Data Science is an interdisciplinary field focusing on methodologies for extracting knowledge and insights from data thus contributing to different areas of science. The Focus Area builds on the pioneering scientific results of the community and paves way for new breakthrough results across disciplines. Data science researchers concentrate on the analysis of complex, heterogeneous, and distributed large-scale data. Data analysis environments vary from high-performance computing systems, cloud environment, to edge and IoT systems. Our research methods are data, system and application driven, and our research areas vary from the genome level, industrial engineering processes, software systems, networks in human societies to astrophysical phenomena.  The overarching goal of the Focus Area is to leverage the synergies of the network in solving significant societal and industrial challenges related to data analysis.

People and Research Interests

Data Science members are including but not limited to the following:

Antti Honkela: Data Science – Machine Learning and AI (University of Helsinki)

Dorota Glowacka : ESP Group; interactive information retrieval, machine learning, exploratory search, user modelling (University of Helsinki)

Hong-Linh Truong: AaltoSEA; Design and optimization of large-scale data analysis across IoT-edge-cloud-HPC continuum (Aalto University)

Jukka K. Nurminen: Data-Intensive Computing in Natural Sciences (Focus area leader, University of Helsinki)

Kai Puolamäki: Computer Science and Atmospheric Sciences (University of Helsinki)

Keijo Heljanko: Parallel and Distributed Data Science (University of Helsinki)

Laura Ruotsalainen Spatiotemporal Data Analysis for Sustainability Science (University of Helsinki)

Maarit Käpylä: Astroinformatics; large-scale computing and data analysis of astrophysical simulated and observational data (Focus area deputy leader, Aalto University)

Mikko Kivelä: Complex systems and networks; Data analysis of networks and related method development (Aalto University)

Nikolaj Tatti: Privacy-aware and secure data science (University of Helsinki)

Pan Hui: Nokia Chair in Data Science (University of Helsinki)

Sasu Tarkoma: Data Science and Distributed Systems (University of Helsinki)

Simon Puglisi Compressed Data Structures Group; Data Science – Knowledge discovery in big data (University of Helsinki)

Wilhelmiina Hämäläinen: Data mining, pattern discovery algorithms, computational statistics (Aalto University)


LUMI-G Pilot: VISSI project aims to test the scalability of the GPU partition and generate workloads on the GPUs, particularly to stress test the storage systems for stability testing, of the GPU partition of the European pre-Exascale supercomputer LUMI. VISSI-projects brings the modelling of solar fluctuation dynamo to an extreme regime not investigated before. Click here for more information about the LUMI-G pilot.

6G: University of Helsinki joined a Coalition to Advance Finland’s 6G Competitiveness.The most relevant Finnish research institutes and companies have founded a national coalition, 6G Finland, to advance Finland’s competitiveness in 6G research and development. The goals are to build international partnerships, to prioritize and coordinate joint actions and increase the impact of 6G expertise globally. Click here for more information about 6G in the Helsinki region

Research Highlights

Our recent work shows that even a small amount of homophily,  i.e. tendency for similar people to prefer connecting to each other, can lead to formation of homogenous social groups in social networks. Further, it can lead to so called core-periphery structure where one of the groups becomes central one leaving the members of the other group to be in the peripheries of the social network. We developed techniques to detect such emergent structure in the networks that could be caused by homophily. See https://doi.org/10.1126/sciadv.aax7310

How the Sun develops its magnetic fields remains enigmatic and controversial. More information can be collected from other solar-like stars, by building larger statistical samples, by conducting sophisticated data analysis efforts, and combining those with detailed numerical modelling results. Recent analysis, where Aalto astroinformatics group participated in, sheds new critical information on the generation of stellar magnetic fields, and rules out scenarios, where turbulence driven by convection in the outer shells of solar-like stars is unimportant. More information from https://www.nature.com/articles/s41550-020-1039-x and https://www.aalto.fi/en/news/turbulent-convection-at-the-heart-of-stellar-activity

MegaSense utilizes 5G network and city reference monitoring stations for precise atmospheric real time readings and Artificial Intelligence platform for a spatially distributed network to field calibrate low cost sensors in operational mode for fixed monitoring stations and mobile air quality monitoring stations to reduce high cost of air quality infrastructure. MegaSense researchers have resolved for the first time, how the ultrafine particles of atmosphere have an effect on the climate and health. More information from https://www.nature.com/articles/s41612-020-00156-5 and https://pubs.rsc.org/en/content/articlelanding/2021/FD/D0FD00078G