1. 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)

2. 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 ( )
Senior Scientist, JAMSTEC (2014-Mar 2017)
Professor, Chiba University, Japan (Apr 2017-)
Remote Sensing Big Data Analysis, Machine-Learning
Model comparison, Model-Data Integration

3. 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)

4. 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.