Prof. Alberto Carrassi
(University of Bologna, Italy, and University of Reading, UK)

Short-bio:

Data assimilation is the research area of the Prof. Alberto Carrassi. His motivation coming from theoretical developments of climate science (prediction, attribution, and impact) and environmental sciences in general. A characterizing aspect of his activity is the inter-disciplinary nature between data assimilation, dynamic systems, and more recently machine learning. He has developed advanced methods based on the dynamic information of the system, studying the impact and mitigation of the model error, and he has contributed to the development of the first combined methods of data assimilation and machine learning. Prof. Alberto Carrassi is Coordinator of several national and international projects. Currently co-PI of the Scale Aware Sea Ice Project (SASIP) funded by the Schmidt Futures' Virtual Earth System Research Institute. SASIP studies the impact of global warming on the polar regions.

Webpage: https://www.unibo.it/sitoweb/alberto.carrassi or https://research.reading.ac.uk/meteorology/people/alberto-carrassi

Prof. Didier Auroux
(Université Côte d'Azur, France)

Short-bio:

Didier Auroux is a graduate of Ecole Normale Supérieure de Lyon (France). He received his PhD degree in Applied Mathematics in 2003 from University of Nice Sophia Antipolis (France), and got an assistant professor position at University of Toulouse (France) in 2004. He completed his habilitation thesis in 2008. Since 2009, he is Professor at the University of Nice Sophia Antipolis (Université Côte d'Azur since 2020). Deputy head of the mathematics department between 2014 and 2019, chair of the computing and mathematics committee for HPC in France since 2016, his research interests are in computational methods and their applications to data assimilation, geophysics, fluid dynamics, image processing, and more generally optimal control and inverse problems.

Webpage: https://math.unice.fr/~auroux/

Dr. Haroldo F. de Campos Velho
(Instituto Nacional de Pesquisas Espaciais , Brasil)

Short-bio:

Dr. Haroldo F. de Campos Velho received his BS degree (1982) in Chemical Engineering from the Pontifical Catholic University of Rio Grande do Sul (PUCRS), Brazil; D.Sc. (1992) and M.Sc. (1988) degrees in Mechanical Engineering on computational fluid dynamics and nuclear reactor physics, respectively, from the Federal University of Rio Grande do Sul (UFRGS), Brazil. He was a visiting scientist for the Istituto di Cosmo-Geofisica (Turim, Italy) – 1997, and Dept. of Atmospheric Science of the Colorado State University (USA) – 1998. He is a senior researcher of the National Institute for Space Research (INPE). He was Associate Director for Space and Environment of the INPE (2008-2010). His research interests include inverse problems, turbulence parameterization, data assimilation, and scientific computing. He and other colleagues, under leadership of the Prof. Orestes Llanes-Santiago, were awarded by the Cuban Academy of Science at 2016 (“Artificial Intelligence and Data Mining Applications to Fault Diagnosis and Parameter Estimation”) and 2021 (“New Paradigms in Fault Diagnosis in Industrial Systems”).

Webpage: http://www.lac.inpe.br/~haroldo

Dr. Takemasa Miyoshi
(RIKEN Center for Computational Science, Japan)

Short-bio:

Dr. Takemasa Miyoshi received his B.S. degree (2000) in theoretical physics on nonlinear dynamics from the Kyoto University, and M.S. (2004) and Ph.D. (2005) degrees in meteorology on ensemble data assimilation from the University of Maryland (UMD). Dr. Takemasa Miyoshi started his professional career as a civil servant at the Japan Meteorological Agency (JMA) in 2000. He was a tenure-track Assistant Professor at UMD in 2011. Since 2012, Dr. Miyoshi has been leading the Data Assimilation Research Team in RIKEN Center for Computational Science (R-CCS), working towards advancing the science of data assimilation with a deep commitment to education. Dr. Miyoshi's scientific achievements include more than 110 peer-reviewed publications and more than 130 invited conference presentations including the Core Science Keynote at the American Meteorological Society Annual Meeting (2015). Dr. Miyoshi has been recognized by several prestigious awards such as the Yamamoto-Syono Award by the Meteorological Society of Japan (2008), the Young Scientists' Prize by the Minister of Education, Culture, Sports, Science and Technology (2014), the Japan Geosciences Union Nishida Prize (2015), the Meteorological Society of Japan Award (2016) - the highest award of the society, the Yomiuri Gold Medal Prize (2018), and the Commendation by the Prime Minister for Disaster Prevention (2020).

