Advancing DNA Data Storage with Computational Innovation

I have a very multidisciplinary background consisting of Biochemistry, Bioinformatics, and Computer Science, this has allowed me to build a strong foundation at the intersection of life sciences and computational technologies, enabling me to approach biomedical challenges from multiple angles.

My previous research has focused on applying machine learning and artificial intelligence to medical and biomedical fields. I have worked extensively on computer-aided diagnosis, combining computer vision, machine learning, and deep learning to enhance diagnostic accuracy and support clinical decision-making. Through this work, I’ve developed methods that help make sense of complex biomedical data, contributing to tools that streamline and improve diagnostic processes.

Recently I have joined EnDROIDS team where I’m focused on the computer-aided design of DNA and RNA origami nanotechnology for DNA data storage. In particular I investigate DNA/RNA origami as a platform for molecular data storage. Using key algorithms, I optimise and adapt designs to meet different application requirements. Additionally, I’m developing a suite of web applications powered by large language models to support researchers in experimental protocol development. These tools aim to help researchers gain insights into experimental landscapes and make more informed decisions in their protocol design.