Robert Haase

Robert Haase is computer scientist by training and follows his curiosity deeper and deeper into the life sciences. He received a PhD from the Faculty of Medicine Carl Gustav Carus of the TU Dresden for his work on swarm intelligence based algorithms for medical image segmentation in the cancer research context. He is lecturer for bio-image analysis, bio-statistics and programming at the Biotechnology Center of the TU Dresden. His postdoctoral research in Gene Myers lab at the Center for Systems Biology and the Max Planck Institute for Molecular Cell Biology and Genetics concentrates on bridging the disciplines computer science and biology to forward understanding of how tissues and organisms form. He studies Tribolium castaneum embryo development and for that programs smart microscopes, real-time image analysis and machine learning tools easing the way how scientists interact with multi-dimensional image data to gain new insights.

About Robert Haase

Microscopy background: Image Analysis

Posts by Robert Haase

Sharing research data with Zenodo

Posted by , on 15 February 2023

TL;DR: Sharing data open access is good scientific practice. If data is shared via online portals such as, we can implement best practices for sharing, licensing, reusing and citing research data. In this blog post I guide through the minimal procedures that are necessary to share a dataset publicly following the FAIR principles; to

Managing Scientific Python environments using Conda, Mamba and friends

Posted by , on 8 December 2022

TL:DR: This blog post gives short instructions and explanations to demystify how some scientific Python programmers, including myself, organize their coding environment for the sake of reproducibility of science. We will see what conda and mamba are, what they are good for, and how to use them properly. We will also take a short excursion

Explorative image data science with napari

Posted by , on 23 May 2022

When analysing microscopy image data of biological systems, a major bottleneck is to identify image-based features that describe the phenotype we observe. For example when characterising phenotypes of nuclei in 2D images, often questions come up such as “Shall we use circularity, solidity, extend, elongation, aspect radio, roundness or Feret’s diameter to describe the shape

Collaborative bio-image analysis script editing with git

Posted by , on 4 September 2021

TL;DR: I’m a computer scientist who often collaborates with biologists on bio-image analysis scripts. We are using more and more git, a version control program, for working on code collaboratively. When using git, we speak about repositories, commits and pushing to the origin. We also make forks, send pull-requests and merge code. This blog post

How CLIJ2 can make your bio-image analysis workflows incredibly fast

Posted by , on 14 July 2020

Do you also spend a substantial amount of your time waiting for automated image analysis to finish? I did, again and again for more than a decade. And, then,  the morning after running a script overnight, you realize that a parameter was wrongly set. It was about time to change that. History Processing on graphics

Comments by Robert Haase

Yes, I think so. Unfortunately, making environment.yml files that work on all operating systems can be tricky, especially when working with deep-learning, GPUs and other advanced stuff. That's why installation instructions are sometimes a bit cumbersome. I hope this will change one day.
by Robert Haase in Managing Scientific Python environments using Conda, Mamba and friends on 27 February 2023
Correct! Mamba is faster than conda in determining which packages should be installed. The installation itself should be similarly fast and after installation there is no difference, too.
by Robert Haase in Managing Scientific Python environments using Conda, Mamba and friends on 9 December 2022