Computer Science

  • Machine Learning
  • High Performance Computing

Remote sensing

  • GIS
  • Google Earth Engine

Geostatistics

  • Multiple Point Statistics
  • Personal Projects
  • Post-Doc
  • PhD
  • Me

As a Researcher (formerly Junior Group Leader) at the Institute for Interdisciplinary Mountain Research within the Austrian Academy of Sciences in Innsbruck, Austria, I bring a wealth of expertise to multiple fields, including Geostatistics, remote sensing, and Machine Learning. Prior to my current role, I was an Assistant Professor in Geo-Environmental Data Science at Utrecht University and a Postdoctoral Fellow at Stanford University. My PhD from the University of Lausanne was focused on Geostatistics, which remains a primary area of my research.
My work involves the use of Multiple Point Statistics (MPS) to simulate complex structures, while also integrating remote sensing techniques such as Google Earth Engine (for which I am a recognized Google Developer Expert) and High-Performance Computing (HPC) to advance geoscience research. I have a broad range of scientific interests and am dedicated to utilizing cutting-edge technologies in my research.

Currently, my research is focused on developing a novel passive sensing technology based on photogrammetry that can generate a digital elevation model (DEM) from a single flyover. Additionally, I am actively engaged in collaborative projects covering various subjects, including remote sensing, geostatistics, and bird tracking.

Picture Mathieu Garvey
Picture Mathieu Garvey

I always keep an older picture, to fit with other researchers!

Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.

H.G.Wells

Machine Learning

My research using Machine Learning is mainly focus around geoscience and how to use such ML framework in unconventional context.

Geostatistics

My research in geostatistics mainly focuses on statistical simulations. More precisely, to develop new algorithms in Multiple Point Statistics (MPS) and Machine Leraning to generate complex structure.

Remote sensing

Remote sensing had a huge impact on my career and remains important to me. Currently, most of my research in remote sensing is done through collaborations on different projects from Vegetation evolution (Vietnam and Valais), growing season, frozen lakes and even NPP (net primary productivity) evolution in ocean. Currently, most of hese studies are done on online platforms such as Google Earth Engine, which enable studies through a gigantic amount of data.

Research philosophy

I put particular importance on investigation cutting-edge solutions, from both a technological as well as algorithmic point of view. Most applications of these solutions are done through collaborations.

Furthermore, I put particular effort and time into allowing the results of various studies to be available to the community though functional, open and easy-to-use software or library.

The true quote is fact:

The time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex world wide states that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and to write.

H.G.Wells

Projects


QuickSampling: an efficent and robust MPS approach
Finished Multiple-point statistics Maintained

The main output of this project is QuickSampling (QS),a training image-based simulation tool that is a free, open and can be furthure developpe. It uses a Direct Sampling approach that was enhanced using FFT for speed and ranking (as opposed to the threshold) that tends to be less sensitive. All this results in a more efficient and robust algorithm. The tool is available through G2S. The current implementation handles continuous, categorical, multivariate and gaped datasets.

Code Documentation Publication

Satellite image colorization
Finished Multiple-point statistics Remote-sensing

The main output of this project is an algorithm - Narrow Distribution Selection (NDS) - to spectrally enhance remotely sensed satellite images. This algorithm uses a similar approach" to QuikSampling to automatically and statistically colorize the image. It uses a pair of images with a high and low spectral resolution in addition to the image to enhance. The algorithm is free, open, modifiable and available through G2S toolset.

Code Documentation Publication

Auto QS: Training image based automatic calibration of direct-pixel MPS algorithm under low verbatim hypothesis
Ongoing Multiple-point statistics Maintained

The main goal of this approach is to propose an alternative to calibrate algorithms such as QucikSampling or Direct Sampling. Based on the analysis of the training image and possibility to reproduce patterns, that algorithm will provide an optimal and evolving calibration, that reduces as much as possible verbatim copy. This approach does NOT rely on complex objective function, and is therefore much more versatile.

Code Documentation Publication

Effect of resolution change on remote sensed analysis
Ongoing Remote-sensing

This study explores the effects and the errors introduced by the change of spatial resolution in remote sensing applications.

