Dr. Mihaela Duta

Principal Research Software Engineer - Service Lead

Mihaela joined OxRSE in 2020, where she leads the group's service provision and contributes to shaping research software strategy across the University. Before this, she held a Senior RSE role in the Department of Experimental Psychology, following earlier postdoctoral research positions in both Experimental Psychology and Engineering Science. Her career combines technical expertise with interdisciplinary research experience, which informs her approach to the design and development of research software across diverse fields.

Mihaela specialises in data science and applied software engineering, with particular expertise in interactive visualisation dashboards, data processing pipelines, mobile and web applications, and instrument control. She focuses on developing robust, maintainable software that supports research and enables innovation. She is also co-founder of OxEd and Assessment, a University of Oxford spin-out providing scalable educational assessment tools—work that was Highly Commended in the 2022 Vice-Chancellor’s Innovation and Engagement Awards for making a positive difference to society.

As a founding trustee of the Society of Research Software Engineering, Mihaela draws on that experience to advocate for best practices and sustainable research software development within the University of Oxford.

Past and Current Projects

OxWell: interactive tool for exploring youth mental health survey data
MAMA: mobile app for participants to the MAMA Study clinical trial
SIMS: clinical tool to generate clinically interpretable pain scores in infants.
EDC: mobile app to assist healthcare workers in diagnosing convulsive epileptic seizures, especially in resource-limited settings.
LanguageScreen: mobile app for screening children’s early language skills
OCSPlus: mobile app for cognitive screening for mild cognitive impairment
OxMET: mobile app for cognitive screening for executive dysfunction
REF 2021 Data Explorer: interactive dashboards to explore, analyse, and visualise REF 2021 research data
Local2Global: python package to infer global embeddings from local graph embeddings