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Dr. Karin Mora

Research Fellow

Umweltdatenwissenschaften und Fernerkundung
Institutsgebäude
Talstraße 35, Room 2-09
04103 Leipzig

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Abstract

I seek to uncover, explain, and predict spatiotemporal dynamics, particularly in ecological systems. I employ a range of modelling approaches, spanning from differential equations to machine learning, to gain profound insights into these complex systems. My ultimate goal is to not only characterise and quantify but also explain the emergence of intricate behaviours in such systems.

Currently, my main project focuses on combining data science and dynamical system theory to reveal complex interactions between biodiversity and climate change. It is funded by the proposed excellence cluster Breathing Nature (https://www.uni-leipzig.de/en/research/excellence-in-research/breathing-nature).


Recent projects:

* Co-PI DeepFeatures, funded by ESA AI4Science, 2024-2026

* Co-PI DeepExtremes, funded by ESA AI4Science, 2022-2024


https://rsc4earth.de/authors/kmora/


Professional career

  • since 12/2020
    Postdoctoral Researcher, Remote Sensing Centre for Earth System Research (RSC4Earth) and German Centre for Integrative Biodiversity Research (iDiv Leipzig).
  • 05/2015 - 03/2019
    Postdoctoral Researcher, Paderborn University
  • 05/2014 - 04/2015
    Postdoctoral Research Fellow, Technion - Israel Institute of Technology (Israel)

Education

  • 01/2010 - 04/2014
    Doctor of Philosophy (Applied Mathematics), University of Bath (UK)
  • 10/2008 - 12/2009
    Graduate School, University of Bath (UK)
  • 09/2004 - 07/2008
    Master of Mathematics, University of Reading (UK)

  • nonlinear dynamics
  • complexity
  • time series analysis
  • data science, machine learning
  • mathematical ecology, biodiversity, phenology
  • land-atmosphere interactions


  • Extracting spatiotemporal macroecological patterns using plant occurrence data crowd-sourced via Flora Incognita (Flexpool)
    Mahecha, Miguel
    Duration: 10/2020 – 09/2022
    Funded by: DFG Deutsche Forschungsgemeinschaft
    Involved organisational units of Leipzig University: Umweltdatenwissenschaften und Fernerkundung
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more projects

  • Mahecha, M.; Bastos, A.; Bohn, F. J. et al.
    Biodiversity and climate extremes: known interactions and research gaps
    ESS Open Archive. 2023.
    DOI: 10.22541/essoar.169462031.19744802/v1
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  • Gaidai, R.; Goelz, C.; Mora, K.; Rudisch, J.; Reuter, E.-M.; Godde, B.; Reinsberger, C.; Voelcker-Rehage, C.; Vieluf, S.
    Classification characteristics of fine motor experts based on electroencephalographic and force tracking data
    Brain research. 2022.
    DOI: 10.1016/j.brainres.2022.148001
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  • Wolf, S.; Mahecha, M.; Sabatini, F. M.; Wirth, C.; Bruelheide, H.; Kattge, J.; Moreno-Martínez, Á.; Mora, K.; Kattenborn, T. J.
    Citizen science plant observations encode global trait patterns
    Nature Ecology & Evolution. 2022.
    DOI: 10.1038/s41559-022-01904-x
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  • Martinuzzi, F.; Rackauckas, C.; Abdelrehim, A.; Mahecha, M.; Mora, K.
    ReservoirComputing. jl: An Efficient and Modular Library for Reservoir Computing Models
    Journal of Machine Learning Research. 2022.
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  • Goelz, C.; Mora, K.; Rudisch, J.; Gaidai, R.; Reuter, E.-M.; Godde, B.; Reinsberger, C.; Voelcker-Rehage, C.; Vieluf, S.
    Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns
    Neural networks. 2021. pp. 363–374.
    DOI: 10.1016/j.neunet.2021.04.029
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more publications

  • 12-GEO-M-AG01 Introduction to Data Science

    • Module in the Master of Science Earth System: Data Science and Remote Sensing
    • Mathematical foundation in data science