About me

Research Focus in Biolab Automation

• Management of big research data in the lab context (FAIR data)
• Integration of hardware and software components for automation
• Machine Learning on multimodal lab data (omics and imaging data)
• Robot-based liquid handling

Work Experience

• Since 12/2020: Research associate and PhD student at the Munich Institute of Robotics and Machine Intelligence (MIRMI) at TU Munich and the Munich School for Data Science (MUDS) with Prof. Dr.-Ing. Sami Haddadin
• 01/2017 - 11/2020: Senior Quantitative Business Modeler at the Horváth & Partners Steering Lab, Munich

Education

• 08/2012 - 10/2015: M. Sc. in Physics at University of Göttingen, University of Stockholm and at the Max Planck Institute for Dynamics and Self-Organization, Göttingen
• 10/2009 - 07/2012: B. Sc. in Physics at University of Göttingen and at the Max Planck Institute for Dynamics and Self-Organization, Göttingen

Publications

• H. Zwirnmann, D. Knobbe, U. Culha and S. Haddadin, "Towards Flexible Biolaboratory Automation: Container Taxonomy-Based, 3D-Printed Gripper Fingers," accepted for publication at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, USA, 2023.
• M. Eckhoff, H. Zwirnmann, A. Swikir and S. Haddadin, "Towards Connecting Control to Perception: High-Performance Whole-Body Collision Avoidance using Control Compatible Obstacles," accepted for publication at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, USA, 2023.
• H. Zwirnmann, D. Knobbe and S. Haddadin, "Towards Automated End-to-End Control for Holotomography Microscopy: Workflow Design and Data Management," 22nd World Congress of the International Federation of Automatic Control (IFAC), Yokohama, Japan, 2023.
• D. Knobbe, H. Zwirnmann, M. Eckhoff and S. Haddadin, "Core Processes in Intelligent Robotic Lab Assistants: Flexible Liquid Handling," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 2335-2342, doi: 10.1109/IROS47612.2022.9981636.
• C. Moldenhauer and H. Zwirnmann, "Basket Analysis in Practice: Mathematical Models and Applications in Offline Retail," in Performance Management in Retail and the Consumer Goods Industry, M. Buttkus and R. Eberenz, Ed. Cham (Germany): Springer, 2019, pp. 369–384, doi: 10.1007/978-3-030-12730-5_24.
• A. Schlemmer, H. Zwirnmann, M. Zabel, U. Parlitz and S. Luther, "Evaluation of machine learning methods for the long-term prediction of cardiac diseases," 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), Trento, Italy, 2014, pp. 157-158, doi: 10.1109/ESGCO.2014.6847567.

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