I am currently working as a postdoctoral researcher in the MRC Cancer Unit at University of Cambridge, working with Prof. Ashok Venkitaraman on mathematical models of early carcinogenesis in epithelial tissues. I am interested in developing machine learning models for integrative analysis of heterogeneous biomedical data.
I did my PhD in statistical genomics at University of Cambridge in the MRC Biostatistics Unit, supervised by Lorenz Wernisch. Before coming to Cambridge, I studied for a Master's degree in Computational Statistics and Machine Learning at University College London. While at UCL, I did my Master's project on sparse linear regression models for gene regulatory network inference, under the supervision of David Barber. I completed my previous degrees in theoretical computer science at the Faculty of Mathematics and Physics at Charles University in Prague.
Apart from my academic research, I am also an active member of the F# community. I speak at conferences and meetups about using F# for data science and machine learning.
Context-dependent clustering [in preparation]
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
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), 2014.
Gene regulatory network inference using sparse probabilistic models
MSc thesis at UCL, supervised by David Barber, 2011. Machine Learning Best Individual Project 2011 - 2nd prize.
Regularization methods for multi-layered neural networks
MSc thesis at Charles University in Prague, supervised by Iveta Mrazova, 2010.
See my upcoming talks at Lanyrd.
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.