About me

Hello everyone! I'm Benedikt — an open-minded researcher passionate about machine learning, innovation, and sustainability. I work in the AI for Climate Impact Team at the IBM Research Zurich lab. We build foundation models for earth observation and work on climate-related applications using satellite data. Eager to share ideas and experiences in the intersection of machine learning and sustainability.

Looking forward to connecting with you!


Selected projects

Remote Sensing Image Retrieval

Embeddings of Geospatial Foundation Models (GeoFMs) enable simple but accurate content-based image retrieval of remote sensing images. We tested the GeoFM Prithvi (IBM-NASA) on two challenging datasets with multi-spectral satellite images and outperformed common computer vision models by a large margin. Check out our repository for more details.

MESS Benchmark

Holistic benchmarks allow a robust quantitative comparison of model architectures. However, such evaluation practice covering a wide range of real-world applications is lacking for multi-modal (i.e., text-to-image) zero-shot semantic segmentation tasks. Thus, our motivation to compose the Multi-domain Evaluation of Semantic Segmentation (MESS) benchmark, which consists of 22 datasets for varying application domains. More details can be found on our website.

CLAIMED Component Compiler (C3)

The CLAIMED framework enables rapid development and deployment of processing steps, e.g. for data preprocessing in deep learning. Using the CLAIMED Component Compiler (C3), components can be easily built from a Python script or notebook and deployed on KubeFlow, Kubernetes, or locally via CLI. Notably, C3 eliminates the need for Docker or Kubernetes expertise, making it accessible to any data scientist to deploy code on clusters. Check out the Getting Started page for more information.

Résumé

Work Experience

Research Scientist, AI for Climate Impact, IBM Research

02/2024 - present

  • Research on pre-training of multi-satellite and multi-modal geospatial foundation models
  • Developing the data pipeline for a geospatial ML platform and foundation model pre-training

Intern, AI for Climate Impact, IBM Research

08/2023 - 02/2024

  • Deploying and applying MLOps tools and pipelines
  • Developing the CLAIMED Component Compiler (C3) for the CLAIMED project
  • Experiments with remote sensing image retrieval using geospatial foundation models

Junior Researcher, Applied AI in Services Lab, KIT

05/2020 - 04/2022

  • Research about human-AI collaboration and machine learning, with a focus on few-shot learning
  • Developed AI applications for a AI project in the AEC industry
  • Worked with multiple computer vision models and deployed them on high performance clusters

Design Thinking project, KIT & HUK Coburg

10/2019 - 06/2020

  • Used design thinking to develop user-centered innovations and prototypes for retirement saving

Intern, R&D Strategy, AUDI AG

07/2017 - 02/2018

  • Supported the project manager in defining a new technology-focused R&D strategy
  • Worked in the project management office of an R&D transformation project

Education

M.Sc. Information Systems, Karlsruhe Institute of Technology

10/2019 - 04/2023

  • Focus on machine learning and service innovation

Exchange, Norwegian University of Science and Technology

01/2021 - 06/2021

  • Courses in computer vision, natural language processing and statistics

B.Sc. Industrial Engineering, Karlsruhe Institute of Technology

10/2015 - 09/2019

  • Focus on digital services and business strategy

Publications