I am a biomedical engineer, physicist, data scientist and lung physiologist; teacher and mentor.

I develop quantitative functional Magnetic Resonance Imaging (MRI) techniques to understand the human lung in health and disease, in particular the spatial distribution of ventilation, perfusion and the ventilation-perfusion ratio. Translating techniques into clinical research, early diagnosis and management of respiratory disease (asthma, in particular) and optimized targeting of aerosolized drugs.

I develop and validate image & signal processing algorithms and software (using artificial neural networks/machine learning, wavelet analysis, …). I have considerable expertise in multimodal integration (combining MRI and gamma scintigraphy, combining multiple MRI modalities). Gatekeeper of UCSD's Pulmonary Imaging Lab toolbox for image processing and quantification of lung MRI.

I am an expert in human lung physiology in space (respiratory mechanics, inhaled Lunar/Martian dust) and on the impact of low gravity in the cardiovascular system.

I spent two years at NIH / NIBIB establishing and managing NIBIB's component of the Medical Imaging and Data Resource Center (https://www.midrc.org) and other medical imaging and clinical data repositories to support Artificial Intelligence / Machine Learning (AI/ML) ecosystems, by providing large, curated, high quality medical imaging data and associate clinical data. Implementing the F.A.I.R. principle in data science: Findable, Accessible, Interoperable and Reproducible. I spend quite some time focusing on the "I" in F.A.I.R. (Interoperability).