504 search results for “algorithms” in the Public website
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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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Following in nature's footsteps
A neural network mimics how our brain works. Evolutionary algorithms use the principle of natural selection to solve complex problems. This kind of 'natural computing' is being used to improve the diagnosis of Parkinson's disease or the production of steel.
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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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Dirk van der Hoeven
Science
d.van.der.hoeven@math.leidenuniv.nl | +31 71 527 7146
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Structure and substructure in the stellar halo of the Milky Way
Promotor: K.H. Kuijken
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Computability of the étale Euler-Poincaré characteristic
Promotor: S.J. Edixhoven, L.D.J. Taelman
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Decompositions in algebra
We show that Kirchhoff ’s law of conservation holds for non-commutative graph flows if and only if the graph is planar. We generalize the theory of (Euclidean) lattices to infinite dimension and consider the ring of algebraic integers as such a lattice.
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Basis reduction for layered lattices
Promotor: H.W. Lenstra
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Links between cohomology and arithmetic
Promotor: S.J. Edixhoven
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System Verification Lab (SVL)
The correctness of computational systems is of great importance to our society, since it becomes ever more reliant on the benefits of computing.
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Exploring deep learning for multimodal understanding
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.
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Explainatory Data Analysis
The Explainatory Data Analysis group develops algorithms and theory that enable domain experts to explain data by finding interpretable patterns and models.
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Webinars
On this page you will find a collection of presentations and videos of the Florence Nightingale Colloquia, seminars at the faculty and other event recordings hosted by the Data Science Research Programme.
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Detection of Archaeological Sites in High Resolution Satellite Images
In this project we develop algorithms to automatically detect a particular type of archaeological sites in satellite images of the Alps.
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Software developments in automated structure solution and crystallographic studies of the Sso10a2 and human C1 inhibitor protein
Promotor: J.P. Abrahams, Co-Promotor: N.S. Pannu
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Ik kijk er naar uit om de komende jaren kruisverbanden te gaan leggen tussen de verschillende organisaties.
In november 2023 is Anne Fleur van Veenstra, wetenschappelijk directeur van TNO Vector, benoemd tot bijzonder hoogleraar ‘Governance van data en algoritmen voor stedelijk beleid’.
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Handbook of Natural Computing
Natural Computing investigates algorithms and phenonema based on nature to create better and new computer science innovations.
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Ahmed Mahfouz: 'The mystery of brain diseases, unravelled cell by cell'
Which brain cell does what, when Parkinson's disease arises? It won't be long before this jigsaw is solved piece by piece. Ahmed Mahfouz, computational biologist, combines bio-knowledge from Leiden with algorithms from Delft and is getting closer to finding the key.
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Computational Drug Discovery
Research in this group, headed by Gerard van Westen, focusses on computational methods integrated in different parts of the drug discovery process. More specifically, topics include innovative treatments for cancer, selectivity modeling, translational research, allosteric modulation, drug resistance…
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Data science
The majority of scientists, from archaeologists through to zoologists, collect enormous volumes of data. Their massive databases contain large amounts of information which is difficult for humans to filter. With a solid grounding in statistics and computer science, we can develop algorithms for analyzing…
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XAIPRE - Explainable AI For Predictive Maintenance
The project XAIPre (pronounce “Xyper”) aims to develop predictive maintenance system for the maritime industry using sensor technology and artificial intelligence. The project aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction…
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Proteochemometric modelling coupled to in silico target prediction
An integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules.
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NeuroSoC
NeuroSoC concentrates on multiprocessor systems on chip with in-memory neural processing units.
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Structured Parallel Programming for Monte Carlo Tree Search
The thesis is part of a bigger project, the HEPGAME (High Energy Physics Game). The main objective for HEPGAME is the utilization of AI solutions, particularly by using MCTS for simplification of HEP calculations.
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Data Science and Artificial Intelligence (BSc)
The focus of the Bachelor Data Science & Artificial Intelligence is on computer science, and its applications in Artificial Intelligence. You will receive a strong basis in mathematics, statistics and computer science, combined with advanced knowledge of machine learning, cognitive science, human-robot…
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Don't Blink: Detecting transiting exoplanets with MASCARA
This thesis describes the Multi-site All-Sky CAmeRA (MASCARA), which consists of two small robotic telescope designed to detect exoplanets around the brightest stars in the sky.
