Find a Data Champion

I provide Bioinformatic support for a few research groups in the Cambridge Stem Cell Institute. For the most part I deal with the analysis of single cell sequencing data, storage management of such data and the deployment of data processing pipelines into HPC environments.

I primarily work with large-scale language datasets, using methods from quantitative and computational social sciences, as well as the digital humanities. I also teach associated research methods, like statistics, data wrangling, and visualization. 

Outside my research, I am an active science communicator, primarily via two websites that I author: Effectiviology (https://effectiviology.com/), where I write about psychology and philosophy that have practical applications, and Solving Procrastination (https://solvingprocrastination.com/), where I write about what procrastination is, why we do it, and how to overcome it. 

As a Data Champion, I focus on the use of quantitative and computational methods for working with data (e.g., for data wrangling and statistical analyses). I also deal with associated topics, like maximizing the impact of research through open research and science communication. 

Marina has a PhD from the Chemical Engineering and Biotechnology Department at the University of Cambridge. She has previous industrial experience in R&D by working with multinational companies and start-ups. Currently, Marina is Commercialisation Associate in the Physical Sciences team at Cambridge Enterprise. She identifies and evaluates new technologies arising from University research, supporting academics with the protection of their intellectual property and commercialisation of their research outputs.

Prior to joining Cambridge Enterprise, Marina worked as a Research Analyst at the University of Cambridge. Amongst her main responsibilities, Marina conducted research and analysis of international projects and partnerships, providing analytical trends and insightful data that allowed the University to forge strategic partnerships with external institutions.

I'm a research fellow working to improve data management practices broadly across the materials and chemical sciences, with the aim to accelerate discovery and enable new data-driven approaches in the physical sciences. I lead the development of the decentralised open source RDM platform, datalab, used by several labs in Cambridge and beyond to track sample provenance, ingest raw characterisation data from instruments and easily disseminate data within and between labs. datalab has been designed primarily with materials chemistry and battery groups in mind, but has been extended to work for polymer research, direct air capture and autonomous labs. I also work extensively on API standardisation for crystal structure databases, via my involvement in OPTIMADE. I am very happy to discuss how people can engage with this work if they are in this domain, or how the approach could be adopted by other domains.

Before starting my current role, I completed my undergraduate studies in Theoretical Physics at the University of Manchester, after which I joined the University of Cambridge for an MPhil in Scientific Computing and subsequent PhD in Physics where I worked on crystal structure prediction for battery materials. I then joined UCLouvain in Belgium for a postdoc and research fellowship, before making my way back to Cambridge in 2025 as a Leverhulme Trust Early Career Research Fellow.