I'm a data scientist and a machine learning researcher and an avid conference speaker. I enjoy giving talks on all topics data science, and functional programming. Currently I do most of my programming in R and F#, and I got awarded the Microsoft MVP award for my work in the F# community. I originally started as a programmer but I got interested in machine learning early on and did a mathematics PhD at the University of Cambridge, where I worked on machine learning and statistical methods to analyze complex biomedical datasets. After that I spent two years as a postdoctoral researcher at the MRC Cancer Unit in Cambridge where I worked on mathematical models of early carcinogenesis in epithelial tissues. Starting from 2018, I'll be joining the Alan Turing Institute in London as a data scientist.
See my upcoming talks at Lanyrd.
If you're interested in an R or F# data science workshops, please get in touch.
A Class of Environmental and Endogenous Toxins Induces BRCA2 Haploinsufficiency and Genome Instability(2017)
Shawn L. W. Tan et al., Cell , Volume 169 , Issue 6.
Clusternomics: Integrative Context-Dependent Clustering For Heterogeneous Datasets (2017)
Evelina Gabasova, John Reid, Lorenz Wernisch.
Non-Replicating Mycobacterium tuberculosis Elicits a Reduced Infectivity Profile with Corresponding Modifications to the Cell Wall and Extracellular Matrix (2014)
Joanna Bacon, Luke J. Alderwick, Jon A. Allnutt, Evelina Gabasova, Robert Watson, Kim A. Hatch, Simon O. Clark, Rose E. Jeeves, Alice Marriott, Emma Rayner, Howard Tolley, Geoff Pearson, Graham Hall, Gurdyal S. Besra, Lorenz Wernisch, Ann Williams, Philip D. Marsh PLoS ONE 9(2).
Gene regulatory network inference using sparse probabilistic models (2011)
MSc thesis at UCL, supervised by David Barber, 2011. Machine Learning Best Individual Project - 2nd prize.
Regularization methods for multi-layered neural networks (2010)
MSc thesis at Charles University in Prague, supervised by Iveta Mrazova.
F# Data Twitter toolbox is an F# open source wrapper around Twitter API. The library allows easy access to timelines, tweets and user information. You can see it in action in Chapter 5 of F# Deep Dives book. View on github.