Kristian Ejlebjærg Jensen

Estimating human predictability from mobile sensor data

Quantification of human behavior is of prime interest in many applications ranging from behavioral science to practical applications like GSM resource planning and context-aware services. As proxies for humans, we apply multiple mobile phone sensors all conveying information about human behavior. Using a recent, information theoretic approach it is demonstrated that the trajectories of individual sensors are highly predictable given complete knowledge of the infinite past. We suggest using a new approach to time scale selection which demonstrates that participants have even higher predictability of non-trivial behavior on smaller timer scale than previously considered.

Topology optimization of viscoelastic rectifiers

An approach for the design of microfluidic viscoelastic rectifiers is presented based on a combination of a viscoelastic model and the method of topology optimization. This presumption free approach yields a material layout topologically different from experimentally realized rectifiers, and simulations indicate superior performance for the optimized design in the regime of moderate elasticity.

Mobile context toolbox: an extensible context framework for s60 mobile phones

We describe an open framework utilizing sensors and application data on S60 mobile phones enabling rapid prototyping of context-aware mobile applications. The framework has an extensible layered architecture allowing new sensors and features to be added to the context framework as they become available on mobile phone platforms. The framework provides access to multiple sensors to derive user context, and we present results from experiments with two prototype applications built using the toolbox. Initial experiments have been carried out to validate the data obtained by the tool. In the experiments 14 participants have been continuously using a Nokia N95 mobile phone with a context logger application for an average of 48 days per user and covering 70% of the time. The study has provided valuable insights into the performance issues of the system in real-life usage situations, including the stability of and power consumption in the system.

Numerical Optimization in Microfluidics

Numerical modelling can illuminate the working mechanism and limitations of microfluidic devices. Such insights are useful in their own right, but one can take advantage of numerical modelling in a systematic way using numerical optimization. In this chapter we will discuss when and how numerical optimization is best used.

Enhancing Metabolic Models with Genome-Scale Experimental Data

Genome-scale metabolic reconstructions have found widespread use in scientific research as structured representations of knowledge about an organism’s metabolism and as starting points for metabolic simulations. With few simplifying assumptions, genome-scale models of metabolism can be used to estimate intracellular reaction rates in any organism for which a well-curated metabolic reconstruction is available. However, with the rapid increase in the availability of genome-scale data, there is ample opportunity to refine the predictions made by metabolic models by integrating experimental data. In this chapter, we review different methods for combining genome-scale metabolic models with genome-scale experimental data, such as transcriptomics, proteomics, and metabolomics. Integrating experimental data into the models generally results in more precise and accurate simulations of cellular metabolism.