FastDS
Ongoing Collaboration Multiple-point statistics Side project

FastDS enhances Direct Sampling by taking advantage of the first few pixels to remove, for the list of potential candidates, all patterns that trivially would not match. Going from random sampling to predicted potential candidate is comparable to going from a naive and uniform rejection sampling to an optimal adaptive rejection sampling. This approach provides a significant boost compared to traditional DS, without introducing measurable bias.

Snow-Vegetation trend in the Alps
Finished Collaboration Remote-sensing Side project

Studing the evolution of snow and vegetation over the last 35+ years.

Publication

Cheetah
Ongoing Multiple-point statistics Side project

Cheetah evolved from Impala and SNESIM. The idea is to encode a k-class n-point patten in k n-bit numbers. The hope is that the method will work nicely, even with just a minor performance improvement. Mathematically, the 3 algorithms are equivalent and therefore provide similar simulations. Currently, investigating the use of FPGA can bring a true breakthrough in performance and power consumption.

MAZAlib
Ongoing Collaboration Side project

MAZAlib is a project to provide a tool - algorithm implementation and interface - to easily do segmentation of porous media scans.

Tracking of vegetation evolution (growth) in Vietnam
Ongoing Collaboration Remote-sensing Side project

The goal of this project is to track the type and the growth of the vegetation in Vietnam.

Website Publication

Glacier tracking
Finished Collaboration Remote-sensing Side project

The project produced a high-frequency and high-resolution image time series of the Gornergletscher (in the Swiss Alps) derived from repeated UAV surveys. I implemented the tracking algorithm.

Code Publication

Open Earth Engine Library
Finished Remote-sensing Side project Maintained
Open Earth Engine Library logo

The Open Earth Engine Library (aka. OEEL) is part of the open-geocomputing initiative. The goal is to provide Google Earth Engine (GEE) users with free and open algorithms.

Code Documentation

Tracking bird migration using pressure
Ongoing Collaboration

This project relies on pressure instead of light to determine the position of birds during migration.

Code R Code MATLAB API using GEE
GEE App Documentation Publication 1 Publication 1

Code and Software


G2S: The GeoStatistical Server
Multiple-point statistics Maintained

The GeoStatistical Server (G2S) is a framework that allows you to use state-of-the-art Multiple Point Statistics (MPS) algorithms to run stochastic simulations. G2S is designed to run simulations in a generic way, independently of the code used or language it is written in. For example, it enables to run a C/C++ simulation code using Python, or Python using MATLAB (or any other combination). It includes QucikSampling, Narrow Distribution Selection (NDS) and autoQS. Furthermore, it can easily be extended to handle any simulation grid-based simulation algorithms.

Code Documentation

Open Earth Engine Library
Remote-sensing Maintained
Open Earth Engine Library logo

The Open Earth Engine Library (aka. OEEL) is part of the open-geocomputing initiative. The goal is to provide Google Earth Engine (GEE) users with free and open algorithms.

Code Examples Documentation

Open Earth Engine extension
Remote-sensing Maintained
Open Earth Engine Library logo

The Open Earth Engine extension (aka. OEEex) is part of the open-geocomputing initiative. The goal is to provide Google Earth Engine (GEE) users with a dedicated chrome extension to enhance their experience.

Code Documentation

Integration effect on gender ratio

A quick and interactive study to evaluate the time needed to correct gender bias as a function of the duration of a career. The code here includes an example with an academic career, but the equations are general and can be directly used in any field."

Code Online

Useful micro toolset for statistical metric
Multiple-point statistics

Few functions to compute nD variograms and 2D cumulants with matlab.

Code

MexInterrupt

An example of how to interrupt C/C++ code in a Matlab-Mex file.

Code

Random Kmin/Kmax

A header only library to find the k smallest/largest values (and their index) in an unsorted array. These functions are unbiased. In case of multiple positions with the same value, all are recorded if under k, otherwise the positions are sampled accordingly. These functions were designed for k <<< N (the size of the array). Furthermore, functions were implemented with intrinsic functions to get optimal performance (similar to searching for extremes). A Matlab wrapper is provided in this repository.

