Abstract: 

As humans we can never fully observe the world around us and yet we are able to build remarkably useful models of it from our limited sensory data. Machine learning systems are often required to operate in a similar setup, that is the one of inferring unobserved information from the observed one. Partial observations entail data uncertainty, which may hinder the quality of the model predictions. In this talk, we will discuss two strategies to mitigate this problem: (1) leveraging the complementarity of different data modalities, and (2) actively acquiring additional information from the same data modality.

Bio: 

Adriana Romero Soriano is a research scientist at Facebook AI Research and an adjunct professor at McGill University. Her research focuses on developing models and algorithms that are able to learn from multi-modal data, reason about conceptual relations, and leverage active acquisition strategies to mitigate their uncertainties. The playground of her research has been defined by problems which require inferring full observations from limited sensory data. She completed her postdoctoral studies at Mila, where she was advised by Prof. Yoshua Bengio. Her postdoctoral research revolved around deep learning techniques to tackle biomedical challenges, such as the ones posed by multi-modal data, high dimensional data and graph structured data. She received her Ph.D. from University of Barcelona in 2015 with a thesis on assisting the training of deep neural networks, advised by Dr. Carlo Gatta.