Marie Piraud

Head of the AI consultant team at Helmholtz AI, Helmholtz Munich
Address: Helmholtz AI Central Unit, Ingolstädter Landstraße 1, D-85764 Oberschleißheim, Germany

& Guest researcher at the Technical University of Munich
Chair for Computer Aided Medical Procedures & Augmented Reality

News

October 2022: Work anniversary. After growing the AI consultant team to full capacity over the last 2 years, we are proud to share that we underwent a successful evaluation, and the external review panel strongly recommended the continuation of the platform. Indeed, my team carried out more than 80 consultancy projects (between 2 weeks and 6 months). We already contributed to 5 published and 12 submitted peer-reviewed papers, and three of our collaborators successfully applied for research grants. The feedback from our collaborators is extremely positive.

AI consulting

At Helmholtz AI, we are pioneering novel research models in Academia. As AI consultants, we have the mission to empower health researchers from the Helmholtz Association with artificial intelligence. We develop tailored models and machine learning solutions, to unveil new insights from our collaborators' data or streamline their daily work. We strive to translate those solutions into open source software, and provide postgraduate trainings, thereby democratizing AI for the global research community. In order to better consider the societal impact of the technologies we develop, we are increasingly encompassing both ethical and ecological considerations in our activities. This is a very fullfilling endeavour, both intellectually, thanks to the variety of problems we address, and personally, as we are enabling world-class biomedical research with AI.

Own research interests

I was trained as a theoretical physicist, and have always enjoyed working at the frontier with other research fields. I am fascinated by complex and multi-scale systems, whose understanding relies on analytical and numerical models, as well as experimental observations. I initially studied complex quantum systems, and I am now exporting those ideas and competences to biological and biomedical problems, for which observational data is increasingly available. There, bridging the gap between small-scale biophysical models and medical observations at the scale of an individual or a population is of high interest. To this aim, I am using the traditional framework of statistical inference as well as developing new methods combining model-based approaches and data-based machine learning techniques. Indeed, joining forces of the previous knowledge acquired on the underlying processes with powerful techniques of artificial intelligence is, in my opinion, a very exciting and meaningful avenue to better predictions.

I am also personally interested in understanding and improving the CO2 footprint, and the ecological impact of AI algorithms and their development, and I am looking for collaboration partners.



Updated 2022