Every year, Aalto University rewards the top 10% of the doctoral dissertations with an Aalto University Dissertation Award. The criteria for the award are academic quality, impact and originality. The award is worth 3000 euros.
The awarded dissertations have all been approved during year 2020 in the School of Science. Altogether 80 dissertations were approved in the School of Science in 2020. The decision on the awards was made on the proposal of the Doctoral Programme Committee, based on the nominations of the departments, and the statements given by the pre-examiners and opponents.
Jori Bomanson, Department of Computer Science: Normalization and Rewriting for Answer Set Programming and Optimization
The dissertation enhanced the performance and verified the correctness of novel logic programming techniques for combinatorial search and optimization.
Zeerim Cheung, Department of Industrial Engineering and Management: Analytically Structured History Approach Using a Relational Database – Essays on the Historical Embeddedness of Strategy Formulation
The dissertation presents an analytically structured history approach to study the historical embeddedness of strategy formulation.
Mohamed Taoufiq Damir, Department of Mathematics and Systems Analysis: Well-Rounded Lattices and Applications to Physical Layer Security
The dissertation is devoted to ensuring reliable and secure communication over noisy wireless channels. The main results in the dissertation used techniques from various mathematical fields, to name just a few, number theory, topology, probability theory, and discrete geometry.
Lauri Himanen, Department of Applied Physics: Materials Informatics – Augmenting Materials Research with Data-driven Design and Machine Learning
The dissertation examines how data-driven science, especially machine learning, can be used to re-imagine the lifecycle of materials data and complement the existing research methodologies in materials science.
Matti Karppa, Department of Computer Science: On Bilinear Techniques for Similarity Search and Boolean Matrix Multiplication
The dissertation consists of research on similarity search and Boolean matrix multiplication. The thesis included both theoretical and practical results, achieved experimentally with actual implementations of the algorithms.
Markus Kettunen, Department of Computer Science: Gradient-Domain Methods for Realistic Image Synthesis
The dissertation presents four new methods for the computational synthesis of photorealistic images for the needs of, for example, movies, advertising, computer games, and virtual reality applications. In addition to estimating the colors of the pixels of the synthesized image, the presented methods also estimate color differences between the pixels, which often leads to considerable time savings.
Antti Mäkinen, Department of Neuroscience and Biomedical Engineering: Applications of magnetic-field modeling for hybrid MEG and MRI
In the dissertation, computational methods for modeling and analyzing the magnetic field were developed. These methods can be applied in magnetic brain imaging and in other research areas that utilize low-frequency magnetic fields.
Aaro Väkeväinen, Department of Applied Physics: Lasing and Bose-Einstein condensation in plasmonic lattices at weak and strong coupling regimes
The dissertation presents experimental studies on strong light-matter interaction, lasing action and Bose-Einstein condensation in metal nanoparticle arrays.