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Out of the 91 projects funded by the Academy of Finland, 31 have been awarded ÄûÃʵ¼º½ University and 6 of them will take place at the Department of Computer Science. The related topics include the Internet of Things, mobile services, acoustics, game design, and machine learning.

The UDIoT project led by Mario Di Francesco addresses the scalability, reliability and security challenges of Internet of Things. Specifically, it aims at building usable and secure methods to configure, manage and maintain a large number of Internet-connected devices, with the ultimate goal of improving the dependability of digital services.

The project of Perttu Hämäläinen aims to develop physically-based movement artificial intelligence for interactive game and simulation characters, and to investigate the opportunities of such technology in supporting movement skill learning and exercise motivation. This is motivated by public health concerns over sedentary lifestyle and low physical activity.

Tapio Lokki is to investigate new scientific methods with Vesa Välimäki, from the Department of Signal Processing and Acoustics, to bring the immersive concert experience to people's homes with the help of head-tracked headphones and sophisticated signal processing techniques. A live music concert will be recorded with a novel method, which captures the signals of instruments and room acoustics separately. At the consumer’s side different streams are rendered together, resulting in more authentic spatial sound reproduction than with traditional methods.

Jari Saramäki will study the digital daily rhythms of individuals and populations with the help of a wide variety of data sets. Goals include automated detection of individuals’ chronotypes (morningness/eveningness) from mobile phone data, understanding daily variations of social contexts, and using Big Data to capture population-level patterns, such as variation of sleep with age or season. 

Matti Siekkinen’s  LACRIMOSA-project will study the anatomy of the end-to-end latency in mobile cloud computing. The goal is to develop methods to measure and model the latency and its impact on the user experience of latency-critical mobile multimedia services. The measurement tools and models will be used to optimize the server deployment strategies and software designs for user experience of these services.

Aki Vehtari’s research objective is to develop novel computational methods for the statistical analysis of survival data, with particular focus on CVD and diabetes diagnostics, and cancer treatment and follow-up. Improvements in predictive performance due to developed survival analysis methods can lead to significant improvements in targeting treatments and thus globally reduce suffering of the patients and the cost of treatments, preventive therapies and follow-up methods.