Bio


Francky is a research statistician and data scientist with extensive experience in developing and implementing advanced geostatistical, statistical, machine learning, and uncertainty quantification methods for geoscience applications (mining, petroleum, environment,...). He also has some teaching experience, having taught probability, statistics, and geostatistics at undergraduate, graduate, and professional levels. He has published over 13 peer-reviewed scientific papers in geostatistics, statistics, and machine learning. He serves as a reviewer for many international peer-reviewed journals and conferences in geostatistics, statistics, and machine learning. Francky held a Ph.D. in Geostatistics from MINES ParisTech (France), the birthplace of geostatistics. Prior to joining Stanford University, he was Post-Doctoral Research Scientist in Geostatistics & Data Science at CSIRO Mineral Resources (Australia) where he developed advanced geostatistical, statistical, and machine learning techniques for use in geoscience applications spanning the minerals value chain. Francky's current research at Stanford University focuses on the development and implementation of advanced geostatistical, statistical, machine learning, and uncertainty quantification techniques as part of a large BHP Billiton mining project on implicit dynamic uncertainty quantification for automation of data fusion and prediction in mineral resources evaluation and planning.

Honors & Awards


  • Recipient - Elsevier Mathematical Sciences Sponsorship Fund, Elsevier (2017)
  • Visiting International Professorship in Geostatistics, University of Liège (2017)
  • First Prize - Top Ph.D. Student Poster, GeoENV, 10th Conference on Geostatistics for Environmental Applications (2014)
  • Second Prize - Top Ph.D. Student Talk, GeoENV, 10th Conference on Geostatistics for Environmental Applications (2014)

Boards, Advisory Committees, Professional Organizations


  • Scientific Committee Member, 14th International Conference on Machine Learning and Data Mining in Pattern Recognition (2018 - 2018)
  • Scientific Committee Member, Annual Conference of International Association of Mathematical Geosciences (2017 - 2018)
  • Member, International Association of Mathematical Geosciences (2016 - Present)
  • Member, Spatial Statistics Society (2015 - Present)

Professional Education


  • Doctor of Philosophy, MINES Paris Tech, Geostatistics (2014)
  • Master of Science by Research, Paris Dauphine University & ENSAE Paris Tech, Statistical Information Processing (2011)
  • Master of Science, Toulouse 1 University Capitole, Statistics & Econometrics (2010)
  • Bachelor of Engineering, Institute of Statistics and Applied Economics, Applied Statistics & Economics (2008)

Current Research and Scholarly Interests


Implicit dynamic uncertainty quantification for automation of data fusion & prediction in mineral resources evaluation and planning.

https://scerf.stanford.edu/research

All Publications


  • Exploring prediction uncertainty of spatial data in geostatistical and machine learning approaches ENVIRONMENTAL EARTH SCIENCES Fouedjio, F., Klump, J. 2019; 78 (1)
  • Geostatistical Clustering as an Aid for Ore Body Domaining: Case Study at the Rocklea Dome Channel Iron Ore Deposit, Western Australia Applied Earth Science, Transactions of the Institutions of Mining and Metallurgy: Section B Fouedjio, F., Hill, J., Laukamp, C. 2017; 127 (1): 15-29
  • A Spectral Clustering Method for Large-Scale Geostatistical Datasets Machine Learning and Data Mining in Pattern Recognition. MLDM 2017 Fouedjio, F. Springer. 2017: 248–261
  • A Fully Non-stationary Linear Coregionalization Model for Multivariate Random Fields Stochastic Environmental Research and Risk Assessment Fouedjio, F. 2017; 32 (6): 1699–1721
  • A Spectral Clustering Approach for Multivariate Geostatistical Data International Journal of Data Science and Analytics Fouedjio, F. 2017; 4 (4): 301-312
  • A Clustering Approach for Discovering Intrinsic Clusters in Multivariate Geostatistical Data Machine Learning and Data Mining in Pattern Recognition. MLDM 2016 Fouedjio, F. Springer. 2016: 491–500
  • Discovering Spatially Contiguous Clusters in Multivariate Geostatistical Data Through Spectral Clustering Advanced Data Mining and Applications. ADMA 2016 Fouedjio, F. Springer. 2016
  • A Hierarchical Clustering Method for Multivariate Geostatistical Data Spatial Statistics Fouedjio, F. 2016; 18: 333-351
  • Second-order Non-stationary Modeling Approaches for Univariate Geostatistical Data Stochastic Environmental Research and Risk Assessment Fouedjio, F. 2016; 31 (8): 1887–1906
  • A Generalized Convolution Model and Estimation for Non-Stationary Random Functions Spatial Statistics Fouedjio, F., Desassis, N., Rivoirard, J. 2016; 16: 35-52
  • Predictive Geological Mapping Using Closed-Form Non-stationary Covariance Functions with Locally Varying Anisotropy: Case Study at El Teniente Mine (Chile) Natural Resources Research Fouedjio, F., Seguret, S. 2016; 25 (4): 431-443
  • Space Deformation Non-stationary Geostatistical Approach for Prediction of Geological Objects: Case Study at El Teniente Mine (Chile) Natural Resources Research Fouedjio, F. 2015; 25 (3): 283-296
  • Estimation of Space Deformation Model for Non-stationary Random Functions Spatial Statistics Fouedjio, F., Desassis, N., Romary, T. 2015; 13: 45-61