Max Planck Gesellschaft
Feng Tao


PhD student with the
Soil Biogeochemistry Group

room: C3.002b (tower)
phone: +49 3641 57-6205
fax: +49 3641 57-6205
email: ftao(at)bgc-jena.mpg.de

Research interests | Current Research | Past Research | Publications


Research Interest:

  • Terrestrial carbon cycle, mechanisms and mathematical representation
  • Uncertainties of land carbon model simulations
  • Radiocarbon
  • Data assimilation
  • Deep learning

Current Research:

I am currently working on:

  • understanding dynamics of global soil carbon cycle and its underlying mechanisms using ecological modelling, data assimilation and machine learning techniques

Past Research:

Sept. 2018 - Present, Tsinghua University, Department of Earth System Science, Beijing, China
Ph.D. student in Global Change Ecology

Aug. 2014 - Jun. 2018: Sun Yat-sen University, School of Environmental Science and Engineering, Guangzhou, China
B.Sc. in Environmental Science


Publication

Peer-reviewed

Tao, F., Zhou, Z., Huang, Y., Li, Q., Lu, X., Ma, S., Huang, X., Liang, Y., Hugelius, G., Jiang, L., Doughty, R., Ren, Z. & Luo, Y. 2019. Deep learning optimizes data-driven representation of soil organic carbon in Earth system model over the conterminous United States, Frontiers in Big Data.

Conference Contribution

Tao, F. and Luo, Y., PROcess-guided deep learning and DAta-driven modelling (PRODA) uncovers key mechanisms underlying global soil carbon storage, 2021 3rd ISMC Conference, 18 - 22 May 2021, Virtual, Invited Talk, Highlights Talk

Tao, F. and Luo, Y., Big data-driven modelling in CLM5 reveals microbial carbon use efficiency as the key mechanism underlying global soil organic carbon storage, 2021 NCAR CESM Land Model and Biogeochemistry Working Group Meeting, 23 - 25 February 2021, Virtual, Talk

Tao, F., Huang, X., Mishra, U., Hugelius, G. & Luo, Y., Big data-driven modelling reveals key mechanisms underlying soil organic carbon stabilization. 2020 AGU Fall Meeting, 1 - 17 December 2020, Virtual, Talk

Tao, F., Huang & Luo, Y., Deep learning and constrained modelling from big data jointly reveal key mechanisms in soil organic carbon stabilization at Symposium SYMP7: Combining Deep Learning and Process-Based Modeling to Advance Ecological Forecasting. 2020 ESA Annual Meeting, 3 - 6 August 2020, Virtual, Invited Talk

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