Hey, I’m Elias Baumann, currently a data and validation machine learning engineer at Kaiko.ai. I develop and optimize machine and deep learning models for large scale image analysis in digital pathology. I am also interested in the development of new methods for the analysis of multi-omics data and the integration of these data types for the development of new biomarkers.

In my professional career, I try to develop innovative solutions for problems, where the fundamental understanding of the respective domain is crucial, and therefore also love to learn everything about the problem domain.

I am currently based in Bern, Switzerland but am working in the Zürich office of Kaiko.

Experience

Kaiko.ai

Jul 2025 - Present

Engineer, Data and Validation

University of Bern

Apr 2021 - May 2025

Phd Student, Computational Pathology

RadboudUMC Nijmegen

Mar 2023 - Aug 2023

Visiting researcher, Computational Pathology

Humboldt University of Berlin

2017 - 2020

Research Assistant, Chair of Information Systems

B.telligent

Nov 2015 - May 2016

Intern, Visual data discovery

Telefónica Germany & Co. OHG

Aug 2014 - Oct 2015

Intern, Mobile and web apps

Education

Ph. D. Computational Pathology

University of Bern

M. Sc. Information Systems

Humboldt University of Berlin

B. Sc. Computer Science

Technical University Munich

Competitions & recognition

SGPath

2023

Best Presentation

AI For Climate Hackathon 2021

2021

Winner

Data Science Game

2018

Finalist (Winner of Fair Play Award)

Tools & technologies

  • Azure

  • AWS

  • Visual Studio Code

  • Adobe Ps , Ai , Id

  • Eclipse

  • Office 365

  • ImageJ, Fiji

Skills

  • Python development
  • Machine learning and deep learning development
  • Image analysis
  • Optimization for deployment
  • Containerization (Docker, Singularity)
  • Cloud (Azure, AWS)
  • Statistics and data science
  • Bioinformatics
  • Research from idea to published paper
  • Project Management

About this site

This site has been adapted from the original webiste by Matt Grey on Github which was made available under the GNU General Public License v3.