Conor Lawless

Scientific Computing

data science, dynamic simulation modelling, genomics, interactive visualisation, dashboards, image & video analysis

If you’d like me to work on projects of any size, please get in touch
t: @cnrlwlss

About me

About Me

I am a freelance scientific computing consultant. I’d really like to help you with any computational projects. My background is in engineering and applied maths and I have a great deal of experience with data science, statistics and programming.

Topics I could help you with include data visualisation, statistical and mathematical modelling, dynamic simulation and image analysis. Please drop me a line or give me a call on +44 (0)7398 748 247 if you’d like to discuss how I could contribute to your project. I can also be found on twitter, linkedin, google+ and github.

Previous experience

I have more than 15 years of experience working alongside experimental biologists within the Institute for Cellular and Molecular Biosciences at Newcastle University, UK and at Rothamsted Research, Harpenden, UK.

Computational biology

I have worked on a wide range of biological topics, including modelling molecular-level and cell-level disease processes in human tissues, predicting the effect of climate change on plant growth and crop yield, robot-assisted high-throughput analysis of the growth rate of genetically modified microbial cultures, quantification of genetic interactions on a genome-wide scale and genome sequencing.


My work as a research scientist has involved tackling a wide range of interesting technical problems: automated high-throughput image analysis, distributed high-performance computing (including cloud computing), interactive visualisation & exploration of large datasets, Bayesian inference for large, hierarchical models, optimisation and dynamic simulation programming. Mostly, I program in Python, R, Haskell, Julia and C++.

Technical writing

An up-to-date list of my academic publications can be found on Google scholar. I also developed websites to host software for image analysis, statistical analysis and data visualisation during quantitative fitness analysis (QFA), a high-throughput screening method I developed at Newcastle University.

An archived set of older blog posts, including a mixture of personal and technical articles, can be found here.