I'm currently a Data Scientist on a product team at REWE Group
where we tackle food waste using ML.
Previously I've been a Data Scientist at UNICEF Germany,
helping my colleagues with everything data-related from visualizations to predictions
and pursuing my own data-driven ideas to support UNICEF's mission on helping children all around the world.
I've also been part of the Data Taskforce and the Green Team.
Climate change, conservation, gobal health, food security and education are some of the topics I care about.
Using data and machine learning in sustainable and responsible ways to bring forward these topics is what I'm excited about.
I did my MSc in Business Administration, Media and Technology Managment at the University of Cologne in Germany,
focussing on Data Science, Technology Management and Digital Transformation.
I was a member of Enactus and spent a term abroad at the Indian Institute of Management Ahmedabad in India.
I worked at the digital consultancy FELD M in Munich
as an intern and working student (Digital Analytics)
and at the tech start-up Metalshub in Düsseldorf
as a working student (Entrepreneurship and Marketing).
I did my BSc in Economics and Business Administration at the University of Tübingen in Germany.
During my bachelor's I was a member of our faculty's student council and Market Team.
I spent a term abroad at Lomonosov Moscow State University in Russia and another one at Oregon State University in the U. S.
I worked at the tech start-up Kreatize in Berlin
as an intern and working student (Business Development and Product Management).
Besides, I enjoy learning about astronomy, philosophy and vegan/regional/seasonal cooking.
I'm a member of the public observatory in Cologne.
On occasion I like to participate in hackathons with a positive societal impact.
Soft skills: Teamwork, communication & presentation, data ethics
Projects
Camera traps
Classifying tigers (and other species) in camera trap images using ML, open data, open source tools and free compute resources
This project focuses on building a ML model to classify tigers (and other species) in camera trap images,
using open-source tools (MEWC), open data (LILA BC), and free compute resources (Colab and Kaggle).
The goal is to provide an automated solution for ecologists and NGOs working on tiger conservation
to reduce the manual labor involved in analyzing large volumes of camera trap images.
The trained species classifier is made available through EcoAssist.
Credentials: LILA BC, MegaDetector, own illustration.
Classifying land cover in KAZA using remote sensing and ML (with WWF Germany's Space+Science Team)
This project constitutes a set of scripts and notebooks for land cover classification in the Kavango-Zambezi
Transfrontier Conservation Area (KAZA), using Sentinel-2 and ML.
The pipeline trains the model in Colab and performs inference in Google Earth Engine, making the process flexible and efficient.
While focused on KAZA, it can be adapted for other regions with suitable labeled data for training.
Mapping cropland in KAZA using remote sensing and ML (with WWF Germany's Space+Science Team)
This project aims to monitor agricultural land use in the Kavango-Zambezi Transfrontier Conservation Area (KAZA).
The focus is on detecting farm plot locations and tracking their changes over time using Sentinel-2 and ML.
The project offers tools for data processing, model training and visualizations, enabling the detailed mapping of croplands.
Detecting stationarity in animal movement data using R Shiny (EMAC23 Coding Challenge for MoveApps by Max Planck Institute of Animal Bevhaior)
The Stationarity Dashboard is an App on MoveApps,
designed to track animal behavior over time and identify stationary individuals or anomalies in movement patterns.
It provides users with a range of features (summary table, tracker map and distance plot)
and options (individual and date range filters and inputs for stationarity detection) to conveniently analyze their movement data.
Linking animal movement data and surface water availability using R Shiny (with Okavango Research Institute)
This is another App on MoveApps, linking animal movement and surface water
that has been mapped in Google Earth Engine using Sentinel-1 beforehand.
It allows users to track changes in animal movement and surface water over time, trying to establish a connection.
This is a work in progress, and working with Sentinel-1 data comes with some caveats for certain regions.
Guest contribution on "Data for Good Causes - An Overview" in Greiner, R., Berger, D., & Böck, M. (2022). Analytics und Artificial Intelligence. Springer Books. https://doi.org/10.1007/978-3-658-38159-2
October 2022: Environ-Mate is featured in the data.europa.eu's Use Case Observatory research project
September to November 2021: Machine Learning Engineer at Omdena’s Dryad Challenge on “Building AI Model to Detect Forest Wood Fire through Sensor Data Analysis”
November 2020: Winning team in remote HIDA Datathon for grand challenges on climate change
March 2020: Webinar on Environ-Mate at EU Lunchtime Conference
