Information Theory, Algorithms & Opera
Hard science theory may be thought of as a pattern recognition and information compression exercise. The more data a theory can account for, the higher the compression ratio and often the more simple and algorithmically beautiful the theory is. The soft sciences often operate on a more descriptive level as patterns are less easily revealed, thus - with a few exceptions such as Darwin’s theory of evolution - achieving a far lower compression rate. I am interested in the interface between hard and soft science and how biological systems build (have evolved) their algorithms to take advantage of the constraints imposed by physics and chemistry. Information Theory and Bayesian Inference offer attractive frameworks with which to analyse and try to understand biological phenomena. I program mainly in Lisp cos Lisp is cool. Oh, yes, and I like opera too.
Richard J Morris
Current Positions:
Project Leader in Computational & Systems Biology; Hon Sen Lecturer in Computing Sciences; Hon Reader in Biological Sciences
 
Previous Positions: Postdoctoral Researcher in Algorithm Development for Structural Bioinformatics (Prof Janet Thornton, FRS, EMBL-EBI Hinxton); Postdoctoral Researcher in Bayesian Structure Solution (Dr Gerard Bricogne, MRC-LMB & Global Phasing Ltd)
 
Education:
PhD in Physical Chemistry (Computational Protein Crystallography) from EMBL & Karl-Franzens University Graz; MSc in Theoretical Physics (Molecular Quantum Mechanics), BSc Physics from the Erzherzog Johann University Graz; Mech. Eng. from the Higher Technical College HTBL u. VA Graz.
 
Contact:
Computational & Systems Biology, John Innes Centre, Colney, Norwich NR4 7UH Richard DOT Morris AT bbsrc DOT ac DOT uk