Sander Timmer is a Lead Data Scientist Architect in the global Word-Wide Data Insights CTO office at Microsoft. In this role, I focus on delivering end-to-end advanced analytic solutions, often using Machine Learning or Deep Learning techniques on top of our Big Data cloud platform. I obtained my PhD from the University of Cambridge and EMBL – European Bioinformatics Institute specialising in Big Data Biology.
My work at Microsoft
As Lead Data Scientist I was involved with the implementation of Machine Learning and Deep Learning models for various customers. Below are a few PR releases related to my work.
Aviation Analytics at Rolls-Royce
For Rolls-Royce and Singapore Airlines focusing on Fuel Analytics and Predictive Maintenance.
Predictive Maintenance at Otis Elevators
Press release by Otis Elevators.
In Ewan Birney‘ team I worked on understanding genetic variation on both molecular and whole body phenotypes. My PhD thesis has been published under the title understanding the human epigenome using system genetics. To this extent, I have been working on the following project:
- With Richard Durbin and Vishy Iyer working on heritable transcription factor binding measured using CTCF-seq. My part mainly focus on CTCF to CTCF interactions and CTCF on the X-Chromosome.
- Published in PLOS Genetics: Quantitative Genetics of CTCF Binding Reveal Local Sequence Effects and Different Modes of X-Chromosome Association.
- With the Molecular Cardiology MRC department at London to obtain robust measurements of the Human skeleton phenotypes and Human heart phenotypes from MRI scans using Machine Learning approaches.
- With several labs from the ENCODE scale-up group (Jason Lieb, Greg Crawford and Vishy Iyer) working on genome wide associations on heritable chromatin signatures (cQTL) measured using FAIRE-chip. The reconstruction of the Human Epigenome by the integration of chromatin status, transcription factor binding and gene expression. Using System Genetics to determine the effect of genotypes on the epigenome. Professional interests
Big data, biomarker discovery, business strategy, cloud computing, complex biological networks, data mingling, data visualisation, epigenetics, machine learning, market potential, medical genetics, mobile applications, molecular biology, new technologies, next generation sequencing, online marketing, open access, open data, open source, personalised medicine, science into business, start-ups, Internet of Things (IoT), usability and web applications.