Professor Kimmo Kaski finished his MSc (1973) and Licentiate in Technology (1977) degrees at Helsinki University of Technology in electrical and electronics engineering, then PhD (1981) at University of Oxford in theoretical physics. After this (1981 – 1984) he held a postdoctoral position at Temple University, Philadelphia, while spending time at the Universite de Geneve Switzerland and Forschung Centrum (earlier KFA) in Julich Germany.
In 1984 he became a university lecturer at Tampere University of Technology, Finland and became an adjunct professor at University of Jyväskylä, Finland and associate professor at Temple University, Philadelphia for 1986-1987, a full professor (microelectronics) at Tampere University of Technology, Tampere, Finland for 1987-1996, during which time (1992-1993) he acted as the Scientific Director of the Research Institute for Theoretical Physics at University of Helsinki and then in 1996 became a full professor (computational science) at Helsinki University of Technology which later (2010) transformed to Aalto University.
Over the years he has held several visiting professorial positions at Oxford University, UNAM, Mexico, University of Southern Illinois, IL, USA, Northeastern University, Ma, USA, and The Tokyo University, Japan. His current research interests are in the complexity of physical, economic, social and information systems, computational and data science with a focus on social networks and human sociality, and digital data-driven health and wellbeing.
Study of social network structure and dynamics through extensive data analytics of large-scale mobile phone communication dataset. This aims to understand the role of different communication channels in maintaining social relationships, by focusing on the dynamics, mutuality, and frequency of interactions between people; how the evolution and maintenance depends on past interactions and on the type of communication channels; and how it is influenced by the structure of the social network.
The aim is to get unprecedented insight into the correlations between the input conditions (emotional closeness, communication preferences, geo-localisation, language, gender, financial involvement, and time constraints) and the output (communication patterns, social engagement, and co-occurrence in offline and online space).
Guided by these data-driven discoveries of social network structures and micro-scale dynamics in them, the next step is modelling the dynamics of and on social networks by using agent-based models to study multi-layered social network formation including community or group formation to explore the plausible social mechanisms explaining their formation. This part of the study will also focus on investigating various spreading mechanisms including information diffusion and social contagion as well as co-evolutionary opinion formation where opinion spreading affects the structure of the network.