Description

Leader of the group Scientific Computing: Daan Crommelin.

The SC groups develops efficient mathematical methods to simulate and predict real-world phenomena with inherent uncertainties. Such uncertainties arise from e.g. uncertain model parameters, chaotic dynamics or intrinsic randomness, and can have major impact on model outputs and predictions. Our work is targeted in particular at applications in climate, energy, finance and biology. In these vital areas, the ability to assess uncertainties and their impact on model predictions is of paramount importance. Expertise in the SC group includes uncertainty quantification, data assimilation, stochastic multiscale modeling and risk assessment. The availability of data to inform and improve simulations and predictions, for example through learning and data-driven modeling, plays an important role in our research.

 

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News

Smart mobility start-up Skialabs launched by CWI researchers

Smart mobility start-up Skialabs launched by CWI researchers

Traffic flows in cities could be managed much more efficiently thanks to cutting-edge technology pioneered by the new start-up Skialabs. By using huge dataflows collected in cities, the Skialabs platform provides a real-time view on the mobility flows in the city. This allows creating cost-effective and sustainable mobility services that react instantly to the demands of the end-users.

Smart mobility start-up Skialabs launched by CWI researchers - Read More…

Extreme events better investigated with new math method

Extreme events better investigated with new math method

Fortunately, incidents like extreme weather, earthquakes or massive power grid blackouts are a rare occurrence. Analysing properly how likely such rare incidents are to happen can be very valuable, but also very challenging. In his PhD thesis, Krzysztof Bisewski developed mathematical methods that greatly speed up simulations for estimating the probability of rare events.

Extreme events better investigated with new math method - Read More…

New data framework illuminates uncertainties in offshore wind farm conditions

New data framework illuminates uncertainties in offshore wind farm conditions

Wind speeds and wave heights can have a major effect on offshore wind farms. But because they are correlated, their combined significance for wind farm designs couldn’t be factored in until now. CWI researcher Anne Eggels developed methods which take the effect of such correlations or dependencies into account. Today, she will publicly defend her thesis at the University of Amsterdam.

New data framework illuminates uncertainties in offshore wind farm conditions - Read More…

Current events

Machine Learning in Quantitative Finance and Risk Management

  • 2020-07-02T09:00:00+02:00
  • 2020-07-02T17:00:00+02:00
July 2 Thursday

Start: 2020-07-02 09:00:00+02:00 End: 2020-07-02 17:00:00+02:00

on-line

Organised by Kees Oosterlee and Kristoffer Andersson (CWI)

Thursday July 2nd 2020

This workshop will take place, on-line, via zoom.us:
https://us02web.zoom.us/j/88570100112?pwd=NUZGOXpVLzJlcHQ1eEJpbWVLbE9mdz09
Meeting ID: 885 7010 0112, password: 811135

The workshop times are CET, Central European Time:

10:00 AM (Keynote) Christoph Reisinger (U. Oxford): "Deep xVA solver -- A neural network based counterparty credit risk management framework"
11:00 AM  Kristoffer Andersson (CWI): "Learning exposure profiles for portfolios of exotic derivatives"
11:50 AM Shashi Jain (IISc Bangalore): "Universal static hedging using a shallow neural network"

13:30PM Anastasia Borovykh (Imperial College, London): "To interact or not? On the convergence properties of interacting particle optimization"
14:20PM Shuaiqiang Liu (TU Delft): "Deep learning for large time-step simulations of stochastic differential equations"
15:15PM (Keynote) Yuying Li (U. Waterloo, Canada): "Asset allocation without pain: learning dynamic strategies directly from market data"

The speakers:

Christoph Reisinger is Professor of Applied Mathematics at Oxford's Mathematical Institute and Tutorial Fellow in Mathematics at St Catherine's College. He is Editor-in-Chief of The Journal of Computational Finance, and serves on the editorial board of Applied Mathematical Finance and the International Journal of Computer Mathematics.

Kristoffer Andersson is a PhD candidate in the group of Prof. Kees Oosterlee. He is currently working, in the context of a European Industrial Doctorates project, on counterparty credit risk management, XVA and optimal stopping problems, in combination with modern machine learning paradigms.

Dr Shashi Jain is currently an assistant professor in the department of management studies at Indian Institute of Science, Bangalore. He has fond memories of CWI, where he did his PhD under the guidance of Professor Kees Oosterlee. He worked as front office quant at ING, Amsterdam before taking up his current position at IISc. His research interest have primarily been on Monte Carlo methods in financial engineering, with particular focus on pricing of early exercise options. Other areas of interest include self exciting point processes with applications in market microstructure, portfolio allocation problems and real options.

Anastasia Borovykh is currently a post-doctoral researcher at Imperial College, London, working on understandable machine learning techniques.
She got her PhD in Mathematics on algorithms in financial mathematics and computational finance from the University of Bologna, Italy, 2018.

Shuaiqang Liu is a PhD candidate in the group of Prof. Kees Oosterlee. He is currently working on computational finance and machine learning, particularly developing fast data-driven numerical solvers.

Yuying Li is a professor at Cheriton School of Computer Science, University of Waterloo in Canada. Prior to joining Waterloo, she was a senior research associate in Computer Science Department at Cornell University 1988-2005. She is the recipient of the 1993 Leslie Fox first Prize in numerical analysis at Oxford England. Her main research interest includes financial data science (including supervised and unsupervised learning, clustering, anomaly detection, fraud detection, and data driven optimal decision), computational finance, and computational optimization.
Li has been an associate editor of the Journal of Computational Finance (since 2008),  the Journal of Finance and Data Science (since 2015). Li has been on the Advisory Board of the Journal of Financial Innovation since 2017. Presently, Li is the (graduate program) Director of Data Science at University of Waterloo.

Members

Associated Members

Publications

Current projects with external funding

  • Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
  • Physics based ICT: The digital twin in pipelines (DP-Trans)
  • Dronesurance
  • Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
  • Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
  • Verified Exascale Computing for Multiscale Applications (VECMA)
  • WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)

Related partners

  • DNV GL Netherlands B.V
  • Max Planck Institute for Informatics
  • Bull Sas
  • CBK Sci Con Ltd
  • Bayerische Akademie der Wissenschaften
  • Maritiem Research Instituut
  • MeitY
  • Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
  • Rijksuniversiteit Groningen
  • Suzlon Blades Technology
  • TNO
  • Technische Universiteit Eindhoven
  • Technische Universiteit Delft
  • Brunel University London
  • University College London
  • Universiteit Leiden
  • Universiteit van Amsterdam