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 analyze 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, and his work has been cited more than 9500 times.