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Dr Anita Faul

Dr Anita Faul

Teaching Associate

Fellow, Director of Studies in Mathematics and Graduate Tutor at Selwyn College


Office Phone: +44 (0)1223 337273

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. This collaboration resulted in an Erdős number of 4 (Powell - Beatson - Chui - Erdős). After this she 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 visualization. Current projects are on machine learning techniques. In teaching she enjoys to bring out the underlying, connecting principles of algorithms which is the emphasis of her book on Numerical Analysis. She is working on a book on machine learning. She has another life, and it definitely feels like another life, as mother of three.

ORCID iD iconorcid.org/0000-0002-5911-2109

Moodle

Fundamentals, Interpolation and Non-Linear Systems
Linear Systems
Numerical Integration and ODEs
Numerical Differentiation and PDEs
Machine Learning
Machine Learners

Collaborators

Key Publications

A Concise Introduction to Numerical AnalysisThis textbook provides an accessible and concise introduction to numerical analysis for upper undergraduate and beginning graduate students from various backgrounds. It was developed from the lecture notes of four successful courses on numerical analysis taught within the MPhil of Scientific Computing at the University of Cambridge. The book is easily accessible, even to those with limited knowledge of mathematics.

Students will get a concise, but thorough introduction to numerical analysis. In addition the algorithmic principles are emphasized to encourage a deeper understanding of why an algorithm is suitable, and sometimes unsuitable, for a particular problem.

Additional material such as the solutions to odd numbered exercises and MATLAB® examples can be downloaded from the publishers' webpage. The book can be purchased here.

  • ''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.
  • "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.