Robert Haase

Dr. rer. medic. Robert Haase

Lecturer, Training Coordinator
ScaDS.AI Dresden/Leipzig
Data Science Center
Universität Leipzig
Humboldtstraße 25
04105 Leipzig

Research interests

• AI-Systems solving Github issues
• Benchmarking Large Language Models for Bio-image Analysis Code Generation
• GPU-accelerated image processing
• Software bridging bio-imaging and image data science

Affiliations / organizations

• Center of Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden / Leipzig, Leipzig University
• Network of European BioImage Analysts (NEUBIAS)
• Global BioImage Analysis Society (GloBIAS)
• German Bioimaging (GerBI)
• NFDI4BioImage

Curriculum vitae

• Since 2023 Lecturer and Training Coordinator, Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig University
• 2020-2023 Group Leader, Bio-image Analysis Technology Development Group, DFG Cluster of Excellence “Physics of Life”, University of Technology, TU Dresden
• 2017-2020 Postdoctoral researcher, Gene Myers lab, Max Planck Insitute for Molecular Cell Biology and Genetics Dresden / Center for Systems Biology Dresden
• 2015-2017 Bio-Image Analyst, Scientific Computing Facility, Max Planck Insitute for Molecular Cell Biology and Genetics Dresden / Scionics GmbH
• 2013-2015 Scientific Assistant, High-Precision Radiotherapy Group (Dr. Perrin / Dr. Richter), OncoRay, Medical Faculty Carl Gustav Carus, University of Technology, TU Dresden
• 2010-2013 PhD Student, Scientific Assistant, Biological and Molecular Imaging Group (Prof. Abolmaali), OncoRay, Medical Faculty Carl Gustav Carus, University of Technology, TU Dresden
• 2008-2010 Student Assistant, Biological and Molecular Imaging Group (Prof. Abolmaali), OncoRay, Medical Faculty Carl Gustav Carus, University of Technology, TU Dresden
• 2006-2010 Student of Computer Science, University of Applied Sciences, HTW Dresden
• 2004-2005 Junior IT Project Leader, Spiritlink GmbH, Erlangen
• 2001-2004 Trainee, IT specialist for software development (Fachinformatiker Fachrichtung Anwendungsentwicklung), Spiritlink GmbH, Erlangen

Funding

• CZI Essential Open Source Software program
  • GPU-accelerating Fiji and Friends Using Distributed CLIJ, NEUBIAS-style (2021-2024), collaboration with Stephane Rigaud (Insitut Pasteur, France) and Martin Jones, Amy Strange and Lucy Collinson (The Francis Crick Institute, London, UK)
• CZI Napari Plugin grants
  • Improving Image Processing (2021-2022), collaboration with Lachlan Whitehead (WEHI Australia)
  • Seamless Integration of Quantitative Bio-Image Analysis Plugins (2022-2023)
• DFG National Research Data Management Initiative
  • NFDI4BioImage (2023-2028), lead by Stefanie Weidtkamp-Peters, Heinrich Heine University Düsseldorf
• ANR-DFG French-German Collaboration for Joint Projects in Natural, Life and Engineering Sciences
  • DeepLineage project (2022-2025) lead by Michalis Averof (IGFL Lyon, France)
• BMBF Program for Interdisciplinary Collaborations to investigate Pathomechanims
  • PACETherapy project (2022-2025) lead by Mike O Karl, DZNE/TU Dresden
• CZI Advancing Imaging Through Collaborative Projects
  • Society for Knowledge Exchange in BioImage Analysis (2023-2025)

Open-Access Training Materials & Talks

• Bio-image Analysis Notebooks
• Generative Artificial Intelligence Notebooks

2026

• “AI4Science Training”, UFZ Magdeburg (Materials, Slides)

• “How to use Generative Artificial Intelligence for Science”, GRK NeuroTune, Leipzig University (Materials, Slides)

• “How to use Generative Artificial Intelligence for Science”, iDiv Leipzig, (Materials, Slides)

• “How Generative Artificial Intelligence impacts Bio-image Data Science”, Danish BioImaging, University of Copenhagen (Video, Materials, Slides)

