Best doctoral theses and master's theses 2023 in the School of Science awarded

Annually Aalto University awards the best 10 percent of doctoral theses. This year seven doctoral theses were honoured at the School of Science.

The criteria for the Doctoral Thesis Award are academic quality, impact, and originality. The award is worth 3000 euros.

Altogether 70 theses were approved in the School of Science in 2023. 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.

The awards were granted to

• Kukka-Emilia Huhtinen: Superconductivity and normal state properties in flat bands

  Department of Applied Physics

  Supervising professor and thesis advisor: Professor Päivi Törmä, Department of Applied Physics

  The thesis investigates superconductivity in flat band systems, and how their properties relate to quantum geometry. The results confirm the potential of flat bands for realising exotic correlated states.

  Kukka-Emilia Huhtinen is working as a Postdoctoral Researcher at ETH Zürich.

• Viliam Vaňo: Designing quantum matter in two dimensions

  Department of Applied Physics

  Supervising professor and thesis advisor: Professor Peter Liljeroth, Department of Applied Physics

  The thesis uses a combination of molecular beam epitaxy and scanning tunneling microscope to create and probe exotic quantum states of matter.

  Viliam Vaňo is working as PCCM Postdoctoral Fellow at Princeton University, USA

• Sorrachai Yingchareonthawornchai: Vertex Connectivity via Local Computation: Breaking Quadratic Time, Poly-logarithmic Max-flows, and Derandomization

  Department of Computer Science

  Supervising professor: Professor Parinya Chalermsook, Department of Computer Science

  Thesis advisor: Professor Danupon Nanongkai, Max Planck Institute for Informatics and Saarland University, Saksa

  The thesis presents an elegant theory of local computation in algorithmic graph theory and resolves a 50-year-old open problem in the field of network connectivity.

  Sorrachai Yingchareonthawornchai is working as a post-doctoral fellow at the Hebrew University of Jerusalem, Israel.

• Sebastian Szyller: Ownership and Confidentiality in Machine Learning

  Department of Computer Science 

  Supervising professor: Adjunct Professor N. Asokan, Department of Computer Science

  Thesis advisor: Research Fellow, Dr. Samuel Marchal, WithSecure

  The thesis examines the susceptibility of machine learning models to black-box theft. It proposes multiple attacks and defenses, and investigates the potential for conflicting interactions with protection mechanisms against other threats.

  Sebastian Szyller is a research scientist at Intel Labs. He works on security and privacy in machine learning. 

• Lassi Meronen: Uncertainty Quantification in Deep Learning

  Department of Computer Science

  Supervising professor and thesis advisor: Professor Arno Solin, Department of Computer Science

  The thesis examines ways to reduce the overconfidence of modern deep learning models, by improving their ability to estimate the uncertainty of their predictions. This is done by making connections to principled probability models, and bringing useful properties from them into deep learning models.

  Lassi Meronen is currently staying in Chamonix, France collecting experience and a tick-list to apply to mountain guide training in the spring 2025. This means climbing and skiing in the Alps to gain the necessary experience.

• Juho Roponen: Computational models for adversarial risk analysis and probabilistic scenario planning

  Department of Mathematics and Systems Analysis 

  Supervising professor and thesis advisor: Professor Ahti Salo, Department of Mathematics and Systems Analysis

  People are often faced with decisions without perfect information about the potential outcomes. The thesis examines probabilistic computational models, that can be used to evaluate different decision alternatives in the face of uncertainty.

  Juho Roponen as a postdoctoral researcher at University of Jyväskylä, Faculty of information technology, Multiobjective optimization group.

• Koos Zevenhoven: Unconventional MRI scanner technology and intelligent dynamics

  Department of Neuroscience and Biomedical Engineering 

  Supervising professor: Lauri Parkkonen, Department of Neuroscience and Biomedical Engineering

  Thesis advisor: Professor (emer.) Risto Ilmoniemi, Department of Neuroscience and Biomedical Engineering

  In this thesis, a new kind of brain scanner was designed and constructed based on a combination of nuclear magnetic resonance and electromagnetic detection of brain activity. In addition, an approach called dynamical pulse-waveform coupling was introduced.

  Koos Zevenhoven is working as a research group leader at Aalto University Department of Neuroscience and Biomedical Engineering.

Master's Theses awards

The School of Science awarded prizes to five master's theses. The prize is worth 1000 euros.

The awarded

• M.Sc.(Tech.) Eeli Lamponen: Resonance valence bond superconductivity and quantum critical points in multiband systems

  Supervisor: Professor Päivi Törmä

• M.Sc.(Tech.) Mark Laukkanen: Post-implementation training of eHealth services facilitating patient-provider communication: Good practices and the supporting role of software vendors

  Supervisor: Assistant Professor Johanna Viitanen

• M.Sc.(Tech.) Emil Verkama: Repairing the Universality Theorem for 4-Polytopes

  Supervisor: Assistant Professor Kaie Kubjas

• M.Sc.(Tech.) Anni Hukari: Mutual information and Pearson correlation on M/EEG time series

  Supervisor: Aalto Distinguished Professor Riitta Salmelin

• M.Sc.(Tech.) Perttu Laiho: 5G-enabled digital transformation in the Finnish forest industry

  Supervisor: Associate Professor Robin Gustafsson