Nicos Angelopoulos

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I received my PhD in computer science from City University, London in 2001.
Prior to that I studied for a Masters in advanced computer science at Imperial College (1993)
and a BSc in computer science and statistics at Keele University ('89-'92).
I am currently a group leader in computational biology at the Pirbright Institute.

Previously I worked in a number of research positions, including

• senior staff scientist in applied statistics at the Sanger Institute's Cancer Genome Project (now CASM)
  where I promoted the use of Bayesian Networks for the analysis of large large genomic cohorts
  and worked on statistical modeling of cancer evolution (e.g. in clear cell renal cell cancer)
  
• research fellow at University of York where I worked with James Cussens.
  There we developed Bims, a Bayesian machine learning, probabilistic programming language and system

Since my PhD I worked as a researcher on a number of projects in the UK and the Netherlands.
The last few years my research has focused on the development and application of principled
formalisms that combine logic and probability along with associated stochastic algorithms in computational biology.

My long term goal is towards incorporation of existing knowledge in reconstruction biochemical networks from data.
The techniques I concentrate on draw from research in AI. Of particular interest in this quest
is the conceptually clear way with which the Bayesian framework allows for the incorporation of prior information.
Furthermore, Bayesian averaging techniques can provide a stochastic setting for robust network inference.

I have worked with logic and logic programming throughout my career.
I am a staunch advocate of logic programming's suitability for doing research in computational biology.

In my research I heavily, if not solely, use open source software such as Yap and SWI Prolog systems, R, TeX/LaTeX, and Linux.

Last update 2024-8-11