Tietotekniikan laitoksen tutkimusryhmät
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The AML group is developing or has developed new machine learning techniques and applications.
Our group studies various aspects of efficient computations, including for instance approximation algorithms, online algorithms, exact algorithms, combinatorial optimization, and data structures.
Complex Systems is a transdisciplinary research area that builds on statistical physics, computer science, data science, and applied mathematics.
The Computational Logic Group develops automated reasoning techniques for solving challenging computational problems in engineering and science.
Our research group focuses on developing methods for high-throughput bioinformatics, computational biomedicine, synthetic biology and probabilistic modeling.
We develop novel machine learning methods for automatic multimedia analysis and retrieval.
The data-mining group focuses on developing novel methods to extract knowledge from data, designing algorithms to summarize large volumes of data efficiently and effectively, and exploring new ways of using the extracted information.
The Digital Content Communities studies the intersection of groups, technology and society. This includes research aimed to produce novel technical tools for group interaction as well as more social science oriented examination on the implications new communication technology may have to groups and society.
Our current research focuses on the foundations of distributed computing, the key research question is related to the concept of locality in the context large computer networks.
The Distributed and Pervasive Systems area spans from mobile networking and communications to distributed computing and Big-data.
Group studies software technologies for open, loosely-coupled, heterogeneous, and dynamic distributed systems.
Games are a multidisciplinary field, and our research interests include physics simulation, procedural animation, control optimization, AI, full-body human-computer interaction, virtual and augmented realities, games and learning.
The KEPACO group develops machine learning methods, models and tools for data science, in particular computational metabolomics. The methodological backbone of the group is formed by kernel methods and regularized learning.
The Learning + Technology Group focuses on computing education, educational technology and software visualization. We adopt a research perspective on learning and teaching that allows us to improve education through better educational technologies and teaching methods
The group seeks to understand, model, and program naturally occurring or nature-inspired self-organising processes.
The Product Requirements and Architecture Research Group (Preago) is specialized in high quality research of topics related to requirements engineering, software architectures and variability.
We develop new methods for probabilistic modeling, Bayesian inference and machine learning. Our current focuses are in particular learning from multiple data sources, Bayesian model assessment and selection, approximate inference and information visualization.
The goal of the Secure Systems research group is to create new technologies and design and analysis methods for the development of secure computing and communication systems.
Group researches machine-processable semantics related to, e.g., the Semantic Web.
The group develops and analyzes efficient algorithms for information retrieval. Our perspective is algorithm engineering. We consider both exact and approximate string searching as well as indexing methods. Also algorithms for data compression and computational biology are studied.