• “How Generative AI impacts research and teaching”, Arqus Artificial Intelligence and Digital Transformation Living Lab Lecture (Slides)

• “Vision Language Models” and “Agentic Systems”, Guest Lectures in “Secretes of LLMs” Lecture Series, Facult of Computer Science, TU Dresden (Slides)

2025

• “AI4Science” Training, UFZ Leipzig (Slides, Exercises)

• “How Generative AI impacts research and teaching”, Lunch Lecture, Leipzig University (Slides)

• “Using Large Language Models for Bio-image Analysis”, EMBO Practical Course on Advanced Bio Image Analysis, EMBL Heidelberg (Slides, Exercises)

• “How Large Language Models impact Bio-Image Data Science” Janelia & EMBL BioImaging Seminar Series, EMBL Heidelberg, (Video, Slides, Website)

• “AI4Psychology” Training, Faculty of Life Sciences, Leipzig University (Slides, Exercises)

• “Research Data Management” and “Explainable Machine Learning”, AI4Medicine Training, ScaDS.AI, Leipzig University (Slides, Slides, Website)

• “Reproducible Bio-Image Analysis using Python, Napari, Jupyter and AI”, EMBO Practical Course on Lightsheet Microscopy, MPI-CBG Dresden (Slides, Exercises)

• “Autonomous AI Systems for Data Analysis”, ScaDS.AI Life Science Seminar, Leipzig (Slides)

• “DevOps Hands-On Training”, DataWeek Leipzig (Slides)

• “Wie Generative Künstliche Intelligenz Arbeit und Studium verändert”, DataWeek Leipzig (Slides)

• “KI-Kompetenz-Training” an der Uni Leipzig / ScaDS.AI (Slides, Exercises)

• “Bio-image Data Science Lectures” at Uni Leipzig / ScaDS.AI (Slides, Exercises)

• “Collaborative working and Version Control with Git[Hub]” at University Library Leipzig (Slides)

• “Research Data Management and Open Science” at AI4Seismology Training at ScaDS.AI (Slides, Website)

• “Introduction to Machine Learning” at AI4Seismology Training at ScaDS.AI (Slides, Website)

• “Learning and Training Bio-image Analysis in the Age of AI” at Globias + DataCarpentries Workshop IST Austria, Klosterneuburg (Slides, Website)

• “Large Language Models: A practical Introduction for Computer Scientists” Training session at the SECAI Spring Meeting (Slides, Exercises)

• “Explainable AI for Computer Vision” (Slides, Exercises)

• “Large Language Models: An Introduction” guest seminar at Max Planck Institute for Human Cognitive and Brain Sciences (Slides)

• “Large Language Models: An Introduction” guest seminar at Max Planck Institute for Math in Sciences (Slides)

• “Prompt Engineering, Retrieval Augmented Generation and Agents” and “Multi-modal Language Models” guest lectures in lecture series “Secrets of LLMs” at TU Dresden (Slides)

• “Artificial Intelligence in Everydays Research Routine” (Slides, Exercises)

• “Collaborative working and Version Control with Git[Hub]” at University Library Leipzig (Slides)

2024

• “Angebote der NFDIs im Bereich Zoologie”, Meeting of the DFG Fachkollegium Zoologie, December 4th 2024, virtual (Slides)

• “Large Language Models: An Introduction for Life Scientists” at KI Stammtisch at University Library Medicine and Natural Sciences, Leipzig (Slides, Website)

• “Developing a Training Strategy” and “Training Computational Skills in the Age of AI” (Slides 1, Slides 2, Website)

• “Bio-Image Analysis Code Generation”, invited talk at “International Symposium on Image-based Systems Biology”, September 26th 2024, Jena (Website)

• “Towards Preservation of Life Science Data with NFDI4BIOIMAGE” at NFDI4Microbiota Digital Preservation Workshop, September 3rd 2024 (Slides, Website)

• “Bio-image Analysis Code Generation using bia-bob“ Python Webinar Series of the Young Microscopists and Image Analysts working group of German BioImaging e.V. (Video, Slides)

• “Bio-image Analysis Code Generation”, From Images to Knowledge (I2K) virtual conference, October 2024 (Video, (Slides)[https://zenodo.org/records/14001044], Website)

