Data Assimilation Laboratory - Data Science Institute - Imperial College London

Intro video to the Data Assimilation Laboratory - Data Science Institute

The data assimilation (DA) laboratory promotes and leads scientific advances and technological innovations through data assimilation, sensitivity/uncertainty/error analysis, design optimization and control, and computational modelling, simulation and visualisation methodologies. Our vision is to provide leadership, in the UK and beyond, in data assimilation as a strategic resource for scientific education, research and inquiry.

Research Aims

Our aim is to bring to bear recent developments in data assimilation into advanced models and apply these to science and engineering. The data assimilation techniques developed in the DA laboratory will be used to combine observation measurements with different models in Engineering.

A few application areas include:

  • Ocean and flooding;
  • Atmosphere;
  • Urban and indoor flows;
  • Oil reservoirs; 
  • Vehicle design;
  • Industrial processes;
  • Medical.

data matrix

We will introduce the following core research themes:
(1)       CFD/Solid-Mechanics/Radiation codes for science and engineering;
(2)       Optimized accuracy of models;
(3)       Data assimilation techniques;
(4)       Uncertainty analysis;
(5)       Rapid responding models;
(6)       Data collection optimization;
(7)       Design optimization;
(8)       Control/management;
(9)       High performance computing.


 Our aim is to bring to bear recent developments in data assimilation into advanced models and apply these to science and engineering. The data assimilation techniques developed in the DA laboratory will be used to combine observation measurements with different models in Engineering.


Chris PainHead, Professor Chris Pain
He is a Professor in the department of Earth Science and Engineering at Imperial College London (ICL), UK, as well as head of the Applied Computation and Modelling Group (AMCG), which is the largest department research group at ICL and comprises of about 70 research active scientists. The group has core research interests in numerical methods for ocean, atmosphere and climate systems, engineering fluids including multiphase flows, neutral particle radiation transport, coupled fluids-solids modelling with discrete element methods, turbulence modelling, inversion methods, imaging, and impact cratering. He was honoured by Imperial College London by winning its Research Excellence Award in 2010, in recognition of his world-leading research.

Executive Manager: Dr. Fangxin Fang
Her research areas focus on  predictive modelling (data assimilation methods, model reduction and optimal controls) in geophysical models as well as applications in ocean, atmospheric, multiphase flows and environmental problems. The application areas she mostly look at are environmental issues (air pollution), natural hazard (flooding, for example), ocean and engineering problems.

Chief Scientist:  Prof. I.M.Navon, Department of Scientific Computing, Florida State University
He has three main research interests:  Advanced 4-D Var Data-Assimilation Methods,  Large-Scale Minimization, and  Ensemble Kalman filter methods.

DataLearning working groupDr. Rossella Arcucci
Her work focuses on numerical and parallel techniques for accurate and efficient Data Assimilation by exploiting the power of machine learning models.

Lab Investigators

Professor Matthew Jackson:  His research interests include: Investigation, prediction, quantification, and measurement of transport processes in heterogeneous geological porous media, using field, experimental, mathematical and numerical techniques.

Professor Omar Matar : Omar Matar is a Professor of Fluid Mechanics in the Department of Chemical Engineering. His research interests are in transport phenomena and multiphase flows with a wide range of applications: process intensification, light-mediated manufacturing, surfactant-replacement therapy, crude-oil and food processing, coating flow technology, manufacturing of pharmaceuticals, pipeline transportation of crude oil, distillation, and microfluidics. He has studied a wide range of data-rich problems in fluid mechanics including surfactant transport on non-Newtonian layers, phase inversion in concentrated emulsions, thin film flows over rapidly rotating discs, nonlinear bubble sound interactions, fouling in heat exchangers in crude oil distillation units, dynamics of liquids spreading on compliant substrates, multiphase flow in large-diameter pipes, advanced experimental and numerical methods for the prediction of complex vapour liquid annular flows, and the removal of soft-solids adhering to solid substrates.

Professor Cedo Maksimovic: After having worked for 24 years at the Faculty of Civil Engineering, University of Belgrade and joining the EWRE Section in 1996 Professor Cedo Maksimovic created and heads the UWRG (Urban Water Research Group) at Imperial College London. His research fields include applied fluid mechanics in urban water systems: storm drainage, urban flooding water supply and interactions of urban water systems and infrastructure with the environment. In addition to lecturing on the MSc and UG courses, Prof. Maksimovic serves as a project co-ordinator of EPSRC, EU and UNESCO projects in UK, and other projects in Europe and in other continents dealing with the above topics.

Professor Eric YeatmanEric M. Yeatman FREng, FIEEE has been a member of academic staff in Imperial College London since 1989, and Professor of Micro-Engineering since 2005. He was appointed Head of the Department of Electrical and Electronic Engineering in Sept. 2015. He has published more than 200 papers and patents on optical devices and materials, micro-electro-mechanical systems (MEMS), and other topics. 

Lab Affiliates

Professor Jiang Zhu: Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences. Prof Jiang Zhu (director of IAP) is an expert in Ensemble Kalman filter and ocean modelling.

Dr Razvan Stefanescu: From Virginia Tech. His research interests include: numerical methods for inverse problems, PDE - constrained optimization, reduced order optimization methods; adjoint-based space-time adaptive solution algorithms for sensitivity analysis and inverse problems; numerical methods (theory, algorithms and computer programs) for optimal control problems governed by PDEs and by variational inequalities; Bayesian methods and uncertainty quantification and optimal design of experiments for nonlinear inverse problems.

Dr Jefferson GomesFrom University of Aberdeen. His interests include: Computational multi-fluids dynamics (CMFD), multi-physics modelling, adaptive numerical methods, finite element methods (FEM), computational optimisation, porous media flow, turbulence modelling, nuclear criticality, reactor physics, heat and mass transfers.

Get Involved

We are currently growing the Data Assimilation Lab. Interested in becoming an affiliate? Please contact:

DA representation

Projects and Collaborations