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Best doctoral theses and master's theses of 2021 in the School of Science awarded

The awards were based on academic quality, impact, and originality.
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The Aalto University Doctoral Thesis Awards are granted annually to the best 10 percent of doctoral theses at the university. The awards are based on academic quality, impact, and originality, and they are worth 3000 euros.

In the School of Science, altogether 50 doctoral theses were approved in 2021. Decision on the awards was made on the proposal of the Doctoral Programme Committee, based on nominations by departments and statements given by pre-examiners and opponents. Five theses have now been awarded:

  • Valentina Candiani, Department of Mathematics and Systems Analysis, with the thesis Computational approaches in electrical impedance tomography with applications to head imaging. This thesis aims to understand how Electrical Impedance Tomography can be valuable as a noninvasive medical imaging modality, in particular for its application to the detection and classification of stroke. 
    Supervising professor and thesis advisor: Professor Nuutti Hyvönen
  • Antti Moilanen, Department of Applied Physics, with the thesis Bose-Einstein condensation in plasmonic lattices. The thesis demonstrates the first Bose-Einstein condensate of plasmonic quasiparticles. The research includes experimental and theoretical studies of the condensates’ properties such as spatial and temporal coherence. 
    Supervising professor and thesis advisor: Professor Päivi Törmä
  • Robert van der Have, Department of Industrial Engineering and Management, with the thesis Seeking Speed: Managing the Search of Knowledge to Innovate Faster. The thesis examines how the way companies structure their search for information and knowledge impacts the speed of their innovation processes.
    Supervising professor and thesis advisor: Professor Markku Maula
  • Vikas Verma, Department of Computer Science, with the thesis Algorithms for Data-Efficient Training of Deep Neural Networks. The thesis proposes novel algorithms for training Deep Neural Networks with a limited amount of labeled samples.
    Supervising professor: Professor Juho Kannala

  • Ivan Zubarev, Department of Neuroscience and Biomedical Engineering, with the dissertation Developing machine-learning methods for the analysis of electromagnetic brain activity. This thesis investigates how machine-learning methods can be applied to the analysis of electromagnetic brain activity measured by Electro- and Magnetoencephalography, and how complex multivariate patterns extracted by these methods can be used to advance our understanding of the human brain.
    Supervising professor and thesis advisor: Professor Lauri Parkkonen

 

Master's thesis awards

Four graduates have received the master's thesis award of the School of Science this year. The award is worth 1000 euros.

  • DI Tiina Ahva: Legitimacy risks in public corporate political activity – the case of a Nordic bank. Thesis supervisor Professor Robin Gustafsson.
  • DI Niklas Miller: Algebraic number theory: On generic well-rounded lattices. Thesis supervisor Professor Camilla Hollanti.
  • DI Severi Rissanen: A critical look at the identifiability of deep latent variable models in causal inference. Thesis supervisor Professor Pekka Marttinen.
  • DI Ali Salloum: Quantifying polarization in social networks. Thesis supervisor Assistant Professor Mikko Kivelä.

 

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