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David A. Knowles

As of July 2012 I'm a postdoc with Daphne Koller at Stanford. I did my PhD with Zoubin Ghahramani in the Machine Learning group of the Cambridge University Engineering Department. I was the Roger Needham Scholar at Wolfson College, funded by Microsoft Research. My undergraduate degree comprised two years of Physics before switching to Engineering to complete an MEng with Zoubin. I took the MSc Bioinformatics and Systems Biology at Imperial College in 2007/8.
My research involves both the development of novel machine learning methods and their application to data analysis problems in biology.
I was involved with running the Cambridge University Statistics Clinic.
You can download my CV here. Here's a video of me and my friend Johan falling off cliffs on skis in Flaine, France.

Publications

[with abstracts]

*joint first author.

Workshop papers

Reports/Theses

Presentations

Code

The Matlab code for infinite sparse factor analysis is available here. Please note that this is research code and as such is provided with no warranty and limited to no support.

Infer.NET

I'm a part-time developer and heavy user of Infer.NET. I recently wrote a blog post about some of the new features we've added in Infer.NET 2.4, check it out here.

Misc

Lagrangian duality

My last journal club was on convex optimisation. I think I finally got my head round Lagrangian duality, and hopefully came up with a reasonably intuitive explanation. My focus is on intuition rather than rigor, and is based almost entirely on Boyd and Vandenberghe's tome. I thought I should write this up while it's still fresh, so here you go:

Lagrangian Duality for Dummies

Binomial p-values

Regarding my work on 454 pyrosequencing error rates with Professor Holmes, I was asked about how to calculate a p-value for comparing two draws from a Binomial distribution to test the hypothesis that the number of substitutions seen in the sample is significantly greater than the number of substitutions seen in the control. There is actually no need to use the Poisson approximation, and the Binomial distribution very naturally takes care of varying coverage. I explain my approach here.

Emacs for Dummies

A few notes on using Emacs for Unix non-gurus.

Contact details

E-mail: dak33 at cam dot ac dot uk
Mail: Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, UK

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Information provided by David Knowles (dak33)