Department of Electrical Engineering and Automation

Leo Kärkkäinen

Professor of Practice

Contact information

Full researcher profile


Deep learning has provided means to model complicated systems using very large amounts of data. I work in collaboration with hospitals to gather image and sensor data, with annotation to build models for classification tasks. The challenge is, after building the models, to find out the essential features that predict the classification. If successful, one can get hold on some part of the “dark knowledge” from decade long experience of professionals, which  they may find hard to quantify and explain.

Also, when data is sparse, simulations of physical systems can be used to produce datasets for training regression models and classifiers. One example is utilizing convolutional neural networks to estimate material parameters in ultrasound tomography.

Finally, when the models are trained and perform well enough, the challenge is to be able to simplify these in a way that makes them run even in embedded systems without significant loss of accuracy.

Research groups

Embedded Systems in Biomedical Technology


Not published at Aalto University, Embedded Systems in Biomedical Technology

Differential photoplethysmogram sensor with an optical notch filter shows potential for reducing motion artifact signals

Publishing year: 2018 Biomedical Physics and Engineering Express
Not published at Aalto University, Embedded Systems in Biomedical Technology

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

Publishing year: 2018 Journal of the Acoustical Society of America