Webpage: https://data-assimilation.riken.jp/~miyoshi/

Dr. Amos Lawless
(University of Reading, UK)

Short-bio:

Dr Amos Lawless is associate professor of data assimilation and inverse problems. He has a joint position in the Department of Mathematics and Statistics and the Department of Meteorology at the University of Reading. He is also part of the UK National Centre for Earth Observation (NCEO), where he has the roles of Head of Data Assimilation Methodology (jointly leading the Data Assimilation division with Prof Sarah Dance) and NCEO Training Lead.  He was previously a research scientist at the Met Office (1992-2001), developing the linear and adjoint models for the incremental 4D-Var system. His research interests are in the mathematical theory of data assimilation and its application to large, complex systems. Current research interests include particularly data assimilation for coupled atmosphere-ocean systems. 

Webpage: http://www.personal.reading.ac.uk/~sms00asl/

Dr. Max Yaremchuk
(Naval Research Laboratory, Stennis Space Center, USA)

Short-bio:

Dr. Max Yaremchuk is a research scientist at the Naval Research Laboratory in Stennis Space Center, USA. His research interests are in computational methods and their applications to data assimilation, using both variational and ensemble methods. He has an extensive experience in the development of 4D variational data assimilation systems in oceanography, including general circulation, sea ice and surface wave models of various complexity. Max Yaremchuk has been invited to serve as an expert for multiple NSF panels in computational mathematics and NASA oceanography programs. He is an author of more than 60 publications related to the development of variational data assimilation methods in oceanography and their applications.

Webpage: https://www.ocean.nrlssc.navy.mil/ (http://iprc.soest.hawaii.edu/people/max.php)

Prof. Marc Bocquet
(CEREA École des Ponts & EdF R&D, Île-de-France, France)

Short-bio:

Marc Bocquet has a PhD in theoretical physics from École Polytechnique, and a Habilation from Paris Sorbonne University. He has been a postdoc fellow in the physics departments of the University of Warwick and of the University of Oxford. He is currently Professor at École des Ponts and deputy director of the atmospheric environment research and teaching center (CEREA). He works in the field of data assimilation, machine learning, inverse problems, and dynamical systems in the geosciences, with applications to atmospheric chemistry as well as in environmental statistics. He develops new mathematical methods to better estimate the state of the atmosphere and the ocean, and their constituents, using large sets of observations and complex models. He is a Fellow of the European Centre for Medium-Range Weather Forecasts, and an Editor for the QJRMS, Foundation of Data Science and Frontiers in Applied Mathematics and Frontiers / Dynamical Systems.

Webpage: http://cerea.enpc.fr/HomePages/bocquet/

Prof. Peter Jan van Leeuwen
(Colorado State University, USA)

Short-bio:

His research is focused on the use of data assimilation and causality (cause and effect) inference for better understanding geophysical fluids, with emphasis on the atmosphere and the ocean. This includes further development of data-assimilation methodology for highly nonlinear high-dimensional geophysical systems and of causality theory for these systems. He also has a research group at the University of Reading, working on these two projects: (a) Data assimilation in highly nonlinear systems, (b) CUNDA: Causality Relations using nonlinear Data Assimilation. He is a Professor in data assimilation and physical oceanography, Colorado State University (USA), and Professor in data assimilation, University of Reading (USA). He was Director of the Data Assimilation Research Centre (DARC) from 2009-2019, Director Data Assimilation Research NCEO from 2014-2018, Director National Centre for Earth Observation (NCEO) from 2013-Oct/2014, associate Professor Utrecht University from 2006-2009, assistant Professor at Utrecht University from 1997-2006.

Webpage: https://www.atmos.colostate.edu/people/faculty/peter-jan-van-leeuwen/

Dr. Olmo Zavala-Romero
(Florida State University, USA)

Short-bio:

Dr. Olmo Zavala-Romero is a research scientist at the Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University (FSU). His research areas include applied machine learning in earth sciences, Lagrangian analysis of physical oceanographic phenomena, and geospatial data analysis and visualization tools. Ongoing research projects include deep learning for ocean data assimilation, Lagrangian modeling to estimate sources and destinations of marine debris in the Caribbean, and efficient visualization of Lagrangian datasets through the web.  He is working on applied deep learning methods to oceanography.

