Computers zijn in staat om ongelooflijk nauwkeurige voorspellingen te doen op basis van machine learning. Met andere woorden, deze computers kunnen zonder tussenkomst leren als ze eenmaal door mensen zijn voorgeprogrammeerd. Bij LIACS verkennen en verleggen we de grenzen van wat een revolutionaire nieuwe…

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

Computers zijn in staat om ongelooflijk nauwkeurige voorspellingen te doen op basis van machine learning. Met andere woorden, deze computers kunnen zonder tussenkomst leren als ze eenmaal door mensen zijn voorgeprogrammeerd. Bij LIACS verkennen en verleggen we de grenzen van wat een revolutionaire nieuwe…

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…

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.

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.

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.

Time-series forecasting through modelling sequences of temporally dependent observations has many industrial and scientific applications. While machine learning models have been widely used to create time-series forecasting models, creating efficient and performant time-series forecasting models is…

The ongoing increase in antimicrobial resistance combined with the low discovery of novel antibiotics is a serious threat to our health care.

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

In this dissertation, we study partition-based models that can be used both for interpretable predictive modeling and for understanding data via interpretable patterns.

This thesis addresses questions on effectively using variational quantum circuits for machine learning tasks.

The marine shipping industry is one of the strongest emitters of nitrogen oxides (NOx), a pollutant detrimental to ecology and human health. Over the last 20 years, the pollution produced by power plants, the industry sector, and cars has been decreasing.

This thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies delved into the application of proteochemometrics (PCM), a machine learning technique that can be used to…

Cláudio de Sá voorspelt voorkeuren van mensen door gebruik te maken van ranglijsten. Dit doet hij door ‘klassieke’ machine learning-technieken aan te passen. Zijn werk kan onder andere gebruikt worden om de uitslagen van verkiezingen te voorspellen. Promotie op 16 december.

‘We hopen dat ons artikel de mogelijkheden en voordelen laat zien van het gebruik van kunstmatige intelligentie in de quantumfysica om nieuwe ontdekkingen te doen.’ Vedran Dunjko van het Leiden Institute of Advanced Computer Science droeg bij aan een artikel dat vorig jaar verscheen in PNAS. Het artikel…

De huidige en toekomstige quantumcomputers vormen dezelfde uitdaging als de laser in zijn begindagen. In theorie werd voorspeld dat de laser een bron van zeer speciaal, zeer krachtig licht zou zijn. Maar in die tijd waren er geen duidelijke toepassingen voor. Critici van het idee noemden het een probleem…

It is a common technique in global optimization with expensive black-box functions to learn a surrogate-model of the response function from past evaluations and use it to decide on the location of future evaluations.

Learning from small data sets in machine learning is a crucial challenge, especially when dealing with data imbalances and anomaly detection. This thesis delves into the challenges and methodologies of learning from small datasets in machine learning, with a particular focus on addressing data imbalances…

Novel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel entities, leading to the formation of aggregated contamination.

De Applied Machine Learning Days (AMLD) is een wereldwijd platform dat experts en deelnemers uit meer dan 40 landen samenbrengt uit industrie, academie en overheid op het gebied van machine learning. In de editie van dit jaar organiseerden leden van het BIAS-project een track rond het onderwerp 'Eerlijkheid…

Robert Ietswaart doet bij de befaamde Harvard Medical School in Boston onderzoek naar humane genregulatie. Hij ontwikkelde een machine learning-algoritme om beter te kunnen voorspellen of een kandidaatmedicijn bijwerkingen kan gaan vertonen. Ietswaart studeerde wiskunde en natuurkunde in Leiden, en…

Als gevolg van de grote technologische vooruitgang in de gezondheidszorg worden in toenemende mate gegevens verzameld tijdens de uitvoering van klinische onderzoeken.

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.

The research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency, which is evaluated based on donors' hemoglobin and ferritin levels.

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.

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

Leiden University and the Ministry of Infrastructure and Water Management are involved in a collaboration in the form of a research project titled 'Data-Driven Risk Assessment in Infrastructure Networks'

In this thesis, we examine various systems through the lens of several numerical methods.

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.

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.

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.

Predictive maintenance (PdM) is a maintenance policy that uses the past, current, and prognosticated health condition of an asset to predict when timely maintenance should occur.

Mining time series is a machine learning subfield that focuses on a particular data structure, where variables are measured over (short or long) periods of time.

In this work, we attempt to answer the question:

Promotie

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

Over onorthodoxe uitingen van wetenschap.

Bacteriophages, or phages for short, are the most abundant biological entity in nature. They shape bacterial communities and are a major driving force in bacterial evolution.

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.

The invention of neural networks marks a critical milestone in the pursuit of true artificial intelligence. Despite their impressive performance on various tasks, these networks face limitations in learning efficiently as they are often trained from scratch.