103 search results for “machine learning” in the Public website
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Massively collaborative machine learning
Promotor: J. N. Kok, Co-promotor: A. J. Knobbe
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Computational speedups and learning separations in quantum machine learning
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…
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Reliable and Fair Machine Learning for Risk Assessment
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.
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Machine learning and computer vision for urban drainage inspections
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.
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Automated machine learning for dynamic energy management using time-series data
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…
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of post-translationally modified peptides in Streptomyces with machine learning
The ongoing increase in antimicrobial resistance combined with the low discovery of novel antibiotics is a serious threat to our health care.
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The flux and flow of data: connecting large datasets with machine learning in a drug discovery envirionment
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…
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Machine learning-based NO2 estimation from seagoing ships using TROPOMI/S5P satellite data
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.
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PNAS Paper Prize for quantum machine learning
‘We hope our paper highlights the possibilities and benefits of including artificial intelligence in quantum physics to do new discoveries.’ Vedran Dunjko of the Leiden Institute of Advanced Computer Science contributed to a paper that was published in PNAS last year and now received a Cozzarelli Prize…
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Frans Rodenburg
Science
f.j.rodenburg@biology.leidenuniv.nl | +31 71 527 2727
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Wouter van Loon
Faculteit der Sociale Wetenschappen
w.s.van.loon@fsw.leidenuniv.nl | +31 71 527 2727
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Optimally weighted ensembles of surrogate models for sequential parameter optimization
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.
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Simon Marshall
Science
s.c.marshall@liacs.leidenuniv.nl | +31 71 527 2727
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I-Fan Lin
Science
i.lin@liacs.leidenuniv.nl | +31 71 527 2727
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Philipp Kropf
Science
p.kropf@cml.leidenuniv.nl | +31 71 527 2727
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Modelling the interactions of advanced micro- and nanoparticles with novel entities
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.
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Data-driven donation strategies: understanding and predicting blood donor deferral
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.
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Exploring big data approaches in the context of early stage clinical
Als gevolg van de grote technologische vooruitgang in de gezondheidszorg worden in toenemende mate gegevens verzameld tijdens de uitvoering van klinische onderzoeken.
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Network analysis methods for smart inspection in the transport domain
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.
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Michael Lew
Science
m.s.lew@liacs.leidenuniv.nl | +31 71 527 7034
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Algorithm selection and configuration for Noisy Intermediate Scale Quantum methods for industrial applications
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.
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Tom Kouwenhoven
Science
t.kouwenhoven@liacs.leidenuniv.nl | +31 71 527 4799
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Surendra Balraadjsing
Science
s.balraadjsing@cml.leidenuniv.nl | +31 71 527 2727
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Numerical exploration of statistical physics
In this thesis, we examine various systems through the lens of several numerical methods.
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Data-Driven Risk Assessment in Infrastructure Networks
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'.
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A document classifier for medicinal chemistry publications trained on the ChEMBL corpus
Source: J Cheminform, Volume 6, Issue 1 (2014)
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Gerard van Westen
Science
gerard@lacdr.leidenuniv.nl | +31 71 527 3511
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Rayyan Toutounji
Faculteit der Sociale Wetenschappen
r.toutounji@fsw.leidenuniv.nl | +31 71 527 2727
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Guilherme D'Andrea Curra
Faculteit Archeologie
g.dandrea.curra@arch.leidenuniv.nl | +31 71 527 2727
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Diego Barbosa Arize Santos
Faculteit der Sociale Wetenschappen
d.barbosa.arize.santos@fsw.leidenuniv.nl | +31 71 527 2727
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Model-assisted robust optimization for continuous black-box problems
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.
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Spectral imaging and tomographic reconstruction methods for industrial applications
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.
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Methods and Tools for Mining Multivariate Time Series
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.
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Robust rules for prediction and description.
In this work, we attempt to answer the question:
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Fons Verbeek
Science
f.j.verbeek@liacs.leidenuniv.nl | +31 71 527 5773
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Chen Li
Science
c.li@cml.leidenuniv.nl | +31 71 527 2727
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Matthijs van Leeuwen
Science
m.van.leeuwen@liacs.leidenuniv.nl | +31 71 527 7048
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Evert van Nieuwenburg
Science
e.p.l.van.nieuwenburg@liacs.leidenuniv.nl | +31 71 527 5523
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Using cryo-EM methods to uncover structure and function of bacteriophages
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.
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Alex Brandsen
Faculteit Archeologie
a.brandsen@arch.leidenuniv.nl | +31 71 527 2727
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Automated de novo metabolite identification with mass spectrometry and cheminformatics
Promotor: Prof.dr. T. Hankemeier, Co-Promotores: T. Reijmers, L. Coulier
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Filter-based reconstruction methods for tomography
Promotor: K.J. Batenburg
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Novel analytical approaches to characterize particles in biopharmaceuticals
Particles are omnipresent in biopharmaceutical products. In protein-based therapeutics such particles are generally associated with impurities, either derived from the drug product itself (e.g. protein aggregates), or from extrinsic contaminations (e.g. cellulose fibers).
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Aske Plaat
Science
a.plaat@liacs.leidenuniv.nl | +31 71 527 7065
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Elise Dusseldorp
Faculteit der Sociale Wetenschappen
elise.dusseldorp@fsw.leidenuniv.nl | +31 71 527 8046
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Guillermo Guerrero Egido
Science
g.guerrero.egido@biology.leidenuniv.nl | +31 71 527 4791
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Marjolein Fokkema
Faculteit der Sociale Wetenschappen
m.fokkema@fsw.leidenuniv.nl | +31 71 527 7996
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Alina Karakanta
Faculty of Humanities
a.karakanta@hum.leidenuniv.nl | +31 71 527 2978
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Felix Frohnert
Science
f.frohnert@liacs.leidenuniv.nl | +31 71 527 2727
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Daniel Vale
Faculteit Rechtsgeleerdheid
d.s.vale@law.leidenuniv.nl | +31 71 527 8838