• “Large Language Models – an introduction for life scientists”, GloBIAS Seminar Series, Virtual, August 2024 (Slides, Website)

• “ChatGPT for image Analysis” session in the “AI in Microscopy” Workshop, Virtual / Copenhagen Denmark, August 2024 (Slides, Website)

• Bio-image Data Science Lecture + Practicals, Summer Semester 2024, Leipzig University (Materials, Slides)

• “Large Language Models for Bio-image Analysis”, Mexican Bioimaging worshop (virtual), June 2024 (Website, Slides)

• “Large Language Models for Bio-image Analysis”, Biological Image Processing and Analysis Symposium, June 14th 2024, Nice France, (Slides, Website)

• “Large Language Models for Quantiative Bio-image Analysis”, ScaDS.AI Summer School, Leipig June 2024 (Website, Slides)

• Python Programming Basics, Leipzig University / Academic Lab / ScaDS.AI, June 2024 ([Website)

• Research Data & Software Management, DataWeek Leipzig (Session slides: git, Sharing/Licensing Data Management)

• “Research Software Management using git and zenodo” at RDM Symposium “From Papers to Pixels” at TU Dresden, May 27th 2024 (Slides, Website)

• “Prompt Engineering” at the Networking Event “AI at Leipzig University”, May 2024 (Slides)

• ScaDS.AI Bio-image Data Science Training School, May 2024 (Website)

• “Cultivating Open Training to advance Bio-image Analysis”, FocalPlane Webinar, April 27th 2024 (Slides, Website)

• “Large Language Models for Bio-image Analysis”, ScaDS.AI Living Lab Lecture, April 4th 2024 (Video, Slides)

• “Prompt Engineering for Quantitative Bio-image Analysis”, Monash Advanced Microscopy Seminar (online), March 20th 2024, (Slides, Video recording)

• “Bio-image Analysis with the Help of Large Language Models”, Leibnitz IPHT Seminar, Jena, March 13th 2024 (Slides)

• “Bio-image Analysis using Large Language Models”, IMOL Seminar, Johann Wolfgang Goethe University Frankfurt am Main, March 6th 2024 (Website, Slides)

• “Cultivating Open Training”, SaxFDM Digital Kitchen, March 2024, (Website, Slides)

• “Cultivating Open Training”, Workshop on Efficiently Communicating Bio-image Analysis, Buxted Park, 12-15 Feb 2024, (Slides)

• Sharing and Licensing workshop, ScaDS.AI General Assembly, (Slides)

2023

• “Nichts hält ewig – Wie gehen wir damit um?” (“Nothing is forever - How do we deal with that?”), 5. Ulmer Workshop “Administrative Herausforderungen in Core Facilities und Technologieplattformen”, Ulm University 2023 (Website, slides)

• “Challenges and opportunities for bio-image analysis core-facilities”, Danish BioImaging Stakeholder Meeting, 2023 (slides)

• Prompt Engineering Tutorial (ScaDS.AI Living Lab) (Slides + Notebooks)

• Napari Tutorial (ScaDS.AI Life Science Jour Fixe) (Slides + Notebooks)

• Generative Artificial Intelligence for Bio-image Analysis (I2K2023 Tutorial) (Video, Slides)

• EMBO Practical Course on Advanced Methods of Bio-image Analysis, Heidelberg ( (Website, Video lecture 1, Slides 1, Video lecture 2, Slides 2))

• DIGS-BB Principles of Light Microscopy – Course for DIPP Students, Bio-image Analysis session, Dresden (Website, Materials)

• PoL Early Career Bio-Image Analysis Training School, Dresden (Website, Materials)

• PoL GPU-Accelerated Bio-Image Analysis Training School, Dresden (Website, Materials)

• Guest lecture in the master program Regenerative Biology and Medicine “Quantitative Bio-image Data Analysisin the age of Machine Learning” (Materials)

• NEUBIAS Pasteur course on Bioimage Analysis, Insitut Pasteur, Paris (Website, Materials)

• Latin America workshop on Image data science with Python and Napari, Virtual (Materials)

