Dr Anita Faul
Data Scientist
Contact information
+44 (0)1223 272618
British Antarctic Survey
High Cross
Madingley Road
Cambridge
CB3 0ET
United Kingdom
Biography
Anita Faul came to Cambridge after studying two years in Germany. She did Part II and Part III Mathematics at Churchill College, Cambridge. Since these are only two years, and three years are necessary for a first degree, she does not hold one. However, this was followed by a PhD on the Faul-Powell Algorithm for Radial Basis Function Interpolation under the supervision of Professor Mike Powell. She then worked on the Relevance Vector Machine with Mike Tipping at Microsoft Research Cambridge. Ten years in industry followed where she worked on various algorithms on mobile phone networks, image processing and data visualisation. Current projects are on machine learning techniques applied to data from the natural environment.
Research interests
There are several challenges with which data presents us nowadays. For one there is the abundance of data and the necessity to extract the essential information from the data. When tackling this task a balance has to be struck between putting aside irrelevant information and keeping the relevant one without getting lost in detail, known as overfitting. The law of parsimony, also known as Occam’s razor should be a guiding principle, keeping models simple while explaining the data. The next challenge is the fact that the data is not static. New data arrives constantly through the pipeline. Therefore, there is a need for models which update themselves as the new data becomes available. The models should be flexible enough to become more complex should this be necessary. In addition the models should inform us which data needs to be collected so that the collection process becomes most informative. The third challenge is the analysis. Can we build systems which inform us of the underlying structure and processes which gave rise to the data? Moreover, it is not enough to discover the structure and processes, we also need to add meaning to it. Here different disciplines need to work together. Another challenge are the conclusions we draw from the data. After all, as popularised by Mark Twain: "There are three kinds of lies: lies, damned lies, and statistics." An objective measure of confidence is needed to make generalized statements.
Publications
Textbook: "A Concise Introduction to Machine Learning" https://www.amazon.com/gp/product/0815384106/
Textbook: "A Concise Introduction to Numerical Analysis" https://www.amazon.com/gp/product/1498712185/
"Semi-supervised Learning with Graphs: Covariance Based Superpixels for Hyperspectral Image Classification”. P. Sellars, A. Aviles-Rivero, N. Papadakis, D. Coomes, A. Faul, C.-B. Schönlieb. International Geoscience and Remote Sensing Symposium (IGARSS), IEEE (2019)
“Deep Learning Applied to Seismic Data Interpolation”. A. Mikhailiuk, A. Faul, European Association of Geoscientists and Engineers (EAGE), IEEE (2018).
”Bayesian Feature Learning for Seismic Compressive Sensing and Denoising”, G. Pilikos, A.C. Faul, Geophysic (2017).
“Seismic compressive sensing beyond aliasing using Bayesian feature learning”, G. Pilikos, A.C. Faul and N. Philip, 87th Annual International Meeting, SEG, Expanded Abstracts (2017).
“Relevance Vector Machines with Uncertainty Measure for Seismic Bayesian Compressive Sensing and Survey Design”, G. Pilikos, A.C. Faul , IEEE International Conference on Machine Learning and Applications (2016).
“The model is simple, until proven otherwise – how to cope in an ever changing world”, A.C. Faul, G. Pilikos, Data for Policy (2016).
“A Krylov subspace algorithm for multiquadric interpolation in many dimensions”, A.C. Faul, G. Goodsell and M.J.D. Powell, published in IMA Journal of Numerical Analysis (2005).
“Fast marginal likelihood maximisation for sparse Baysian models”, M.E. Tipping, A.C. Faul, published in Proceedings of the Ninth International= Workshop on Artificial Intelligence and Statistics (2003).
“Analysis of Sparse Bayesian Learning”, A.C. Faul, M.E. Tipping, published in Advances in Neural Information Processing Systems 14 (2002).
“A variational approach to robust regression”, A.C. Faul, M.E. Tipping, published in the Proceedings of ICANN’01.
“Proof of convergence of an iterative technique for thin plate spline interpolation in two dimensions”, A.C. Faul, M.J.D. Powell, published in Advances in Computational Mathematics, Vol. 11 (1999).
“Krylov subspace methods for radial basis function interpolation”, A.C. Faul, M.J.D. Powell, published in Numerical Analysis, (1999).
“Iterative techniques for radial basis function interpolation”, Ph.D. thesis.