Promotor: Prof.dr. A. Doelman, Co-promotor: V. Rottschäfer

Promotor: Prof.dr. P.C. van Welzen

Promotores: S.J. Edixhoven, P.Parent

Promotor: Prof.dr. S.F. Portegies Zwart

Promotor: K.J. Batenburg

Radiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the presence of foreign objects.

Promotor: Prof.dr. K.J. Batenburg

The Copenhagen interpretation of quantum mechanics states that a measurement collapses a wavefunction onto an eigenstate of the corresponding measurement operator.

In this thesis, topics relating to the optimization of high-throughput pipelines used for imaging are discussed. In particular, different levels of implementation, i.e., conceptual, software, and hardware, are discussed and the thesis outlines how advances on each level need to be made to make gains…

Automated Machine Learning (AutoML) is widely used to automatically build a suitable practical Machine Learning (ML) model for an arbitrary real-world problem, reducing the effort of practitioners in the ML development cycle for real-world applications. Optimization is a key part of a typical AutoML…

This thesis focuses on addressing four research problems in designing embedded streaming systems.

Optimization tasks in practice have multifaceted challenges as they are often black box, subject to multiple equality and inequality constraints and expensive to evaluate.

For optimization problems, it is often unclear how to choose the most appropriate optimization algorithm. As such, rigorous benchmarking practices are critical to ensure we can gain as much insight into the strengths and weaknesses of these types of algorithms.

Building a Deep Cross-media Transfer Network to extract visual relations that relieve the problem of insufficient training data for visual tasks.

Promotor: Prof.dr. W.T.F. den Hollander

Understanding interactions in quantum many-body systems remains one of the most profound and difficult challenges in condensed matter physics.

Machine Learning is becoming a more and more substantial technology for industry.

Promotor: K.E. Schalm, Co-promotor: S.S. Lee

Constraint multi-objective optimization with a limited budget for function evaluations is challenging. This thesis tackles this problem by proposing new optimization algorithms. These algorithms are applied on holistic ship design problems. This helps naval architects balance objectives like cost, efficiency,…

In this thesis, deep learning is studied from a statistical perspective. Convergence rates for the worst case risk bounds of neural network estimators are obtained in the classification, density estimation and linear regression model.

Promotor: R.D. Gill, F. Taroni

We present an extensive study of methods for exactly solving stochastic constraint (optimisation) problems (SCPs) in network analysis. These problems are prevalent in science, governance and industry.

Op 11 november 2021 verdedigde Mariana Gkliati het proefschrift 'Systemic Accountability of the European Border and Coast Guard'. Het promotieonderzoek is begeleid door promotoren prof.dr. P. Rodrigues en prof.dr. L. Besselink (UvA).

In this thesis we will explore the use of fuzzy systems theory for applications in bioinformatics.

Als kennisinstellingen, gemeente, burgers en andere partijen of organisaties elkaar onvoldoende weten te vinden, komt kennis niet voldoende ten goede aan de samenleving. Het Leiden Healthy Society Center heeft de ambitie om kennis, beleid en praktijk duurzaam met elkaar te verbinden. Zo willen we onderwijs…

Promotor: R. Tijdeman, Co-promotor: H.J.J. te Riele

This project uses archaeological, literary and epigraphic evidence to investigate urban development in Roman-period North Africa, compiling this in a GIS-linked database in order to analyse the development of urban settlement spatially over time.

Promotores: H.W. Lenstra, B. Erez, Co-promotor: L. Taelman

Promotor: Prof.dr. J.N. Kok, Co-promotor: F.J. Verbeek

A convolutional neural network (CNN) is a biologically inspired algorithm, highly capable at processing images and videos.

Promotor: S.J. Edixhoven, Co-promotor: K.J. Batenburg

Promotor: F. Arbab, R. De Nicola, Co-Promotor: M. Tribastone

In this Yearbook you will find some fascinating examples of what was done in 2021, not only by ourselves, but also by our international colleagues.

Promotor: S.M. Verduyn Lunel, Co-Promotor: O.W. van Gaans

Promotores: J. Kok, M. Chaudron (Chalmers University)

On a structural level, the properties featured by a majority of mechanical metamaterials can be ascribed to the finite number of soft internal degrees-of freedom allowing for low-energy deformations.

In this thesis we study products of linear error correcting codes.

In this dissertation (titled: Exploring the Synergies between Transfer in Reinforcement Learning and Procedural Content Generation) we explore how the two research fields named in the title, namely Transfer in Reinforcement Learning (TRL) and Procedural Content Generation (PCG) can synergize togethe…

Promotores: W. Th. F. den Hollander, M.O. Heydenreich

A modern ship design process is subject to a wide variety of constraints such as safety constraints, regulations, and physical constraints.

Promotor: P.D. Grünwald

This dissertation consists of two parts, with the common theme

Promotores: Prof.dr. J.N. Kok, Prof.dr. C.A. Mandemakers, Co-Promotor: G. Bloothooft

In my research, we developed a method to distinguish knots. A knot is a mathematical depiction of the everyday knot that occurs in ropes and cords.

We explored how to morphologically classify well-resolved jetted radio-loud active galactic nuclei (RLAGN) in the LOw Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) using machine learning.

In this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional contexts, we have focused on computational fluid dynamics (CFD) simulations.

Promotor: Prof.dr. T.H.W. Bäck

This dissertation focuses on developing new mathematical and statistical methods to properly represent time-varying covariates and model them within the context of time-to-event analysis.