Michael Paul Stewart Brown
- Bioinformatics. Application of probabilistic and machine learning
techniques to new bioinformatics problems with large amounts of data.
- Probabilistic models. Hidden Markov models, stochastic context-free
grammars, graphical models, Fisher features.
- Classifiers, Machine Learning. Support vector machines, Guassian
processes, information geometries, boosting.
- Unsupervised learning of concepts. Mixture and product models,
simulated annealing, Expectation Maximization.
- Characterization of information flow and mechanisms of living
things. Genomic organization, pathway inference, RNA structure,
transcription, translation, regulation, RNAi, cross species analysis.
- Information retrieval and modeling. Vector space models, large
vocabulary modeling, efficient search, link structure analysis.
Hidden Markov Models, stochastic context-free grammars, Dirichlet
mixtures, support vector machines, RNA modeling including ribosomal
RNA, RNA pseudoknot modeling, and DNA microarray data analysis....
- Email: michaelbrownid "at" gmail.com
- CV: cv.pdf
Just wait until I start using this site for real... :-)
Calculate how confident you are of biological truth when you want to
detect the presence of a minor variant but there is sequencing
noise. Is it real or noise?
A python solution to a simple combinatorial math problem.
A short summary of how to use imagemagick to do image manipulations.
General computer buying guide
Some random links on Irreproducible Research, physics and machine
learning, discrete math, Tensegrity.