Moritz Gerstung's research leverages machine learning and AI to understand tumor growth and development. His work spans cancer evolution, genomics, and digital pathology, integrating computational approaches to derive biological insights.
For more information, please visit: https://www.dkfz.de/en/artificial-intelligence-in-oncology
Wolfgang Huber enjoys exploring new datasets, developing statistical methods, and discovering new biological insights. The Huber group studies biological systems by developing statistical and computational methods for the analysis of new data types and novel, large systematic datasets. These include single-cell and spatial omics, high-throughput drug- and CRISPR-based perturbation assays, and quantitative imaging. Projects range from applied data analysis for biological discovery to theoretical method development. The team studies fundamental biological model systems as well as clinical samples for direct applications in biomedicine and precision oncology.
For more information, please visit https://www.huber.embl.de/group/.
As a Group Leader at EMBL, Anna Kreshuk supervises an interdisciplinary team working with large, volumetric, challenging datasets. She is passionate about working closely with biologists and microscopists, “I want to help biologists do things they’re not even considering at the moment because it takes too long to do. By removing the imaging analysis bottlenecks, we’ll enable researchers to think of more interesting and ambitious experiments.”
To learn more about the Kreshuk group’s work with machine learning-based methods, tools for automatic segmentation, classification, and analysis of biological images, go to: https://www.embl.org/groups/kreshuk/
