On June 30, 2021, diiP hosted the Interdisciplinary Projects Day, where all 16 projects got together and showed their progress.

It had been 6 months since the 16 diiP projects have started their work. The two diiP research associates have started their work on the three selected Strategic projects. The Masters-level interns have also been recruited, and integrated in the interdisciplinary teams, which allowed them to introduce their project’s progress during this Interdisciplinary Projects Day.

Below are the posters presented during the event, showcasing the progress made and the main questions the project has so far responded to.

Strategic projects

Digital Pathology: when AI meets with anatomo-pathology
PARKER — Planetary lidAR seeKing for lifE signatuRe
Autoimmunity/inflammation Through RNAseq Analysis at the single Cell level for Therapeutic Innovation – ATRACTion

Masters projects

Inferring cultural transmission of reproductive success through machine learning methods
Large image time series analysis for updating vineyard geographic databases
Smoothing of incomplete air pollution regions of interest from satellite observations
Combining visual and textual informations for enhancing image retrieval systems in radiological practices
Automatic production of environmental indicators from freely available remote sensing data: from a global to a local scale
Machine learning model of volcanic lava properties helps understanding the dynamics of volcanic eruptions
ComplexNeuroViz: Complexity Visualisation for Neural Machine Translation
Machine learning for the study of EEG data recorded during general anesthesia
Influence of blood pressure and aqueous humor dynamics on the response to glaucoma medication: a data-driven computational study
Machine Learning techniques applied to eye movement analysis for early screening of learning disorders in young children
Artificial Intelligence for source deblending in the next generation of astrophysical big data imaging surveys – Combining Euclid and LSST
Malvasia - MAchine Learning to VAlue Single Interferometer Analysis

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