Description
Leader of the group Stochastics: Bert Zwart.
Many natural and man-made systems and processes are driven by random phenomena. Examples can be found in areas such as communication, energy, information, logistics, and transportation systems. In order to model, describe, and improve such systems, our group develops and studies probabilistic, operational and statistical models. Our group’s analysis of these models relies on techniques from fundamental probability theory, queueing theory, stochastic scheduling, spatial stochastics and stochastic geometry. Our work with various companies in these areas, as well as with top researchers and universities across the globe, ensures our position at the forefront of every aspect of this research field.
Vacancies
No vacancies currently.
News
CWI researchers develop new mathematical framework to explain the emergence of large blackouts
The study shows that the statistical distribution of blackout sizes can be linked to city sizes. Bert Zwart and his colleagues indicate that it might be more effective to help consumers react to a blackout instead of performing network upgrades.
In Memoriam Willem van Zwet (1934-2020)
Prof. Willem van Zwet from Leiden University was advisor to the Mathematisch Centrum (now called CWI) from 1965-1984, chair of CWI’s Science Committee and Member of the Board. Throughout his career, Van Zwet played a key role in academic mathematical statistics in the Netherlands.
CWI develops new mathematical techniques for optimal relocation and dispatch of fire trucks
On Monday 11 May Dmitrii Usanov will defend his PhD thesis. His research focused on dispatching and relocating resources in the emergency domain and the reduction of response times.
NEW PROBABILISTIC TECHNIQUES FOR THE ANALYSIS OF ENERGY NETWORKS UNDER UNCERTAINTY
Tommaso Nesti, PhD student at CWI’s Stochastics group, has developed new mathematical techniques to help model, control and operate modern-day power grids in the presence of uncertainty.
Members
Associated Members
Publications
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Zweers, B.G. (2021, January 19). Optimization of hinterland container transportation and terminal operations.
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Koch, T, & Dugundji, E.R. (2020). A review of methods to model route choice behavior of bicyclists: inverse reinforcement learning in spatial context and recursive logit. In ACM SIGSPATIAL. doi:10.1145/3423335.342
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Kleinrouweler, J.W.M. (2020, October 28). Modeling and optimization of network assisted video streaming.
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Mahfoud, M, Bernasco, W, Bhulai, S, & van der Mei, R.D. (2020). Forecasting spatio‑temporal variation in residential burglary with the integrated Laplace approximation framework: Effects of crime generators, street networks, and prior crimes. Journal of Quantitative Criminology, 1–28. doi:10.1007/s10940-020-09469-3
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Patch, B.J, & Zwart, A.P. (2020). Analyzing large frequency disruptions in power systems using large deviations theory. In 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings. doi:10.1109/PMAPS47429.2020.9183551
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Usanov, D. (2020, May 11). Emergency response networks: dynamic dispatching and relocation of resources.
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van Kampen, J.R.A, Jayaraj, M.A, Pauwels, E.J.E.M, van der Mei, R.D, & Dugundji, E.R. (2020). Understanding the relation between distance and train station choice behavior of cyclists in the western region of the Netherlands. Procedia Computer Science, 170, 98–106. doi:10.1016/j.procs.2020.03.013
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Buijs, R.J.W, Koch, T, & Dugundji, E.R. (2020). Using Neural Nets to Predict Transportation Mode Choice: An Amsterdam Case Study. In The 11th International Conference on Ambient Systems, Networks and Technologies (pp. 115–122). doi:10.1016/j.procs.2020.03.015
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Nesti, T. (2020, March 30). Stochastic analysis of energy networks.
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Saxena, M, Kapodistria, S, & Núñez Queija, R. (2020). Perturbation analysis of two queues with random time-limited polling. In Queueing Theory and Network Applications.
Current projects with external funding
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Ontwikkeling van Data Analytics, Data Science en Crypto Analyse technieken ()
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Impactstudie Noord/Zuidlijn Gemeente Amsterdam
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Samenwerkingsovereenkomst Stichting 113 / CWI - Zelfmoordpreventie
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Data-driven optimization for a vital elderly care system in the Netherlands (DOLCE VITA)
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Dynamic urban traffic flow management using floating-car, planning, and infrastructure data (Dynafloat)
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Lane Analysis & Route Advisor (LARA)
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Evidence-based effective monitoring and control of Covid-19 after the initial outbreak (None)
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Het dectecteren van (corona-gerelateerde) problemen bij de hulpvragers van 113 Zelfmoordpreventie voor en de uitbraak van de coronapandamie in Nederland middels state-of-the-art Natural Language Processing (NLP) (None)
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State estimation for spatio-temporal point processes with applications to criminology (None)
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PPS Engie
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Robust, Effective and Adaptable Ship Designs for Uncertain Transition Paths (READINESS)
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Urban Mobility Observatory (UMO)
Related partners
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De Verkeersonderneming
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Gemeente Amsterdam
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RIVM
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Stichting 113
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Universiteit Wageningen
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Academisch Medisch Centrum
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Stichting Sensor City
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Engie Services West Nederland B.V.
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Erasmus Universiteit Rotterdam
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Leids Universitair Mediach Centrum
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Technische Universiteit Eindhoven
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Technische Universiteit Delft
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Universiteit Utrecht
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Vrije Universiteit Amsterdam