Daniele has a background in engineering, computer science, and bioinformatics. He earned master’s degrees from Politecnico di Milano and the University of Milan, in Computer Science and Engineering and in Bioinformatics for Computational Genomics. During his studies, he joined the Saez-Rodriguez Group at Heidelberg University, where he completed two thesis projects and worked as a student research assistant. His research examined how biological prior knowledge and other inductive biases can, and sometimes should not, be incorporated into machine learning models, focusing on graph neural networks and knowledge-constrained architectures, via convex optimization. He also contributed to network biology software and to metabolic prior knowledge resources integration. Daniele joined the Ewald Group at EMBL-EBI in 2026 as a predoctoral fellow, and his research now focuses on decoding human chemical exposures and their biological effects through high-dimensional molecular profiling. More broadly, he is interested in biologically informed machine learning, perturbation modelling, and interpretability.
Search for Daniele Bottazzi's papers on the Publications page