Event
Talk on Applying Deep Learning to Clinical Data
Location
Date
Type
Title
All that glitters is not gold: Tales from applying deep learning to clinical data
Abstract
Deep learning is gathering a lot of interest for its potential in furthering scientific research (sometimes referred to as “AI for the sciences”), and biomedical applications are no exception. However, using deep learning for clinical data presents scientific challenges. Starting from my experience in several projects, I will discuss what kind of problems deep learning does or does not solve well as of now, what challenges remain to be addressed, and what potential pitfalls to pay attention to.
Speaker
Bio
Chloé-Agathe Azencott is an associate professor of the Centre for Computational Biology (CBIO) of Mines Paris and Institut Curie (Paris, France). She earned her PhD in Computer Science at University of California, Irvine (USA) in 2010, working at the Institute for Genomics and Bioinformatics. She then spent 3 years as a Postdoctoral Researcher in the Machine Learning and Computational Biology group of the Max Planck Institutes in Tübingen (Germany) before joining CBIO.
Her research revolves around the development and application of machine learning methods for biomedical research, with particular interest for feature selection and the integration of structured information.
Cholé-Agathe Azencott is also the Co-President of the Machine Learning and Computational Systems Biology Community of Special Interest of ISCB, and the Co-Founder of the Parisian branch of Women in Machine Learning and Data Science.