FoNS Data Science Poster Competition: winner announced!

The Faculty of Natural Sciences invited its PhD students, Postdocs, Early career researchers and research groups working on the area of Data Science theme to participate at this virtual poster competition (closing date was 30th November). The idea was to bring together those interested in data science, constituencies that are producers/developers of data science methods, those who are users of innovative methods and those that do both. Data Science theme leads, Professors Guy Nason and Sophia Yaliraki would like to thank everyone for their contribution to this poster competition.

The judging panel nominated the top two best posters, and in addition the ‘popular choice’ award saw more than 170 Imperial students and staff voting for their favourite poster. 

Congratulations to the winners!

If you would like to learn more about some of these PhD students and find out why they have chosen to focus their research in the area of data science, please read our news. You can check out all the participating posters by clicking on the below links!

View the posters 

1. Hisham Abdel Aty, Department of Chemistry

Machine Learning in Chemistry: From Simulation to the Laboratory

2. George Adams, Department of Life Sciences

Quantifying the spatial structure and cellular population of the bone marrow

3. Mira Davidson, Department of Life Sciences

PlasmoCount: A machine learning tool for detection and staging of malaria parasites from cytological smears

4. Florian Klimm, Department of Mathematics

Node-weighted networks for the integration of single-cell RNA-sequencing data with protein–protein interaction networks

5. Jenna Lawson, Department of Life Sciences

(Note: you need to download this poster in order to listen to the audios)

Silent plantations of Costa Rica - Can forestry plantations support acoustic biodiversity as well as native forests? A big data approach.

6. Bryan Liu, Department of Mathematics

What is the value of experimentation & measurement?

7. Daniel Platt, Department of Mathematics

Group In variant Machine Learning through Near-Isometries

8. Francesco Sanna Passino, Department of Mathematics

Mutually exciting point process graphs for computer network modelling

9. Leonie Stroemich and Florian Song, Department of Chemistry

ProteinLens: a user-friendly web-based application to uncover allosteric signalling in structural data

10. Titus-Stefan Dascalu, Department of Physics

Numerical study of proton beam transport through space-charge lens

11. Nan Wu, Department of Chemistry

 Prediction of allosteric sites: insights from benchmarking datasets