Uppsala University, founded in 1477, was the first university in Scandinavia. The new University was small, having at most 50 students and a handful of professors.
Today the university has over 41000 students, over 1800 teachers 2427 graduate students, 48% of them female and 674 professors and 25% of those are women.
The university has three disciplinary domains, one of them of Science and Technology, including Mathematics and Computer Science, including the Department of Information Technology.
Research Group Background
The research activities at the Department of Information Technology span a very broad area – from gathering and management of data via signal processing, scientific computing and control engineering to communication of results with the aid of database management and human-computer interaction. A common ground is provided by research in theoretical computing science, real-time systems and computer architecture.
The department is linked to several long-term collaborations in strategic important areas via the following research centers:
e-Science – developing and employing information and communication technology (ICT)-based tools for scientific investigations
UPMARC – Uppsala Programming for Multicore Architectures Research Center, 2008-2017
UPPMAX – Uppsala Multidisciplinary Center for Advanced Computational Science, which is Uppsala University’s center for high performance computing and one of the nodes in the Swedish National Infrastructure for Computing (SNIC)
Internet of Things – The department hosts the Swedish strategic innovation initiative in this area of research.
The department is involved in NESUS via the Division of Scientific Computing, a leading center for research and education in Scientific Computing. The research has a broad scope, ranging from numerical analysis over software development and high-performance computing to collaborative projects in Computational Science and Engineering and industrial applications.
The other divisions of the department are:
Computing Science: research is focused around algorithm design; combinatorial optimisation; compiler construction (how to create efficient, fast, and correct machine code); databases; distributed systems; e-commerce; formal methods; machine learning; programming languages; and software engineering.
Computer systems: the research includes formal specification, verification, experimental systems development, and evaluation. The major application areas in these respects are computer networks, computer architecture, and embedded systems.
Systems and Control: methodological research is done in the areas of estimation, system identification, signal processing, spectral analysis, fault detection, and automatic control. Application areas include wireless communications, radar and synthetic aperture radar, modelling and control of mechanical systems, process control, environment, and medicine.
Visual information and Interaction: the research targets visualization and interaction in general, medicine and health care, usability of IT systems, quantitative microscopy and more.
Doc. Dr. Maya Neytcheva works at the Division of Scientific Computing. She has her expertise in the development and analysis of efficient and robust preconditioned iterative solution methods, including multilevel techniques and optimal order methods, as well as parallelization and scalability issues for those methods. She was a member of the Management Committee in the EU COST Action IC0805 Open European Network for High Performance Computing on Complex Environments(ComplexHPC)and presently is the STSM coordinator within NESUS.
She is also the Associate Editor of ‘Numerical Linear Algebra with Applications’ – a highly rated scientific journal that has in focus scalable and both numerically and computationally efficient solution techniques, that are among the potential technuqies, applicable for untrascale computer architectures.
Major Interest in Action Scientific Topics
- Development of methods and algorithms that are both accurate and well paralelizable, suitable for large, very large and ultrascale computer systems.
- Energy efficiency in very large scale systems
- Multicore Programming Frameworks
- Power modelling, efficient architecture modelling
- Resource sharing modelling
- Methods and techniques for data and information management – scalable techniques for querying, mining, and integrating information from data streams, files, databases, storage managers, and other information sources in distributed environments
- Developing technology and tools for model-based design of real-time embedded systems