|Affiliation||Ersilia Open Source Initiative|
|Research area code||(I4) Artificial intelligence|
|Fellowship Inauguration Year||2022|
My main interest is to bridge the gap between computer scientists and experimentalists, with the goal of integrating AI/ML tools into day-to-day laboratory research. I am particularly focused in underfunded areas where computer science can speed up research, such as neglected diseases.
I am trained as a molecular biologist, and completed a PhD in the field of Stem Cells and Colorectal Cancer at the IRB Barcelona. During my studies I had to rely on bioinformaticians and fellow computational pharmacologists to analyse the results of my experiments and infer new hypothesis, which raised my interest in AI/ML modelling of biology data, and I became a self-learner (mostly using Pyhton). As a result, the focus of my career shifted from the purely academic career to try and bring down the existing barriers to implement these models by non-expert scientists. In 2020, I co-founded the Ersilia Open Source Initiative, a UK non-profit organization with the mission to democratize access to AI/ML tools. My current efforts are focused into building and expanding the Ersilia Model Hub, a platform where models can be deployed in a user-friendly manner so that researchers worldwide can access them.
During the fellowship I aim to, first, improve the sustainability of the software we are creating, by interacting with other fellows and learning about best practices. Second, encourage computer scientists and software engineers to focus on deployment and usability of their tools by scientists who might not have coding skills, via conferences and workshops, and, third, offer trainings carpentries-style both for contributors and end users, to raise awareness of the tools we are creating, generate quality training material on how to use them and, of course, obtain feedback and provide tailor-made solutions to specific needs. I hope all this will contribute towards bringing the expertise of UK researchers to where it is most needed.
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