Algorithm research is characterised by the development of new algorithmic solutions for well-defined problems, an exact mathematical approach and computational modelling.
Research focuses especially on the theory of artificial intelligence and machine learning, exact algorithms for NP-hard problems, string processing, succinct data structures and the modelling of biological systems, such as evolution. Some researchers concentrate on the theoretical aspects of algorithms, others on the practical optimisation of algorithms, and yet others specialise in algorithmic tailoring for bioinformatics and life science informatics.
The algorithmic bioinformatics group aims to provide a solid foundation for reliable and scalable methods enabling new breakthroughs. The group’s research is based on the development of biological sequencing data and algorithmic analysis and assembly techniques. The group comprises five teams:
The algorithmic data science group aims to develop efficient and easily manageable data systems and use them for novel applications. The group’s research covers all stages of data science, from data management and processing to model inference and applied data analysis.
Group leader: Associate Professor Michael Mathioudakis
The bioinformatics and evolution research group develops algorithms and algorithmic theory that help produce better tools for studying and understanding evolution. In its research, the group uses big data and collaborates closely with both clinicians and experimental researchers.
The constraint reasoning and optimization group focuses on the development and analysis of decision-making, search and optimisation procedures and their applications in computationally hard problem domains. The group also works with the optimisation of machine learning and with computational applications related to argumentation theory.
Group leader: Professor Matti Järvisalo
The data science and evolution group employs data science to build an understanding about the evolutionary processes in nature and society and about their causal mechanisms. The group’s present research focuses on methods for better interpreting and modelling fossil records and past conditions.
Group leader: Associate Professor Indrė Žliobaitė
The exploratory data analysis group studies algorithmic and probabilistic methods of artificial intelligence. The goal is to help understand and interpret large heterogeneous data sources with the help of artificial intelligence. The group focuses especially on the use of randomisation methods and constrained randomisation.
Research in artificial intelligence develops new methods for artificial intelligence, machine learning and data mining. The goal is to produce computationally efficient, theoretically justified and reliable methods. In addition to theoretical research, our research groups apply these methods for various needs, from computational creativity to evolutionary modelling.
The cognitive computing group conducts research at the intersection of artificial intelligence, cognitive science and human-computer interaction. The group’s research focuses on creating models for machine learning and models based on human cognitive processes.
Group leader: Academy Research Fellow Tuukka Ruotsalo
The complex systems computation group (CoSCo) investigates computational problems related to complex systems, focusing on prediction and modelling. Working at the intersection of computer science, information theory and mathematical statistics, the group carries out both basic research and applied research, solving problems in the fields of social sciences, ecology and medicine.
The computational creativity and data mining group works with artificial intelligence and data science and focuses especially on computational creativity and data mining. In the group’s vision, data analysis is used to make intelligent systems or agents aware of their environment and themselves. The research group is also interested in the use of artificial intelligence for text mining and text creation.
Group leader: Professor Hannu Toivonen
The exploratory search and personalisation group studies reinforcement learning and user modelling to develop systems that help users navigate massive datasets. The group’s research focuses on personalisation, exploratory search, experimental design and evaluation methodology.
Group leader: Associate Professor Dorota Glowacka
The machine and human intelligence group focuses on probabilistic machine and human learning. In the group’s research, the brain and computers are treated as statistical inference engines with probabilistic operations and constrained resources.
Group leader: Assistant Professor Luigi Acerbi
The multi-source probabilistic inference group studies statistical machine learning and artificial intelligence. The group develops new methods and algorithms, focusing especially on approximate Bayesian inference of probabilistic programs.
Group leader: Associate Professor Arto Klami
The probabilistic inference, privacy and computational biology group develops machine learning methods based on Bayesian inference, focusing on privacy-preserving machine learning and applications in computational biology and genomics.
Group leader: Professor Antti Honkela
The spatiotemporal data analysis group develops estimation, machine learning, computer vision and signal processing algorithms. The group aims to create navigation based on artificial intelligence and related methods for ecologically sustainable smart cities of the future.
Group leader: Associate Professor Laura Ruotsalainen
Data communication research combines the Department’s traditional research in wireless and mobile computing with new, growing research themes. The research groups focus on networked systems and their enablers: interoperability (incl. service and software platforms, trust and safety), mobility (technology and location independence, wireless communication), information networks, service networks, context awareness, ubiquitous computing and interactive systems. The research focus is expanding from protocols to application layer problems and solutions.
Collaboration with groups studying distributed systems is an essential part of research. This collaboration is coordinated by the NODES unit (Networking in Open Distributed Environments).
The collaborative and interoperable computing group develops solutions for the interoperability and management of services. Its research focuses especially on interoperability between businesses and systems and on automation solutions for interfaces.
Group leader: University Lecturer Lea Kutvonen
The collaborative networking group focuses on large-scale distributed systems and online applications based on voluntary collaboration. Research topics include cloud services, opportunistic mobile networks and the Internet of Things.
Group leader: Professor Jussi Kangasharju
The content-centric structures and networking group studies data communication and networks from the perspective of content transfer and sharing. The group aims to develop data communication solutions that are more efficient than the ones based on the current TCP/IP protocol.
Group leader: Professor Sasu Tarkoma
The parallel and distributed computing group investigates the use of parallel computing and distributed systems in various applications, especially in data management and its analysis.
Group leader: Professor Keijo Heljanko
The pervasive data science group studies the proliferation of sensor-enabled devices in our everyday environments. The group is particularly interested in how the data provided by such devices can be used to support scientific investigations.
Group leader: Associate Professor Petteri Nurmi
The secure systems group studies the development and optimisation of information systems. Its research focuses on ways to build state-of-the-art systems that are easy to use, have reasonable development costs and ensure adequate safety.
Group leader: Professor Valtteri Niemi
The systems and media group investigates various digital systems, such as mobile networks, online services and virtual reality, and how users operate in the systems. In its research, the group focuses on users’ behaviour and, based on the data collected, strives to build better systems that are safe, energy-efficient and scalable.
Group leader: Professor Pan Hui
The wireless internet group studies the uses and technological opportunities of wireless networks. The group has worked on the topic since the 1990s and has participated in the development of numerous methods and tools for measuring and improving network performance.
Group leader: Lecturer Markku Kojo
Software plays an important role in nearly all industries, with software being integrated into a growing number of industrial products. At our Department, software research encompasses software, interactive systems, information systems and programming, as well as research into the teaching of programming and analytics of learning.
The agile education research group studies the development of education from the perspective of data-driven iteration. The goal is to use the data collected to create operating models for the continuous development and improvement of education. The group’s research aims to develop learning processes to increasingly better prepare students for professional duties.
Group leader: University Lecturer Matti Luukkainen
The empirical software engineering group studies the development of software engineering for industrial needs. The group emphasises the role of empirical methods, which the members use to deepen their understanding of the boundary conditions for software development and architecture. The group carries out both project research and long-term basic research.
The ubiquitous interaction group studies human–computer interaction. Its research focuses especially on multimodal interaction, interactive intent modelling and mixed reality. Through its research, the group aims to produce information and applications for various search and wellbeing applications.
Group leader: Professor Giulio Jacucci
The unified database management systems group studies database management systems and their optimisation. The group develops algorithms that aim to improve the management, usability and performance of databases and large information systems.
Group leader: Professor Jiaheng Lu