Bevorderen van AutoML-systemen gericht op machine learning-taken op basis van aardobservatie datasets.

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

This thesis advances automata learning, a key area in computer science, with applications in software verification, biological analysis, and autonomous technologies. It explores three main themes: first, it introduces a passive learning algorithm for generating compact probabilistic models from positive…

In this work we describe three methods to improve the performance of Quantum Field Theory calculations.

Uncertainty and noise are frequently-encountered obstacles in real-world applications of numerical optimization. The practice of optimization that deals with uncertainties and noise is commonly referred to as robust optimization.

We are living in an information era where the amount of image and video data increases exponentially.

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…

Binnen het brede netwerk van de Universiteit Leiden neemt het instituut deel aan onderzoek binnen de volgende profileringsgebieden:

The database research community has made tremendous strides in developing powerful database engines that allow for efficient analytical query processing.

Humans share meaning through language. Over time, repeated interactions have shaped languages into forms that match our cognitive preferences, making them structured, expressive, easy to learn, and ultimately, meaningful.

Reinforcement learning is a framework that enables agents to learn in a manner similar to humans, i.e. through trial and error. Ideally, we would like to train a generalist agent capable of performing multiple tasks and achieving various goals.

Transport inspectorates are looking for novel methods to identify dangerous behavior, ultimately to reduce risks associated to the movements of people and goods. We explore a data-driven approach to arrive at smart inspections of vehicles.

This thesis mainly focuses on multimodal understanding and Visual Question Answering (VQA) via deep learning methods. For technical contributions, this thesis first focuses on improving multimodal fusion schemes via multi-stage vision-language interactions.

Promotor: Prof.dr. H.A.G. Wijshoff

De ontwikkeling en implementatie van een schaalbare, veilige en geïntegreerde infrastructuur van waterstof te bevorderen ter ondersteuning van de energietransitie.

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…

This thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.

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

Software development is a complex process. It is important that software products become stable and maintainable assets.

Promotor: Prof.dr. J.N. Kok

Intelligentie was lange tijd hét kenmerk van de mensheid. Maar inmiddels beginnen computers ons naar de kroon te steken. Kunstmatige intelligentie komt in hoog tempo onze samenleving binnen, en blijkt dingen te kunnen die tot voor kort uniek menselijk leken. Maar hoe is een computer slim? En wat zegt…

Creëren van een framework voor de schatting van milieuparameters.

In this thesis, we have studied cell images from two types of cells, including pollen grains and the immune cells, neutrophils. These images are captured using a bright field microscope and a confocal microscope.

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

Dit project beoogt de ontwikkeling van geavanceerde data-analysemethoden voor monitoring en het vergroten van ons inzicht in de dynamiek van de biodiversiteit in natuurgebieden zoals de Oostvaardersplassen.

In the era of big data, extracting insights from complex datasets is a key challenge. This thesis demonstrates the superiority of graph-based methods over traditional clustering (e.g., k-means, DBSCAN) and outlier detection for analyzing high-dimensional and noisy data.

The focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.

Patients share valuable advice and experiences with their peers in online patient discussion groups.

Quantum computers are a proposed fundamentally new type of computer. They aim to perform some computations much faster than previously possible by exploiting phenomena at the quantum scale, called superposition and entanglement.

Onderzoeken hoe gegevens afkomstig van draagbare technologieën kunnen worden gebruikt om effectieve gedragsveranderingen te identificeren die hopelijk zullen leiden tot gezondheidsverbeteringen.

Lezing

OpenML stelt wetenschappers in staat om transparant en samenwerkend, reproduceerbaar AI-gedreven onderzoek te doen.

Promotor: Prof.dr. J.N. Kok

Verbeteren van pandemische paraatheid via een gezamenlijk privacy-by-design datamodelleringsplatform.

Search and conversational systems have become central to how people access information and perform tasks.

Promotie

De Faculteit der Sociale Wetenschappen wil bijdragen aan de versterking en ontwikkeling van wereldwijde sociale wetenschap, geïnspireerd door uitdagingen en ideeën uit alle delen van de wereld.

Technologie, recht en rechtvaardigheid is een van de vier facultaire profileringsthema's van de Faculteit der Rechtsgeleerdheid.

Het wetenschappelijk van de onderzoek van de Faculteit Governance and Global Affairs is georganiseerd binnen verschillende instituten en centres in Den Haag, stad van vrede, veiligheid en recht.

De faculteit biedt een internationale academische omgeving met 36% van de studenten, promovendi en onderzoekers uit het buitenland.

Onderzoekers van LIACS werken aan het bedenken van de computers van morgen die de ruggengraat zullen vormen van de Cloud en Edge computing paradigma’s en ‘the Internet of Things’. In dit verband zijn we betrokken bij onderzoek en ontwikkeling van high performance computing systemen, embedded & real-time…

Promotor: Prof.dr. S. Haring

In 2018, the number of mobile phone users will reach about 4.9 billion. Assuming an average of 5 photos taken per day using the built-in cameras would result in about 9 trillion photos annually.

Quantum hardware comes with a different computing paradigm and new ways to tackle applications. Much effort has to be put into understanding how to leverage this technology to give real-world advantages in areas of interest for industries such as combinatorial optimization or machine learning.

Promotor: J. N. Kok, Co-promotor: A. J. Knobbe

With the dramatic growth of the image data on the web, there is an increasing demand of the algorithms capable of understanding the visual information automatically.

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.

In this work, we attempt to answer the question:

Promotores: Prof.dr. F.S. de Boer, Prof.dr. J.J.M.M. Rutten (Radboud Universiteit Nijmegen)

Big Science, commonly defined as conventional science made big in three dimensions, namely organizations, machines, and politics, brings a plethora of different stakeholders together, often for a long period of time. This includes policymakers, scientists, (scientific) managers as well as local “host”…