Dr. Simon Rogers

Simon is a a senior lecturer in the School of Computing Science at the University of Glasgow. His research interest involves the development of Machine Learning and Statistical techniques to help with the analysis of complex datasets, particularly within the field of Metabolomics but also other -omics fields, Human-Computer Interaction and Information Retrieval. His metabolomics work is done in collaboration with many people, particularly Glasgow Polyomics (of which he's an affiliate member).

Dr. Justin van der Hooft

After his PhD in Wageningen, The Netherlands, Justin moved to Glasgow Polyomics to work with Dr Karl Burgess and Prof. Mike Barrett and different partners from Glasgow Polyomics. Justin obtained an ISSF Fellowship from the Wellcome Trust to work on method development and implementation of fragmentation approaches to enhance the metabolite annotation capacities of the high-resolution LC-MS systems focusing on small polar metabolites in urine, beer, and bacterial extracts. Justin has been working on several metabolomics projects thereby exploiting the information-rich fragmentation data that modern mass spectrometers generate and alleviate the bottleneck of metabolite annotation and identification in untargeted metabolomics approaches. In the MS2LDA project, Justin provides valuable biological data interpretations and insights that is crucial to the development of the system. He recently moved back to his academic roots to take up a shared Postdoc position between WUR and the group of Prof. Pieter Dorrestein at the UCSD, USA. The work will be focusing on how to combine workflows developed for genome and metabolome mining to aid in functional annotations of genes and structural annotations of metabolites.

Dr. Joe Wandy

Joe is a data scientist at Glasgow Polyomics, University of Glasgow. Before working at Glasgow Polyomics, Joe was a PhD student at the School of Computing Science, supervised by Simon. His research interest is in natural language processing and the application of machine learning methods to the analysis of complex biological data. In particular, this also involves the necessary large-scale processing of data required prior to modelling and also the effective visualisations of analysis results.

Yunfeng (Frank) Zhu

Frank is a data scientist at the Wellcome Centre for Molecular Parasitology, University of Glasgow. His duties in the bioinformatics team include building biological pipelines and performing data analysis. Frank has an MSc in Artificial Intelligence from the University of Edinburgh. He is always keen learner on Machine Learning and new techniques, and he is passionate about working with data.