Talk Title: “Interpretable deep learning for cancer personalized medicine”<\/p>\n
Dr. Mar\u00eda Rodr\u03afguez Mart\u03afnez is the Technical Leader of Systems Biology at IBM Research Europe (Switzerland) and an associated member of the Department of Biology at ETH since 2014. A theoretical physicist by training, she became interested in the development of computational and statistical approaches to unravel cancer molecular mechanisms using high-throughput multi-omics datasets and single-cell molecular data. In recent years, her team has specialized in the development of AI approaches for personalized drug modeling. More recently, she is building multi-scale models of the immune system through a combination of deep learning and mechanistic models. Through this effort, her team has developed deep learning models to predict the specificity of T cell receptors and stochastic mechanistic models to recapitulate B cell development.<\/p>\n
She is also quite active in the area of interpretable deep learning. Deep learning has achieved astounding performances in a broad range of disciplines, but breakthrough performances have often come at the price of a lack of information about the rules that govern a model\u2019s decision. Interpretable deep learning aims to develop models that can not only make a prediction with high accuracy, but can also provide insight into the reasons underlying the prediction. On this area, her team has contributed several novel methods for different applications in computational biology, ranging from AI-driven protein modeling to the integration of image and RNA-Seq data modalities.<\/p>\n","protected":false},"excerpt":{"rendered":"