
Algorithms and computational complexity lie at the heart of the fundamental question of determining which problems in computer science we can or cannot hope to solve in a reasonable amount of time. In recent years this has been nowhere highlighted more clearly than in the rapidly developing areas of bioinformatics and computational biology. The advent of techniques for gathering huge volumes of genetic data at a reasonable cost has lead to hopes of understanding many deep problems in biology. However, it lies in the sphere of theoretical computer science to answer the question of which of these problems will be solvable, and which will turn out to be NPhard, which would suggest it is impossible to compute an exact solution efficiently. Phylogenetics is the reconstruction and analysis of phylogenetic (evolutionary) trees and networks based on inherited characteristics. In evolutionary biology, phylogenetic trees are used to represent the ancestral history of a collection of presentday species. Creating a such a "tree of life" has been a primary goal of systematic biology since Charles Darwin's first sketch of an evolutionary tree in 1837, and is now the focus of a global academic effort. Phylogenetics has also proved to be important in the study of mutating diseases; recent work reconstructing the phylogeny of HIV has helped trace the origins of the disease. The broad aim of this project was to develop algorithms and randomised approximation schemes which will be beneficial to biologists working in the field of phylogenetics, as well as to devise new techniques for analysing such algorithms, which will be of independent interest in theoretical computer science. 
Novemeber 2006:  School of Computing Sciences, University of East Anglia, UK.  
2 January  27 March 2007:  Department of Mathematics and Statistics, University of Canterbury, NZ.  
September  December 2007:  At the Phylogenetics programme at the Newton Institute, Cambridge, U.K.  
March 2008:  At the Combinatorics and Statistical Mechanics programme at the Newton Institute.  
11 May  16 May 2008:  Dagstuhl seminar: Design and Analysis of Randomized and Approximation Algorithms.  
Spring 2009:  Department of Mathematics and Statistics, University of Canterbury, NZ. 