• Bio-Image Analysis, Biostatistics, Programming and Machine Learning for Computational Biology Lecture in Molecular Bioengineering (Master) and Molecular Biology and Biotechnology (Bachelor) at CMCB, TU Dresden (Materials)

• “Current challenges in GPU-accelerated image processing for the life sciences”, Guest Lecture in Highly Parallel Computing, Faculty of Computer Sience, TU Dresden 2023 (slides)

2022

• “User-friendly, interactive Bio-image Analysis Workflow Design in Napari”, The Crick Bio-image Analysis Symposium, London UK 2022 (slides)

• Image data science with Python and Napari, virtually at EPFL Lausanne (Slides)

• “Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing”, NEUBIAS Defragmentation Training School, 2022 (slides)

• “Hitchhiking through a diverse Bio-image Analysis Software Universe”, Frontiers in Bioimaging, Birmingham, UK 2022, (slides)

• “Advanced bio-image analysis, image data science & AI”, Zebrafish PI Meeting, Dresden, 2022 (slides)

• “Bio-image analysis island hopping: From Fiji to Napari and back”, talk at Instituto Gulbenkian de Ciência Oeiras, Portugal 2022 (slides)

• Bio-Image Analysis, Biostatistics, Programming and Machine Learning for Computational Biology Lecture in Molecular Bioengineering (Master) and Molecular Biology and Biotechnology (Bachelor) at CMCB, TU Dresden (Materials)

• Customizing ImageJ/Fiji using ImageJ Macro + Interactive Design of GPU-accelerated Image Data Flow Graphs in Fiji, Workshop Image analysis and data processing in the super-resolution microscopy, Faculty of Science, Charles University Prague (Slides)

• 3D Image Data Science with open tools & CLIJ crash course: GPU-accelerated image processing in Fiji, EMBO Practical Course on Lightsheet Microscopy, CEITEC BRNO (Website, Slides, Slides, Slides)

• Tracking cells in microscopy image data, EMBO Course on 3D Developmental Imaging, IGC Oeiras (Website, Slides)

• Advanced bio-image analysis, image data science & AI, Workshop at Zebrafish PI Meeting, Dresden (Slides)

• “clesperanto: Open-source, GPU-accelerated image processing across programming languages and software ecosystems” SIMposium 2022 (slides)

• Current challenges in GPU-accelerated image processing for the life sciences, Guest Lecture in High Performance Computing, TU Dresden (Slides)

2021

• Bio-Image Analysis, Biostatistics, Programming and Machine Learning for Computational Biology Lecture in Molecular Bioengineering (Master) and Molecular Biology and Biotechnology (Bachelor) at CMCB, TU Dresden (Materials)

• ImageJ Macro Programming, Centre for Cellular Imaging, Bio-image analysis Course, Gothenburg (Slides)

• Interactive Data Visualization 101 with Fiji & Friends, HIP Summer School (Slides)

• “GPU-accelerated bio-image analysis focusing on processing segmented images”, LS2 Switzerland Webinar 2021 (slides)

• “User friendly GPU-accelerated bio-image analysis beyond cell segmentation”, PSB Symposium Frontiers in Bioimaging, 2021 (slides)

• “Bio-image analysis island hopping: From Fiji to Napari and back”, EuroBioImaging Virtual Pub 2021 (slides, video)

• “Interactive image data flow graphs and GPU-accelerated image processing for everyone”, ABRF Annual Meeting 2021 (slides)

• Object tracking in images, Seminar at HZDR Dresden (Slides)

• Quantitative Bio-image Analysis, DIGS-BB Course Principles of Light Microscopy (Slides)

• Sharing and licensing material, EMBO Workshop on Advanced Methods in Bio-image Analysis (Website, Slides)

• On-the-fly image processing with Python and napari, Smart Microscopy Workshop - Image analysis to improve remote image acquisition (Slides)

2020

• Applied Bio-Image Analysis, Lecture in Molecular Bioengineering (Master) and Molecular Biology and Biotechnology (Bachelor) at BIOTEC, TU Dresden (Videos, Slides)