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Quantum computation with Majorana zero modes in superconducting circuits
Promotor: C.W.J. Beenakker, Co-Promotor: A.R. Akhmerov
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Harmonic duality : from interval ratios and pitch distance to spectra and sensory dissonance
This dissertation derives from the development of tools for algorithmic composition which extract pitch materials from sound signals, analyzing them according to their timbral and harmonic properties, putting them into motion through diverse rhythmic and textural procedures.
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Image analysis for gene expression based phenotype characterization in yeast cells
Promotores: T.H.W. Bäck, A. Plaat, Co-promotor: F.J. Verbeek
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Radicals in arithmetic
Promotor: Prof.dr. P. Stevenhagen, Co-promotor: Prof. dr. B. de Smit
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Real-time tomographic reconstruction
With tomography it is possible to reconstruct the interior of an object without destroying.
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The use of Deep Learning in the automated detection of archaeological objects in remotely sensed data
Generally the data from remote sensing surveys - the scanning of the earth by satellite or aircraft in order to obtain information about it - is screened manually in archaeology. However, constant monitoring of the earth's surface causes a huge influx of data of high complexity and high quality. To…
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Gibbs processes and applications
Gibbs processes, g-measures, fuzzy Gibbs states
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Multi-Objective Bayesian Global Optimization for Continuous Problems and Applications
A common method to solve expensive function evaluation problem is using Bayesian Global Optimization, instead of Evolutionary Algorithms.
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Een algemene normtheorie toegepast op open normen in het belastingrecht
On 14 May 2020, Pieter Rustenburg defended his thesis 'Een algemene normtheorie toegepast op open normen in het belastingrecht'. The doctoral research was supervised by Prof. A.O. Lubbers and Prof. J.L.M. Gribnau.
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Programme structure
This programme is aimed at students who have a strong interest in programming and computer science, as well as an interest in the cognitive psychology and artificial intelligence.
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Extrasolar Planet Detection Through Spatially Resolved Observations
Promotor: Prof. dr. I. Snellen, Co-Promotor: Dr. M. Kenworthy
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New scanning method turns objects inside out at high speed
What if you could watch a CT scan live, instead of analysing the images afterwards? If it is up to the Leiden mathematician Jan-Willem Buurlage, that will soon be a reality. He is developing methods to make the algorithms behind 3D scans faster. Quite a challenge: ‘Just like mathematicians, computers…
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Computational Network Science Lab
The Leiden Computational Network Science Lab (CNS Lab) researches methods for knowledge discovery from real-world network data.
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Arakelov invariants of Belyi curves
Promotores: Bas Edixhoven, Jean-Benoit Bost, Co-promotor: Robin de Jong
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Reinforcement learning
The Reinforcement Learning lab conducts research into Reinforcement Learning and Intelligent Combinatorial Algorithms.
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Inhibitor Selectivity: Profiling and Prediction
Less than 1 in 10 drug candidates that enter phase 1 clinical trials actually gets approved for human use.
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AI-MAPS
AI MAPS adopts a freedom and social well-being perspective and focuses on three themes to address key security challenges; Social disorder and public nuisances in neighbourhoods, High-impact crime, and Crowds and events.
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Discovering the preference hypervolume: an interactive model for real world computational co-creativity
In this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance introverted intuition using extraverted intuition's communication lines.
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Improved Strategies for Distance Based Clustering of Objects on Subsets of Attributes in High-Dimensional Data
This monograph focuses on clustering of objects in high-dimensional data, given the restriction that the objects do not cluster on all the attributes, not even on a single subset of attributes, but often on different subsets of attributes in the data.
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Quantum Delta NL research programme
Quantum Delta NL, a research programme in which Leiden University participates, has been awarded 615 million euros from the National Growth Fund to help develop the Netherlands into a top player in quantum technology.
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Focal-plane wavefront sensors for direct exoplanet imaging: Theory, simulations and on-sky demonstrations
One of the key limitations of the direct imaging of exoplanets at small angular separations are quasi-static speckles that originate from evolving non-common path aberrations (NCPA) to which the primary adaptive optics system is inherently blind. The main focus of this thesis is the development and…
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Progressive Indexes
Interactive exploration of large volumes of data is increasingly common, as data scientists attempt to extract interesting information from large opaque data sets. This scenario presents a difficult challenge for traditional database systems, as (1) nothing is known about the query workload in advance,…
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Predicting dementia
In the future, physicians may be able to identify dementia much earlier than they can today because a computer algorithm will be able to predict from brain scans how our memory is going to develop.