DataLearning is an interdisciplinary working group of researchers and students developing new technologies based on Data Assimilation and Machine Learning. DataLearning came out of the idea to couple and integrate Data Assimilation with Machine Learning technologies in order to exploit the best features of both.

The group fosters effective communication between scientists coming from different scientific disciplines and departments at Imperial College London and other institutions. DataLearning focuses on discussion and activity about Data Assimilation with/and/or Machine Learning. It is important for each scientist to learn about achievements of colleagues to tackle complex scientific problems.

In addition to regular meetings, we organise scientific seminars and workshops, where both experienced scientists and early stage researchers with different backgrounds will share their ideas and coordinate their efforts on relevant subjects. The exchange scheme welcomes students at all levels. Arrangements will be made for joint supervision of MSc and PhD students by the collaborating supervisors.

Group Meetings

For information on future meetings visit:

Group Leader
Dr Rossella Arcucci (DSI, ICL) 

Dr César A Quilodran Casas
Philip Nadler

Read more about the Data Learning Group by visiting their webpage.

Current projects


PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE)

PREMIERE brings together a multi-disciplinary team of researchers to create unprecedented impact across sectors through the creation of a next-generation predictive framework for complex multiphase systems.

Importantly, the framework methodology will span purely physics-driven, CFD-mediated solutions at one extreme, and data-centric solutions at the other where the complexity of the phenomena masks the underlying physics.

The framework will advance the current state-of-the-art in uncertainty quantification, adjoint sensitivity, data-assimilation, ensemble methods, CFD, and design of experiments to 'blend' the two extremes in order to create ultra-fast multi-fidelity, predictive models, supported by cutting-edge experimental investigations.

Find out more about the project.

Read an Imperial News Story on the project. 


Health assessment across biological length scales for personal pollution exposure and its mitigation

INHALE assesses the impact of air pollution on personal health in urban environments.

This multidisciplinary research project will develop a physics based, multi-scale approach across biological length scales from the cell, lung, person up to the neighbourhood scale.

The research involves integrated modelling of air pollution and air-flow. It will look at the effect of interventions such as roadside hedges or medication for at-risk people such as asthmatics. INHALE will examine pollutants’ potential for cell and tissue damage, and how this relates to health outcomes.

Find out more about the project. 

DataLearning working group


DataLearning Group:

PhD ResearchersPostgraduate StudentsAlumni
Dr Anna K Schroeder (University of Cambridge, UK) Alex Harston (Bioengineering, ICL) Jamal Afzali (Computing, ICL)  Adrian Lowenstein (Computing, ICL)
Dr Asiri Obeysekara (Chemical Engineering,ICL) Alvaro Arroyo Nunez (Electrical and Electronic Engineering, ICL) JD Pizzutti (LSE, UK) Edward M Lim (Computing, ICL)
Dr Boumediene Hamzi (Maths, ICL) Benjamin J Scharpf (Computing, ICL) Lingjun Liu (Computing, ICL) Gabor Tajnafoi (Computing, ICL)
Dr Claire Heaney (Earth Science & Engineering, ICL) Calum Pennington (Life Science, ICL) Julian Mack (Computing, ICL) Lamya Moutiq (Centrale Marseille, France)
Dr Evelyn Heylen (EEE, ICL) Caterina Buizza (EEE, ICL)  Maddalena Amendola (University of Pisa, Italy) Tolga Dur (Computing, ICL)
Evgeny Dyshlyuk (Earth Science, ICL) Clémence Le Cornec (Civil and Environmental Engineering , ICL) Maxime Redstone Leclerc (Computing, ICL)  Xiaonan Chong (Computing, ICL)
Dr Fangxin Fang (Earth Science & Engineering, ICL) Jingqing Zhang (DSI, ICL) Pratha Khandelwal (Computing, ICL)  
Dr James A Scott-Brown (Oxford University) Jemimah-Sandra Samuel (Earth Science & Engineering, ICL) Rose McNally (Computing, ICL)  
Dr Julio Amador Díaz López (DSI, ICL) Luis Baca Ruiz (DECSAI, University of Granada, Spain) Robin Hendrickx (Computing, ICL)  
Dr Julio Perez Olvera (Electrical and Electronic Engineering, ICL) Mihai Suteu (DSI, ICL)    
Dr Kezhi Li (Electrical and Electronic Engineering, ICL) Mohammad Maccido Usman (Earth Science & Engineering, ICL)    
Dr Luke M Phillipson (Physics, ICL) Pablo Ortega San Miguel (Computing, ICL)    
Dr Miguel Molina Solana (University of Granada, Spain) Pan Wang (DSI, ICL)    
Miguel Xochicale (King's College London, UK) Robin Evers (Math, ICL)    
Dr Ovidiu Serban (DSI, ICL) Stefano Marrone (University of Naples Federico II, Italy)    
Dr Pablo Salinas (Earth Science & Engineering, ICL) Vinicius L S Silva (Earth Science & Engineering, ICL)  
Dr Sesh Kumar (DSI, ICL) Zainab Titus (Earth Science & Engineering, ICL)    
Dr Silvia Muceli (Chalmers University of Technology, Sweden)      
Dr Yuan Luo (Computing, ICL)      
Zainab Titus (Earth Science & Engineering, ICL)      
The DataLearning Group meet weekly in the DSI


The 3rd MLDADS 2021 workshop, part of the International Conference on Computational Science (ICCS), will be held virtually on line on the 16-18 June 2021. Join us!
For further information on the programme and registration, please visit the workshop's homepage.

Due to COVID-19, the 2nd MLDADS 2020 workshop, part of the International Conference on Computational Science (ICCS) to be held in Amsterdam 3-5 June 2020, was moved online. You can view a recording of our speakers' talks on YouTube.

The 1st MLDADS workshop was part of the International Conference on Computational Science (ICCS)
held in Faro, Portugal 12-14 June 2019. Visit this webpage for further information.