Yvan Saeys is associate professor of Machine Learning and Systems Immunology at VIB and Ghent University. He is developing state-of-the-art data mining and machine learning methods for biological and medical applications, and is an expert in computational models to analyse high-throughput single-cell data. The methods he develops have been shown to outperform competing techniques, including computational techniques for regulatory network inference (best performing team at the DREAM5 challenge) and biomarker discovery from high-throughput, single cell data (best performing team at the FlowCAP-IV challenge). Yvan Saeys has published >100 papers in top ranking journals and conferences, ranging from methodological development in machine learning and bioinformatics to applications in cancer, immunology and medicine (Nature Immunology, Nature Methods, PNAS, Bioinformatics). The tools he develops have received several awards and are being used by international consortia. His work has been cited more than 5000 times. The Saeys lab provide expertise in cancer data mining, flow cytometry bioinformatics for leukaemia and lymphoma, and general biomarker discovery and systems approaches to cancer research.