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…

Optimally solving real-world expensive Black-Box Optimization (BBO) problems w.r.t. real-world constraints, such as wall-clock time and computational costs, is extremely difficult and tedious.

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…

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

Promotores: H.W. Lenstra, K. Belabas (University of Bordeaux)

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.

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.

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

This thesis seeks to combine two different research topics; Multi-Objective Optimization and Complex Network Analysis.

This thesis applies data mining in commercial settings in the telecommunications industry.

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

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

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

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…

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.

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,…

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

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).

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

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

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

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

In this thesis, we address two problems concerning the tautological rings of the moduli space of curves.

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.

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

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…

Promotor: P.D. Grünwald

This dissertation is about Bayesian learning from data. How can humans and computers learn from data?

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

Principles of Environmental Sciences provides a comprehensive picture of the principles, concepts and methods that are applicable to problems originating from the interaction between the living and non-living environment and mankind. Both the analysis of such problems and the way solutions to environmental…

Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed.

Promotor: Prof.dr. T.H.W. Bäck

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

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

This dissertation consists of two parts, with the common theme

This thesis investigates the contribution of quantum computers to machine learning, a field called Quantum Machine Learning. Quantum Machine Learning promises innovative perspectives and methods for solving complex problems in machine learning, leveraging the unique capabilities of quantum computers…

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.

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.

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.

This study’s main aim is to test whether children’s learning behaviour explains why they do not benefit from a literate environment.

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Promotores: R. Tijdeman, K.J. Batenburg