Academic Career and Research Areas
Tingying Peng (b. 1984) conducts research on AI for microscopy image analysis. The mission of her Helmholtz Young Investigator group is to create new AI methods to help life scientists and pathologists analyze microscopic images more quantitatively and efficiently, allowing them to extract more knowledge. Her group has worked on various microscopy imaging types, including histopathological images for computational pathology, classic brightfield and fluorescence images, and more advanced ones, such as Cryo-ET, 3D light-sheet microscopy and, extended depth-of-field (EDOF) microscope with “Electrically Tunable Lenses”.
Tingying Peng studied Electronic Engineering and Applied Mathematics in Peking University in China in 2005. Then she completed her PhD in Biomedical Engineering from Oxford University in the UK in 2009, working on developing novel signal processing methods for the analysis of cerebral blood flow and metabolism. In 2013, Dr Peng joined the Technical University of Munich (TUM), Germany as a Humboldt research fellow and started her research in the field of microscopy image processing. Since the fall of 2020, she has been a Principle Investigator in Helmholtz AI in Helmholtz Zentrum Münch.
Awards
- 2015 Laura Bassi Award from TUM, Munich, Germany, support female researcher in science
- 2013 Postdoctoral fellowship of the Humboldt Foundation
- 2005 Dorothy Hodgkins Postgraduate Award, support outstanding PhD student, UK
Key Publications
Wagner S. et al.”Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study”. Cancer Cell (2023)
AbstractWagner S. et al.”Make deep learning algorithms in computational pathology more reproducible and reusable”. Nature Medicine (2022)
AbstractMoebel E. et al. “Deep Learning Improves Macromolecule Identification in 3D Cellular Cryo-Electron Tomograms.” Nature Methods (2021)
AbstractPeng T, et al. “A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images”. Nature Communications (2017)
AbstractVahadane A, Peng T, et al. “Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images.” IEEE Transactions on Medical Imaging (2016)
Abstract