A Robot Scientist is a physically implemented robotic system that applies techniques from artificial intelligence to execute cycles of automated scientific experimentation. A Robot Scientist can automatically execute cycles of: hypothesis formation, selection of efficient experiments to discriminate between hypotheses, execution of experiments using laboratory automation equipment, and analysis of results. We have developed the Robot Scientist “Adam” to investigate yeast (Saccharomyces cerevisiae) functional genomics. Adam has autonomously identified genes encoding locally “orphan” enzymes in yeast. This is the first time a machine has discovered novel scientific knowledge. To describe Adam's research we have developed an ontology and logical language. Use of these produced a formal argument involving over 10,000 different research units that relates Adam's 6.6 million biomass measurements to its conclusions. We are now developing the Robot Scientist “Eve” to automate drug screening and QS AR development.

We consider the combination of two approaches for modeling data admitting sparse representations: On the one hand, dictionary learning has proven very effective for various signal restoration and representation tasks. On the other hand, recent work on structured sparsity provides a natural framework for modeling dependencies between dictionary elements. We propose to combine these approaches to learn dictionaries embedded in a hierarchy. Experiments show that for natural image patches, learned dictionary elements organize themselves naturally in such a hierarchical structure, leading to an improved performance for restoration tasks. When applied to text documents, our method learns hierarchies of topics, thus providing a competitive alternative to probabilistic topic models. (Joint work with Rodolphe Jenatton, Julien Mairal and Guillaume Obozinski)

The discovery of differential equations from measured data has been studied in the area of machine learning under the topic of computational scientific discovery, and more specifically equation discovery, for almost two decades. The talk will describe state-of-the-art methods for discovering differential equations from measured data and domain knowledge. It will then discuss several applications in the area of ecology (modelling aquatic ecosystems) and systems biology (modelling endosome maturation).