.
The research we conduct is mainly in AI, machine learning, information extraction, large-scale data analysis, planning and scheduling and optimisation. The methods we develop can be used in several areas such as intelligent decision support systems, image analysis and pattern recognition, analysis of historical documents, health care, transport and logistics as well as anomaly and outlier detection.
Parallel computer systems
In the area of parallel computer systems we study computer systems with multiple processing units (processors), e.g., multi-core systems, distributed systems, cloud based systems, GPUs, and heterogeneous systems. The area includes methods and technologies for design, implementation, and analysis of parallel systems, in order to achieve qualities such as correctness, performance, robustness, scalability, high resource utilization, low energy consumptions. The research includes hardware aspects, as well as system and application software, and the interaction between hardware and software. The application focus is primarily algorithms and methods from the data science area, e.g., algorithms for machine learning, data mining, pattern recognition, and large scale analysis of data images.
Intelligent transportation systems
Research in the field of intelligent transportation systems involves the integration of electronic road tolls and how rail traffic can be re-scheduled when disturbances occur. The focus lies on transportation policy analysis, simulations, development of search algorithms and finding sustainable transportation solutions.
In the distributed systems field architecture of multiprocessors and data in the cloud (a technology based on the use of computers over the Internet) are studied. Another area is the artificial intelligence and human-machine interaction. The focus is on various cloud services and applications in social networks.
In the security research field machine learning techniques and anomaly detection (finding patterns in data that do not conform to an established “normal” behavior) are used to analyze irregularities in large sets of data. The focus is to identify and correct weaknesses in the police- and defense fields, industrial applications and among end users.
Data will be generated at an ever-increasing rate for the foreseeable future. Added value and cost savings can be obtained by analyzing big data streams. The analysis of large data sets requires scalable and high-performance computer systems. In order to stay competitive and to reduce consumption of energy and other resources, the next generation systems for scalable big data analytics need to be more resource-efficient. The research profile, Scalable resource-efficient systems for big data analytics, combines existing expertise in machine learning, data mining, and computer engineering to create new knowledge in the area of scalable resource-efficient systems for big data analytics. The value of the new knowledge will be demonstrated and evaluated in two application areas (decision support systems and image processing).
The needs and interests of our 9 industrial partners are grouped into industrial challenges. Based on these challenges and in cooperation with our partners we have defined initial sub-projects grouped into four research themes:
- Research theme A: Big data analytics for decision support
- Research theme B: Big data analytics for image processing
- Research theme C: Core technologies (machine learning)
- Research theme D: Foundations and enabling technologies
Funding: The Knowledge Foundation (KK-stiftelsen) 2014-2020
Partners: Arkiv Digital AD, Compuverde, Ericsson, Noda Intelligent Systems, Telenor Sverige, and Sony Mobile Communications.
Contact person: Håkan Grahn, email@example.com