Model-Data Integration: How to effectively analyze large data? Kazuhito Ichii Japan Agency for Marine-Earth Science and Technology (JAMSTEC) (by 31 March) Center of Environmental Remote Sensing (CEReS), Chiba University (after 1 April)

Personal Background PhD, Earth Science, Nagoya University, Japan Simplified Earth System Modeling and Terrestrial Remote Sensing) Post-doc, NASA Ames Research Center, CA, USA Monitoring and Modeling of Terrestrial Carbon Cycle (Model-Data Integration, ML-based regression) Associate Prof., Fukushima University, Japan (2007-2014) Senior Scientist, JAMSTEC (2014-Mar 2017) Professor, Chiba University, Japan (Apr 2017-) Remote Sensing Big Data Analysis, Machine-Learning Model comparison, Model-Data Integration

Research Areas/Interests Model-Data Integration Toward Better Understandings of Terrestrial Environment Satellite data: Global 1-8 km data Long-term (e.g. 82-, 2000-) (e.g. Monitoring Veg in China) Better Estimation, Projection Change Detection (Hotspot)

Links to „Computer Science meets Ecology“ I have been using Machine-Learning (Support Vector Regression) to predict CO2 fluxes across Asia etc. I have huge data volume of satellite-data. I really would like to extract some big changes of terrestrial status. However, not many ideas. I have huge data volume of outputs from multiple models. I need to find an efficient ways to summarize results in a simple way. I would like to work model-data integration (data-model fusion) using satellite-based data as constraints. It takes too much computation time if we do optimization of parameter.