Bio
Daniel started as a Data Wrangler at The Alan Turing Institute in 2022, as part of the Data for Research (Data Wrangling) programme led by Ann-Marie Mallon. He has worked on the Early Detection of Neurodegenerative Diseases (EDoN) project, wrangling a large multi-modal dataset containing clinical data and digital biomarkers. More recently, he has started working on projects within the Environment and Sustainability Grand Challenge.
Daniel obtained a BSc in Zoology from the University of Sheffield and a DPhil in Earth Sciences from the University of Oxford, where he studied evolutionary processes in living and fossil fish. After completing his doctorate, he worked as a data wrangler at MRC Harwell and was part of the Data Coordination Centre for the International Mouse Phenotyping Consortium (IMPC), where he worked on high-throughput phenotyping data. He was also part of a collaboration between Novartis and the Big Data Institute in Oxford, where he developed pipelines for integrating and de-identifying over 250,000 multiple sclerosis (MS) MRI scans from a multi-site dataset.
Daniel has experience in a number of areas including data retrieval and preparation, data standards, data harmonisation, visualisation, and analysis. He is also interested in the novel application of AI methods to address data wrangling challenges, such as using deep learning for data quality control. His work focuses on data wrangling in the health, biological, and environmental sciences, but he is also interested in applying these approaches to other domains.
