Assistant professor at the Centre for Computational Biology (CBIO) of MINES ParisTech and Institut Curie (since 2013). Recipient of an ANR Young Researcher grant (2019-2021) and member of an H2020 Initial Training Network (2019- 2022). Instructor at Open Classrooms. Co-founder of Paris Women in Machine Learning and Data Science.
Topics of interest
learning, statistical genetics, genomics, precision medicine
Project in Prairie
Chloé-Agathe Azencott will address feature selection in high-dimensional, heterogeneous data, with applications to biomarker discovery from multi-omics data. She will most notably focus on using biological networks both to constrain the feature selection problem and to facilitate the integration of heterogeneous datatypes. She will teach courses on high-dimensional machine learning, as well as courses with a focus on omics data.
Many of the molecular data sets collected in the context of precision medicine and health pose statistical and machine learning challenges that are very different from those encountered in most artificial intelligence applications. Indeed, we are facing a setting where data are scarce and high-dimensional – there are orders of magnitudes more nucleotides in a human genome than patients suffering from a specific disease. This is therefore an exciting field providing us with many open problems and challenges.