• Designing GPU-accelerated Image Data Flow Graphs for CLIJ2 and clEsperanto, Images2Knowledge Workshop, 2020 (Video, Slides, Materials)

• GPU-accelerated Image Processing with CLIJ2, NEUBIAS Academy @Home (Video, Slides)

• Intelligent Systems for Bio-Image Analysis, Guest Lectures at Faculty of Computer Science, TU Dresden (Slides)

2019

• GPU-accelerated Image Processing in ImageJ Macro, NEUBIAS Training School 13 for Facility Staff, INESC TEC Porto (Website, Slides)

• Stitching & Image Registration, NEUBIAS Training School 12 for Early Career Scientists, INESC TEC Porto (Website, Slides)

• ImageJ Macro Programming & GPU-acceleration, Image Analysis Introductory Course, CCI University of Gothenbourg (Slides)

• Applied Bio-image analysis, Lecture part of Bioinformatics master programme, Charles University Praque (Slides)

• Applied Bio-image analysis, Lecture part of Molecular Bioengineering master programme at BIOTEC Dresden (Slides)

• Multiview Registration with Fiji and Tracking with Trackmate and Mastodon, NEUBIAS Training School 10 for Early Career Scientists, University of Luxembourg (Website, Slides, Slides)

2018

• Working with Pixels and Objects in 2D and 3D, NEUBIAS Training School 8 for Early Career Scientists, University of Edinburg (Website, Slides, Slides)

2017

• Introduction to ImageJ macro programming, Scientific Computing Facility, MPI CBG Dresden (Website, Slides)

2016

• Introduction to Image Analysis with Fiji, Scientific Computing Facility, MPI CBG Dresden (Website, Website, Slides)

Open Source Software

• Generative AI / LLMs:
  • bia-bob: Code generation in Jupyter Notebooks
  • unprompted: Feedback and suggestions in Jupyter Notebooks
  • git-bob: Answering GitHub/Lab issues
  • bluesky-bob: Answer posts on bluesky
  • outlook-bob: Answer Emails
  • screenshot-question-answerer: Answer questions on screen
  • Benchmarking LLMs for Bio-image Analysis: How good is AI-written code for bio-image analysis?
  • story-writer: Writing stories for kids, including pictures.
  • vlm-pictionary: AI playing Pictionary
  • ipy-llm-kernel: Asking LLMs directly in Jupyter
• stackview: Interactive image visualization in Jupyter
• clesperanto: GPU-accelerated image processing
  • pyclesperanto_prototype: GPU-accelerated image processing in Python
  • pyclesperanto: GPU-accelerated image processing in Python
  • clesperantoj_prototype: : GPU-accelerated image processing in Java
• CLIJ: : GPU-accelerated image processing in ImageJ
  • CLATLAB: : GPU-accelerated image processing in Matlab
  • clicy: : GPU-accelerated image processing in Icy
  • clupath: : GPU-accelerated image processing in QuPath
• APOC: : GPU-accelerated pixel and object classification in Python

Publications

• ORCID
• Google Scholar
• Github
• YouTube
• Twitter

Blog posts

• If you license it, it’ll be harder to steal it. Why we should license our work
• Sharing research data with Zenodo
• Scientific Data Analysis: User Documentation 101
• Managing Scientific Python environments using Conda, Mamba and friends
• Explorative image data science with napari
• Collaborative bio-image analysis script editing with git
• How CLIJ2 can make your bio-image analysis workflows incredibly fast
• Installing Microsoft buildtools on Windows
• Using StarDist in napari with GPU-support in Windows
• GPU-accelerated image processing using cupy and cucim
• Browsing the Open Microscopy Image Data Resource with Python
• GPU-accelerated image processing in the cloud using Google Colab and clEsperanto

Cheat Sheets

• ImageJ Macro
• CLIJ – GPU accelerated image processing in ImageJ Macro

Data

• Tribolium castaneum embryo development as seen with lightsheet microscopy (“Lund” sample, first 36h)
• Example data for using napari-pyclesperanto-assistant
• Strausberg_Tribolium_LA-GFP_tailpole_run
• Example Data for clesperanto
• Napari plugin download statistics
• Awesome napari-plugins that are not on the napari-hub (yet)