MICHAEL J FRIEDEL PHD

 

 

ABOUT ME

 

I am a Senior Computational Scientist at Pacific Northwest National Laboratory , USA. I develop and apply innovative algorithms and workflows that discover, quantify, and predict linkages and their response to climate, hydrologic and biogeochemical cycles and geophysical systems across spatiotemporal scales for solving Energy, Environmental and Security challenges. My research uses artificial-adaptive system (data mining, genetic programming, learn-heuristics, deep learning, machine-learning, multimodal transfer learning, and physics-informed learning), numerical (traditional and joint multiphysics inversion) and uncertainty quantification (Bayesian, Monte Carlo) methods. I design, collect, and integrate big data including direct (physical, chemical, biological) and indirect (geophysical and remote sensing) measurements across multiscale environmental networks (space, airborne, surface, borehole) to improve solution predictability.

Prior to these roles, I was the Environmental Data Analytics Science Leader at Lincoln Agritech – Lincoln University and Senior Research Scientist (Hydro-Geophysics) at GNS Science, NZ; and Senior Research Scientist (Hydrologist and Geophysicist) and Supervisory Hydrologist at US Geological Survey, USA. During this period, I successfully developed and applied numerical and machine learning workflows to test hypotheses and answer questions in Earth and Environmental System themes: Climate and land-use change, Ecosystem, Energy and minerals, Natural hazards, Solid-earth, and Water Science. This work resulted in 120 publications, 150 conference presentations, and $33M+ research grants. During this period, I managed multimillion-dollar national/international projects with academic appointments as Adjoint Associate Professor in the School of Geography, Environment, and Earth Sciences at Victoria University, NZ; and Mathematical and Statistical Sciences at the University of Colorado, USA. I also served as Instructor in the Department of Geography and Environmental Sciences at the University of Colorado, USA; and Visiting Professor developing and teaching courses and mentoring students for the Geology Department at Colorado College, USA; Department of Environmental Science, University of Kuopio, FN; Geosciences Institutes at the Universities of Brasilia and Campinas, BR; and held Senior Research positions (Hydrologist, Hydrogeologist, Geophysicist, Hydrogeophysicist) with the Institute of Geological and Nuclear Science, NZ and the US Geological Survey, USA. I have ongoing collaborations with scientists in the United States, Australia, New Zealand, China, Brazil and Denmark.

 

DISCIPLINE &

EXPERTISE

 

 

  • Data Science
  • Finite Element Analysis
  • Earth System Models
  • Ecohydrology
  • Genetic Programming
  • Geohydrology
  • Geophysics
  • Geostatistics
  • Hydrologic Modeling
  • Hydrology
  • Inversion
  • Machine Learning
  • Numerical Simulation
  • Optimization Algorithms
  • Physics-informed Learning Machines
  • Statistical Data Analysis

SELECTED

PUBLICATIONS

 

 

 

 

 

  • Comparison of four learning-based methods for predicting groundwater redox status
  • A self-organizing map approach to characterize hydrogeologic properties of the Serra-Geral transboundary fractured aquifer
  • Mapping fractional soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow
  • Mapping fractional soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow
  • Mapping fractional soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

Click to view more publications

ReserachGate

 

 

 

 

Google Scholar

 

 

ORCID

PROJECT EXAMPLES

 

 

 

 

 

  • Comparison of four learning-based methods for predicting groundwater redox status
  • A self-organizing map approach to characterize hydrogeologic properties of the Serra-Geral transboundary fractured aquifer
  • Mapping fractional soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow
  • Mapping fractional soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow
  • Mapping fractional soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

Click to view more publications

ReserachGate

 

 

 

 

Google Scholar

 

 

ORCID

 

NON-ACCADEMIC APPOINTMENTS

 

 

  • Senior Computational Scientist, Computational Geophysics, Pacific Northwest National Laboratory, United States
  • Associate Researcher, Earth and Environmental Systems, Semien Institute, Italy
  • Data Analytics Science Leader, Environmental Research, Lincoln Agritech Ltd, New Zealand
  • Senior Hydrogeophysicist, Hydrogeology, Institute of Nuclear and Geological Science, New Zealand
  • Senior Research Geophysicist, Crustal Geophysics & Geochemistry Science Center & Central Mineral & Environmental Resource, US Geological Survy
  • Senior Research Hydrologist, Colorado Water Science Center,US Geological Survy
  • Supervisory Res Hydrologist, Illinois Water Science Center,US Geological Survy
  • Research Geophysicist, Geotechnology, US Bureau of Mines

 

EDUCATION

 

  • Senior Computational Scientist, Computational Geophysics, Pacific Northwest National Laboratory, United States
  • Associate Researcher, Earth and Environmental Systems, Semien Institute, Italy
  • Data Analytics Science Leader, Environmental Research, Lincoln Agritech Ltd, New Zealand
  • Senior Hydrogeophysicist, Hydrogeology, Institute of Nuclear and Geological Science, New Zealand
  • Senior Research Geophysicist, Crustal Geophysics & Geochemistry Science Center & Central Mineral & Environmental Resource, US Geological Survy
  • Senior Research Hydrologist, Colorado Water Science Center,US Geological Survy
  • Supervisory Res Hydrologist, Illinois Water Science Center,US Geological Survy
  • Research Geophysicist, Geotechnology, US Bureau of Mines

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