Niclas Thomas MMath MRes PhD

Niclas is a professional data scientist with experience at several large retailers, tackling a wide range of business problems using advanced mathematical and statistical techniques.

He gained a PhD at UCL using machine learning to build predictive models of the immune system, combining standard experimental techniques with advanced analytical methods. These methods were applied to a variety of data types, from high throughput sequencing data to lower dimensional cell frequency data. Following completion of his PhD, he worked as a post-doctoral research associate, using machine learning to predict renal transplant failure from flow cytometry data.

Whilst academic data science differs to corporate data science on a couple of points, such as  scope for innovation and pace of delivery, there are far more similarities than differences. Both require sound theoretical knowledge, the ability to programmatically implement mathematical ideas, a collaborative mindset and the ability to communicate complex approaches and results to non-technical colleagues. The last of these points is a real passion of his.

He consults on several immunological projects, advising on statistical analysis, experimental design and visualisation techniques amongst other things. As well as improving the quality of data analysis, he enjoys bringing data science to new audiences, and this website and associated book is written with this in mind.

Email:  niclas.thomas (at) gmail.com

Laura Pallett BSc PhD

Laura is a postdoctoral research scientist at UCL, focussing on understanding the mechanisms of immune dysfunction in chronic hepatitis B infection. Having gained a PhD in viral immunology with first-author publications in Nature Medicine and Journal of Experimental Medicine, she is now investigating immunological mechanisms at play in the human liver, with specific interests in immunometabolism and tissue-residency.

Whilst having trained as a laboratory scientist, she has recently taken a keen interest in advanced in silico techniques for the analysis of immunological data. In particular, she believes that strong fundamentals in statistics coupled with practical knowledge of visualisation techniques is a must for the modern immunologist.

In addition to her academic experience, she has worked at GlaxoSmithKline and acts on immunology advisory boards for Gilead.

 

She is passionate about science communication, and is the co-founder and co-author of her lab's twitter feed, with the aim of bringing their work to a broader audience. She is also an early careers representative on the British Society of Immunology Forum.

Email: laurajanepallett (at) gmail.com

ABOUT US

DATA SCIENCE
FOR
IMMUNOLOGISTS