Vahideh Reshadat

The intrinsic geometry of a developing embryo

Team: Marseille Machine Learning Team (LIS - CENTURI)

Her background

November 2019 - present | CENTURI PhD student

2009 - 2012 | MSc. in Software Engineering - Shabestar Branch of Islamic Azad University (Shabestar, Iran)

2006 - 2009 | BSc in Software Engineering - Hashtrood Branch of Payame Noor University (Hashtrood, Iran)

About her PhD project

A developing embryo such asthe Drosophila melanogaster is a complex system involving dynamics at multiple scales, from single molecules, to cells, to tissues, to organs. The recent years have witnessed a tremendous development in measurement techniques for developmental biology, from in toto imaging to single cell RNA sequencing. Each of these measurement techniques brings out unique features of a developing embryo (cell differentiation, morphogenesis, ..) leading to the generation of large amounts of heterogeneous data. There is therefore a need to integrate them into a common representation to extract and understand correlations between multiple scales. On the other hand, machine learning methods such as manifold learning or deep learning offer unprecedented ways of fusing heterogeneous data by learning mappings between high dimensional spaces from empirical data, therefore opening new ways for quantitative integrative developmental biology.

 
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