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Personal profile

Artistic and research interests

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.

Education/Academic qualification

Doctor of Philosophy in Natural Sciences

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Research Output

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach

Huttunen, J. M. J., Kärkkäinen, L., Honkala, M. & Lindholm, H., Dec 2019, In : INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING. 17 p., e33303.

Research output: Contribution to journalArticleScientificpeer-review

  • Estimation of groundwater storage from seismic data using deep learning

    Lähivaara, T., Malehmir, A., Pasanen, A., Kärkkäinen, L., Huttunen, J. M. J. & Hesthaven, J. S., 2019, In : Geophysical Prospecting. 67, 8, p. 2115-2126 12 p.

    Research output: Contribution to journalArticleScientificpeer-review

  • 1 Citation (Scopus)

    Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data

    Huttunen, J. M. J., Kärkkäinen, L. & Lindholm, H., 1 Aug 2019, In : PLoS computational biology. 15, 8, p. e1007259

    Research output: Contribution to journalArticleScientificpeer-review

    Open Access
    File
  • 2 Citations (Scopus)
    91 Downloads (Pure)

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

    Lähivaara, T., Kärkkäinen, L., Huttunen, J. M. J. & Hesthaven, J. S., 1 Feb 2018, In : Journal of the Acoustical Society of America. 143, 2, p. 1148-1158 11 p.

    Research output: Contribution to journalArticleScientificpeer-review

    Open Access
    File
  • 13 Citations (Scopus)
    2 Downloads (Pure)

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

    Blomqvist, K. H. & Kärkkäinen, L., 25 May 2018, In : Biomedical Physics and Engineering Express. 4, 4, 8 p., 047004.

    Research output: Contribution to journalArticleScientificpeer-review

  • 2 Citations (Scopus)

    Projects

    Profi2 Kärkkäinen T41000

    Mäkelä, K. & Kärkkäinen, L.

    01/01/201931/08/2020

    Project: Academy of Finland: Competitive funding to strengthen university research profiles

    Press / Media

    Computer science versus COVID-19

    Leo Kärkkäinen

    23/03/2020

    1 item of Media coverage

    Press/Media: Media appearance

    Aalto University: AI is revolutionising health technology

    Leo Kärkkäinen

    28/02/2019

    1 item of Media coverage

    Press/Media: Media appearance