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RAPIDS 2018 Talk - How I learned to stop worrying and love version control
Full RAPIDS 2018 schedule and tickets available here
Keynote - How I learned to stop worrying and love version control - Dr Stephen J Newhouse and Luke Marsden
Dr. Stephen Newhouse. Data Science Group Lead & Senior Bioinformatician, NIHR BRC-MH ;Dept. Biostatistics & Health Informatics, KCL; FARR IHI UCL
Luke Marsden. Founder, Dotmesh.com
Abstract
Coming soon!
Bios
Luke is the CEO and Founder at his new venture, Dotmesh. He is also a Kubernetes SIG lead for SIG-cluster-lifecycle, where he was involved in developing the first version of kubeadm.
He previously worked on Developer Experience at Weaveworks, where he spoke and taught at conferences, meetups and trainings on cloud native topics such as container networking, monitoring with Prometheus, continuous delivery and OpenTracing. Before that he was the CTO and Founder at ClusterHQ, where he got involved right at the start of the Docker and Kubernetes journey, collaborating closely with Docker and others to develop the first Docker volume plugin mechanism and build the first implementation of container persistence, Flocker.
Stephen studied Molecular Biology at The University of Liverpool then went on to complete a Ph.D. in Genetics at Queen Mary University of London. Currently, he’s employed as Lead Data Scientist and Senior Bioinformatician at The Bioinformatics Core at the NIHR Biomedical Research Centre for Mental Health, Kings College London.
His work has included dealing with all kinds of data including molecular, genetic and clinical, cross-sectional and time-series to identify 1) potential biomarkers for disease prediction and progression, 2) novel therapeutic targets, and 3) contribute to a better understanding of human disease.
He has a wide range of experience in the analysis of expression and SNP arrays, next generation sequencing data and network/pathway analyses. His work has required the extensive use of multiple bioinformatic and biostatistical approaches for the creation and implementation of pipelines for mixed -omic data analysis, data integration, and visualization, and applied predictive modelling.
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