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Mdmdma/README.md

Hi, I'm Mathis

MSc Interdisciplinary Science student at ETH Zürich, working at the intersection of remote sensing, machine learning, and physical sciences. I'm broadly curious about science and technology — from radar signal processing and atmospheric physics to model compression and edge computing. IIf it’s about uncovering how the world works or creating something that hasn’t existed before, you’ll have my full attention.

What I'm currently working on

  • Model distillation for remote sensing — compressing foundation models to run on edge devices (PyTorch, Lightning)
  • ancestree.ch — open-source fullstack platform for collaborative family history and photobook generation

Research background

I've worked across a fairly wide range of problems: RCS reduction simulations, pansharpening with graph-regularized super-resolution, multi-object spectroscopy feasibility on small telescopes, and MCMC-based pseudo-absence sampling for habitat suitability modelling (in collaboration with Uni Bologna and the Edmund Mach Foundation).

Stack

Python PyTorch R MATLAB much more

Pinned Loading

  1. AmateurMOS AmateurMOS Public

    Repository containing code to analyse the feasibility and performance of the application of dmd in low cost amateur astronomy.

    Python

  2. USE.MCMC USE.MCMC Public

    A R package in development to perform MCMC sampling for pseudo absences

    R

  3. graph-super-resolution-pan graph-super-resolution-pan Public

    Forked from prs-eth/graph-super-resolution

    Applying graph regularisation for guided super-resolution to pansharpening in the remote sensing setting

    Jupyter Notebook

  4. ancestree ancestree Public

    Website to collaboratively create a familiy tree

    JavaScript 1