Christopher Greenman

Dr

  • 2.18 Biology

If you made any changes in Pure these will be visible here soon.

Personal profile

Career

He has a mathematics degree from Durham University, and a mathematics PhD from the University of Edinburgh, utilizing number theory to study the spacing distributions of quantized harmonic oscillators. He was a lecturer at Bolton University Maths Department for five years, after which two years of mathematical consultancy in industry took place, examining problems varying from satellite positioning algorithms to machine learning approaches to drug discovery. He then spent seven years at the Sanger Institute, developing statistical and mathematical techniques required to analyse cancer genomic data. He then joined the School of Computing Sciences in 2011.

Research Opportunities

Enquiries are welcome (email me) from potential PhD students. He would be keen to recruit anyone interested in stochastic processes, statistical physics, combinatorics and/or their applications to population genetic, genome rearrangement, or evolutionary processes.

 

 

 

Key Research Interests and Expertise

Chris Greenman is part of the Computational Biology and the Machine Learning and Statistics research groups. Recent areas of interest include; the development of techniques from statistical physics applied to age-dependent branching processes, including (quantum) field theoretic methods, applications to cell populations and mutation processes, combinatorial approaches to counting and representing genomic rearrangement processes, cancer and viral evolutionary processes.  

Selected Recent Papers:

  • Greenman C D, Second quantization approaches for stochastic age-structured birth-death processes, http://arxiv.org/abs/1512.05431, 2016.

  • Greenman C D and Chou T, Kinetic theory of age-structured stochastic birth-death processes, Phys. Rev. E 93, 012112, 2016.

  • Penso-Dolfin L, Wu T, Greenman C D, The Combinatorics of Tandem Duplication, Discrete Applied Mathematics, 194, pp. 1–22, 2015.

  • Chedom D F, Murcia P R, Greenman C D, Inferring the Clonal Structure of Viral Populations from Time Series Sequencing, PLoS Computational Biology, 11, 2015.

 

Teaching Interests

Advanced Statistics, Computing Principles, Natural Science Project Coordinator.

Expertise related to UN Sustainable Devlopment Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

Network

Recent external collaboration on country/territory level. Dive into details by clicking on the dots or