Data science

The data science research area is composed of several research groups that fuse computer science, statistics and applied mathematics to solve application problems in a data-driven manner. The research is fuelled by the data arising from every domain of the modern world: generated by high-throughput biomedical technologies, recorded by ubiquitous IoT sensors, or stored by providers of online services for analysing customer behaviour. We apply a range of techniques from network science to Bayesian methods and from machine learning to algorithms research to model the data and to create practical applications. Data science is inherently multidisciplinary; our researchers focus on the computational and methodological core and work together with external domain experts. Major application fields are found in bioinformatics, computational social science and astroinformatics. Our research groups have studied, e.g., how to identify metabolites with fast algorithms, how human gut microbiome dynamics relate to type 1 diabetes, and how to estimate travel times in developing countries from mobile-phone data.






Aristides Gionis

data mining, graph mining, social-network analysis, social media analysis


Keijo Heljanko

distributed systems, cloud computing, big data, distributed computing


Eero Hyvönen

semantic web, linked open data, artificial intelligence, web technologies


Tomi Janhunen

computational logic, automated reasoning, constraints, constraint-based optimization, learning logical representations from data


Alex Jung

statistical learning theory, compressed sensing, big data, compressed sensing, complex networks, convex optimization, graphical models, distributed algorithms, information theory, dimensionality reduction, statistical physics 


Kimmo Kaski

computational science, statistical physics, complex systems and networks, social networks, computational social science


Petteri Kaski

algorithm theory, exact and parameterized algorithms, algebraic algorithms, algorithm engineering


Samuel Kaski

machine learning, probabilistic modelling, artificial intelligence, bioinformatics, computational medicine, user interaction, brain signal analysis


Mikko Kivelä

computational science, complex systems


Jouko Lampinen

computational information technology


Harri Lähdesmäki

bioinformatics, probabilistic modelling, machine learning, systems biology


Pekka Orponen

algorithmics of self-organisation, DNA and RNA self-assembly, stochastic and online algorithms, computational complexity


Kai Puolamäki

explorative data analysis, randomization, machine learning


Juho Rousu

predicting structured data, kernel methods, computational biology, machine learning


Jari Saramäki

data science, complex systems, complex networks, social networks, network neuroscience, computational immunology 


Jukka Suomela

algorithms, theoretical computer science, distributed and parallel computing, digital humanities


Stavros Tripakis

formal methods, system design, cyber-physical systems


Aki Vehtari

Bayesian inference, probabilistic modeling, machine learning



Page content by: communications-cs [at] aalto [dot] fi (Department of Computer Science) | Last updated: 02.03.2018.