Conor Lawless

Scientific Computing

data science, dynamic simulation modelling, genomics, interactive visualisation, dashboards, engineering design, image & video analysis
e: cnr.lwlss@gmail.com

About me

About Me

I am a freelance scientific computing consultant. My background is in engineering and applied maths and I have a great deal of experience with data science, statistics and programming, particularly applied to problems in biology.

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 linkedin and github.

Previous experience

I have a lot of experience of working alongside experimental biologists and clinicians at Newcastle University medical school 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, genome sequencing and single-cell analysis.

Computing

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, dynamic simulation programming and machine learning driven image analysis. 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.

Education