Code

2D Dynamic Warping

Implementation in C/C++ of 2DDW, check the original publication for more information.
I didn't develop the method, I only implemented a C/C++ version.

Code Publication

pyDev4G2S

A module to develop new MPS algorithms in Python. It takes advantage of G2S to allow remote connection and interfacing with other languages such as Matlab.

Code

G2S for QGIS
Multiple-point statistics Remote-sensing

A QGIS interface for G2S. It allows running remote sensing stochastic simulations directly in QGIS.

Code

PyQS-PyDS
Multiple-point statistics

This is a simple code in python to grasp how Direct Sampling and QucikSampling are working. It was intentionally reduced to its roots, the goal is to get the key component and not the performance. This code is a multiple order of magnitude slower than the C/C++ implementations.

Code

Matching Map Maker
Remote-sensing

Do pattern matching between big images (50k x 50k). Extremely useful to follow slow objects such as glaciers over time, that can be deformed.

Code

Fast Gaussian Simulation

Fast Gaussian Simulation (FGS) is a Matlab function which generates multiple n-D Gaussian Field very quickly. It uses the Fast Fourier Transform (FFT). It removes the edge effect on long-range, but introduces a micro bias for shorter ranges (only if the range outside of the simulation size is used).

Code

TIFF Training Images
Multiple-point statistics

A set of training images useful for MPS. Saved as TIFFF, it allows the user to load these images directly from Github in single or few lines. Therefore, it's perfect for use as demonstration code, where there is no need to send a separate training set.

Code

GeoPressureAPI
Remote-sensing

Server that returns the probability of position of a bird based on pressure record using Google Earth Engine to access massive ERA-5 dataset.

Code

The Berezina

Experiences

  • 2023
  • Junior Group Leader of the Digital Landscape group at the Austrian Academy of Sciences in Innsbruck
    • Remote Sensing
    • Geostatistics
    • Machine Learning for Geoscience
  • 2021
    to
    2025
    (INTR spring 2023)
  • Assistant Professor in Geo-Environmental Data Science at Utrecht University
  • Using Machine Learning framework to enhance Geoscience.
  • Feb 2021
    to
    Aug 2022
    (INTR fall 2021)
  • Postdoctoral researcher, Department of Geological Sciences, in Stanford
  • Bridging the gap between machine learning and geostatistical simulations. As part of Jef Caers’ team, Center for Earth Resources Forecasting (SCERF)
  • Mar 2020
    to
    Jul 2020
  • Postdoctoral researcher at the Institute of Geography and Sustainability (IGD), UNIL
  • The forest regrowth in Vietnam, a remote sensed analysis from 1984 to nowadays. As part of Christian Kull’s team.
  • Jun 2016
    to
    Aug 2016
  • Intern at CERN OpenLab
  • Contribute to GEANT V software project to realize large scale stochastic simulation of fundamental particles. Supervisor: Andrei Gheata and Maria Girone
  • Jun 2015
    to
    Dec 2015
  • Assistant researcher at Institute of Earth Surface Dynamics, UNIL
  • Development of a method to automatically complete gaps in point cloud datasets. Supervisor: Prof. Grégoire Mariéthoz
  • Mar 2014
    to
    Dec 2014
  • Intern | Research assistant at BFH, Biel
  • R&D in image processing at cpvrLab, Bern university of applied Sciences Development of an automatic camera to scan a manhole in 3D and also in charge of developing photogrammetric processes (e.g. image processing, point cloud generation, meshing and texturing). (Master project.) Supervisor: Prof. Hudritsch Marcus

Education

  • Jan 2016
    to
    Jan 2020
    Defense date 08.01.2020
  • PhD from Institute of Earth Surface Dynamics, University of Lausanne (UNIL)
  • Title: Multiple point geostatistical approaches to spectrally enhance satellite imagery
    Supervisor:Prof. Grégoire Mariéthoz
    Jun 2017: Multiple Point Statistic Simulations (3 days), University of Neuchatel, Switzerland.
    Oct 2016: Les méthodes de la géostatistique, (3 weeks) Ecole Des Mines de Paris, Fontainebleau, France.
    Mar 2016: An introduction to statistical reasoning and the practice of statistics in environmental sciences (4 days), University of Neuchatel, Switzerland.
  • Sep 2011
    to
    Jun 2014
  • Engineering degree from the École des Mines d’Alès
  • General engineering and computer science. (with an option in innovation)
  • Sep 2008
    to
    Jun 2011
  • Lycée Albert Schweitzer de Mulhouse, France
  • Preparatory class for entrance to Grandes Ecoles, MPSI/MP*: Advanced mathematics, physics and algorithmic.

