Rate My Professor Tim Ebbels

Professor Tim Ebbels is Professor of Biomedical Data Science at Imperial College London, within the Faculty of Medicine's Department of Metabolism, Digestion and Reproduction. He serves as Head of the Section of Bioinformatics in the Division of Systems Medicine. Ebbels earned his PhD in astronomy from the University of Cambridge in 1998, training initially as a physicist before transitioning over 20 years ago into biomedical data science. He applied mathematical techniques from astrophysics to analyze NMR spectroscopic data from biofluids, pioneering computational metabolomics and machine learning applications in spectroscopic data. At Imperial College, he has held positions progressing from Senior Lecturer and Reader in Computational Bioinformatics to his current professorship, promoted in 2021. He co-established the MRes in Biomedical Research programme in 2005, which has trained over 700 researchers, nearly 500 of whom pursued PhDs. Currently, he is Programme Director for the MRes in Biomedical Research and Co-Lead for its Data Science stream. Ebbels also directs the Hands-on Data Analysis for Metabolomics short course and contributes to MSc programmes in Health Data Analytics and Machine Learning, Bioinformatics and Theoretical Systems Biology.

Ebbels' research lies at the interface of multivariate data analysis and post-genomic technologies, focusing on metabolomics, bioinformatics, chemometrics, and multivariate statistics. His group develops methods to integrate metabolomic data with other omics datasets, analyze biological pathways, identify unknown metabolites in complex mixtures, and process data from raw instrument outputs to biological interpretation. Notable contributions include the BATMAN software, which employs Bayesian models to deconvolve overlapping signals in NMR spectra of plasma and other biofluids, facilitating broader use in the NMR metabolomics community. He has authored over 192 publications, with more than 17,000 citations on Google Scholar. Key works include 'Recent Advances in Mass Spectrometry-Based Computational Metabolomics' (2023), 'Use Cases, Best Practice and Reporting Standards for Metabolomics in Regulatory Toxicology' (2019), and 'Metabolic Profiling and the Metabolome-Wide Association Study'. His work advances pathway-based multi-omics integration and large-scale cohort studies on disease origins. Ebbels emphasizes reliable AI applications in biomedicine, advocating for explainable AI to build trust and address biases.