Webpage: https://www.coaps.fsu.edu/contact/our-people/scientists (https://olmozavala.com/)

Prof. Ludger Scherliess
(Utah State University, USA)

Short-bio:

Dr. Ludger Scherliess is a professor of Physics at Utah State University in Logan, Utah. Over the past decade, he has developed physics-based data assimilation models for the near-Earth space environment that are used operationally to specify and forecast the effects of space weather on the Earth’s upper atmosphere. His primary interest is on the specification and forecast of the effects of solar storms on the ionosphere/thermosphere system using data assimilation techniques. He is the chairperson of the International Space Weather Action Teams (ISWAT) – Global and Regional Ionospheric Total Electron Content (TEC) Group and the chairperson of the International Community Coordinated Modelling Center – Living with a Star (CCMC-LWS) Working Group on Regional and Global TEC.

Webpage: https://www.usu.edu/physics/directory/faculty/ludger-scherliess

Prof. Marie-Amélie Boucher
(Univesity of Sherbrooke, Canada; visiting scientist of the European Center for Medium Range Weather Forecasts, UK)

Short-bio:

Marie-Amélie Boucher is an associate professor at Université de Sherbrooke, in Québec, Canada. She is currently a long-term visitor at the European Center for Medium Range Weather Forecasts. Prof. Boucher is one of the co-chairs of HEPEX (the Hydrologic Ensemble Prediction Experiment), a global community that brings researchers and practitioners together to advance topics related to hydrological ensemble prediction. Her main research interests include data assimilation, pre- and post-processing, forecast communication and the socio-economic value of hydrological ensemble forecasts.

Webpage: https://www.usherbrooke.ca/recherche/specialistes/details/marie-amelie.boucher

Dr. Vinicius A. Almeida
(Universidade Federal do Rio de Janeiro, Brasil)

Short-bio:

Researcher at the Laboratory of Applied Meteorology, Federal University of Rio de Janeiro (RJ), Brazil, since 2018. More than 12 years of experience in the private sector working with B2B technology (geoscience, software engineering, advanced data analytics, and cloud computing) in partnership primarily with Google. My research areas focus on the development of machine learning models for many applications and the use of the Regional Atmospheric Model WRF for studying many atmospheric phenomena. Research fields: high-impacting phenomena for aviation, air pollution dispersion, power sector planning (hydroeletric power plants, eolic energy), regional weather forecasts and other advanced data analytics problems in many different sectors.

Webpage: https://lma.ufrj.br/equipe

Dr. Rossella Arcucci
(Imperial College London, UK)

Short-bio:

Lecturer in Data Science and Machine Learning at the Department of Earth Science and Engineering. Rossella has been with the Data Science Institute at Imperial College since 2017, where she has created the Data Assimilation and Machine Learning (DataLearning) Working Group. The group is now a focal point for researchers and students of several departments at Imperial and other Universities in UK and Europe. She collaborates with the Leonardo Centre at Imperial College Business School, contributing to the development of integrative, just and sustainable models of economic and social development. The models Rossella has developed have produced impact in many applications such as finance, social science, engineering, geoscience, climate changes and others. She has developed accurate and efficient models with data analysis, fusion and data assimilation for incomplete, noisy or Big Data problems. She works on numerical and parallel techniques. She finished her PhD in Computational and Computer Science in February 2012. She received the acknowledgement of Marie Sklodowska-Curie fellow from European Commission Research Executive Agency in Brussels in February 2017.

Webpage: https://www.imperial.ac.uk/people/r.arcucci

Prof. Fangxin Fang
(Imperial College London, UK)

Short-bio:

Dr Fangxin Fang is a senior research fellow at Imperial College, and executive manager of data assimilation laboratory at the Data Science Institute, Imperial. She leads the research on advanced computational tools and data science technologies that can help us to manage a safe, comfortable and healthy environment. She has over 25-year experience in data assimilation and mathematical modelling technologies. Her main original contributions center on cutting edge techniques of predictive modelling (machine learning, and data assimilation techniques, reduced order modelling -ROM, adaptive observation). Fang and her group first applied deep learning techniques and ROM  to real-time spatio-temporal prediction of nonlinear fluid flows. The applications are mainly focus atmospheric, pollution, ocean, multiphase flows and environmental problems.

Webpage: https://www.imperial.ac.uk/people/f.fang