Prizes and Awards

  • May 2022
  • Google®
  • Google Developer Expert in Earth Engine
  • Mar 2022
  • NVIDIA®
  • Academic Hardware Grant Program: A100
  • May 2021
  • Swiss National Science Foundation
  • Early Postdoc.Mobility
  • Aug 2019
  • International Association for Mathematical Geosciences
  • Student travel grant
  • Nov 2015
  • Intel®
  • Grand Prize Winner of the Intel® Modern Code Developer Challenge.

Skills

  • Programing

    Language Level
    Google Earth Engine (subset of JavaScript/Python) Offcial Developer Expert
    C / C++ / OpenMP Advance expert
    MATLAB Advance expert
    Python Advance expert
    TensorFlow Advance expert
    CUDA / OpenGL / CL Expert
    Shell / Bash Expert
    Emscripten Expert
    Javascript / JQuery / HTML / CSS Expert
    Markdown Expert
    Latex Intermediate
    R Intermediate
    Maple Intermediate
    Java Intermediate
    SQL Intermediate
    OWL/SPARQL Intermediate

  • Interfacing languages

    MATLAB <==> Python
    Python <==> C/C++
    MATLAB <==> C/C++

  • Softwares

    Name
    Sublime Text
    Git
    Illustrator
    Office
    Cinema4D
    QGIS
    ...

  • Languages

    Language Level
    French +++
    English ++
    German +

GET IN TOUCH

I would love to hear from you!

So don't hesitate to drop me a line at

Advices

General Advice

  • Be curious.
  • Be critical.
  • Do what you can do, but in particular what you are the only one that can do.
  • Never do something that a machine can do for you.
  • Be aware of what you know!
  • Even more important, be aware of what you don't know!
  • Don't be afraid to learn new things

Don't Fear Changing Fields: Success in a specialized field often requires both exceptional skill and luck due to high competition. Switching fields can not only broaden your knowledge and challanges, but also provide you with a unique perspective that sets you apart.

BSc/MSc

Make your own subject! If you need to work on a project, try to adjust it to fit your interests. Usually, you can convince professors to deviate from the original topic. Why? For you, it's more interesting to work on something you're passionate about. On the other hand, if a professor provides a subject, it's probably their expertise field (they'll notice any shortcuts you take). Conversely, if you choose a subject, you become the expert, and the professor might even learn something new.

PhD

  • Maintain 1-2 side projects. They offer a fallback and can keep you engaged when your main research stalls. Be careful that side projects don't overshadow your PhD.
  • Overcome imposter syndrome. If you're experiencing it, you likely shouldn't be. (I won't comment on the opposite scenario.)
  • Own your PhD. Funding may come from elsewhere, but the work and decisions are yours.
  • Test Over Debate: You can spend hours debating whether something will work, but sometimes it's faster and more conclusive to just test it.

Post-PhD

  • Apply! Don't wait until you find the perfect job to apply. Treat previous applications as training.
  • If you don't like your job, then change of job!


Projects for Students

I am always eager to mentor motivated students.
However, there are a few things to note:

  • I no longer provide research subjects. I expect students to come up with their own research topics, as mentioned in the advice section above.
  • Internship applications are welcome. If you're interested, please provide a CV and a cover letter. The cover letter should include a detailed project description and can be more than one page if necessary. Both documents must be in English.
  • Usually, I don't have funds available for internships, but you're always welcome to inquire. As the saying goes, "those who don't try, don't get." If you're securing your own funding, such as through ERASMUS, be aware that Innsbruck is relatively expensive. A bare minimum of 1000€ per month is required for living expenses.