Machine Learning and Bioimage Analysis¶
Short Academic Bio
Qin Yu obtained his PhD in Machine Learning for Bioimage Analysis from the European Molecular Biology Laboratory (EMBL) and Ruprecht-Karls-Universität Heidelberg, supervised by Dr Anna Kreshuk. His thesis focused on robust and accessible segmentation of cells and nuclei in 3D microscopy.
Before joining EMBL, he trained at the Centre for Integrative Systems Biology and Bioinformatics at Imperial College London and the MRC Laboratory of Medical Sciences, where he completed his MSc thesis under the supervision of Professor Aubrey Cunnington.
He received his first degree from University College London (UCL), where he read computer science, mathematics, and statistics. His early research included work on the formal verification of concurrent separation logic soundness, supervised by Professor James Brotherstone, followed by an MEng thesis on GPU computing and machine learning, supervised by Professor John Shawe-Taylor.
Invited Talks¶
- 2025 - Potsdam, Germany 🇩🇪
- The International Conference on Phenotypic Plasticity in Plants
- 2025 - Changchun, China 🇨🇳
- The Young Scholars Forum, The First Hospital of Jilin University
Peer Review¶
- 2025 - PLoS Computational Biology
Teaching¶
Practical courses on deep learning for bioimage analysis with focus on segmentation:
- 🇬🇧 UCL Mentor - University College London (2025)
- 🇮🇹 From Images to Knowledge Workshop - Human Technopole (2024)
- 🇩🇪 Theory@EMBL PhD Course - EMBL Heidelberg (2022, 2023)
- 🇩🇪 Lautenschläger Summer School - EMBL Heidelberg (2022, 2023, 2024)
- 🇩🇪 EMBL Course: Deep Learning - EMBL Heidelberg (2022)
- 🇬🇧 EIPP Bioinformatics PhD Course - EMBL-EBI (2022)
- 🇩🇪 Morphodynamics Workshop - Centre for Organismal Studies Heidelberg (2021)
- 🇺🇸 Deep Learning for Microscopy Image Analysis - Marine Biological Laboratory (2021)
Publications¶
Deep Learning Algorithms¶
- Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022
Bioimage Analysis Methods¶
- PanSeg: Interactive Deep Learning-Based Tissue Segmentation (In Preparation, 2026)
- A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context, Development 2024
Applied Bioimage Analysis¶
- The membrane-to-cortex distance regulates mDia1 activity to control cortical mechanics, Nature Communications 2026