Team

Centrum Wiskunde & Informatica (CWI), Amsterdam



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Enrico Camporeale

Enrico is a staff member in the Multiscale Dynamics group. His research interests cover Computational Plasma Physics, Machine Learning, Space Weather, Solar Wind Turbulence, Plasma Kinetic Theory. He enjoys working on interdisciplinary Physics projects that have a strong component of Applied Mathematics. He is the team leader of the CWI-INRIA project 'Data-enhanced simulations for Space Weather predictions'.

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Svetlana Dubinkina

Svetlana Dubinkina is a staff member of Scientific Computing group. Her current research is focused on studying statistical properties of numerical methods, developing new data-assimilation methodologies, and applying these to different problems, such as estimations of past climate states, of gas reservoir structure, and predictions of wind farm outputs.

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Simon Wing

Simon Wing is a member of Dr. Enrico Camporeale’s team at CWI. He is also associated with The Johns Hopkins University Applied Physics Laboratory and University of Maryland. His research interests include modeling the open field line particle precipitation; modeling the magnetotail pressure, density, and temperature; geosynchronous environment; solar wind-magnetosphere interaction; field-aligned currents; and space weather. US NOAA Space Wether Prediction Center (SWPC) routinely broadcasts Kp forecasts from his Kp models.

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Mandar Chandorkar

Mandar is a PhD student in the Multiscale Dynamics group. His research domain is applied Mathematics in the areas of Machine Learning, Large Scale Inference, Physical Systems and Computer Science. He is currently pursuing his PhD in the topic "Machine Learning for Space Weather predictions".

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INRIA, Paris



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Michele Sebag

With a background in maths (Ecole Normale Supérieure), Michèle Sebag went to industry (Thalès) where she started to learn about computer science, project management, and artificial intelligence. She got interested in AI, became consulting engineer, and realized that machine learning was something to be. She was offered the opportunity to start research on machine learning for applications in numerical engineering at Laboratoire de Mécanique des Solides at Ecole Polytechnique.

After her PhD at the crossroad of machine learning (LRI, Université Paris-Sud Orsay), data analysis (Ceremade, Université Paris-10 Dauphine) and numerical engineering (LMS, Ecole Polytechnique), she entered CNRS as research fellow (CR1) in 1991.

In 2001, she took the lead of the Inference and ML group, now ML & Optimization, at LRI, Université Paris-Sud. In 2003 she founded together with Marc Schoenauer the TAO (ML & Optimization) INRIA project.

Her research interests include reinforcement learning, preference learning, information theory for robotics and surrogate optimization.

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Cyril Furtlehner

Cyril Furtlehner is a permanent research member of the Inria Saclay project-team TAO. Originally trained in theoretical physics, holding a Phd of Paris VI University dealing with quantum disordered systems, he eventually joined first the Inria team PREVAL in Rocquencourt to work on stochastic processes and more applied research themes like the self organization of a fleet of automated vehicles.

After being hired in the TAO team, his scientific interests shifted to interdisciplinary subjects concerning statistical physics problems related to machine learning, optimization and traffic inference, as well as stochastic particle processes related to microscopic traffic modeling. Recent activities are focusing on inverse Markov random field problems for large scale inference algorithms developments.

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Aurelien Decelle

Aurélien Decelle is a permament assistant professor in computer science at Université Paris-sud in the project-team TAO since 2014. His research interests focus on statistical physics applied to Machine Learning, inverse problems and Bayesian inference. He is particularly interested in neural networks and the application of machine learning technics to physics.

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External Collaborators



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Yuri Sphrits (UCLA/MIT)

Dr Shprits' primarily area of scientific research is understanding the dynamics of the radiation belts and their effect on satellites. Dr Shprits has developed codes to quantify the dynamical evolution of the radiation belts. He has also developed codes to quantify quasi-linear scattering rates due to wave-particle interactions. He has quantified the effects of scattering by ELF/ VLF and ULF waves and identified a number of critical effects associated with radial diffusion, local acceleration and loss of the radiation belt electrons.

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