Collective cell migration is a commonly recurring theme in biology and medicine, with examples in developmental biology, wound healing, and disease. For example, in solid tumours, new blood vessels are created by the process of angiogenesis and these then supply the tumour with nutrients and also a way to metastasize. In developmental biology, neural crest cells migrate long distances to form structures, such as the lining of the gut, cranial features etc. I will survey a number of mathematical models that have been proposed for these phenomena and describe how they have been used to generate new biological insights.

Watching basketball is nearly the same as watching repeating coin tossings! By analyzing recently available data from recent National Basketball Association (NBA) basketball seasons, basketball scoring during a game is well described by a continuous-time anti-persistent random walk, with essentially no temporal correlations between successive scoring events. We show how to calibrate this model to account for many statistical season-long metrics of NBA basketball. As further fillustrations of this random-walk picture, we show that the distribution of times when the last lead change occurs and the distribution of times when the score difference is maximal are both given by the celebrated arcsine law--—a beautiful and surprising property of random walks. We also use the random-walk picture to construct the criterion for when a lead of a specified size is "safe" as a function of the time remaining in the game. The obvious application to game-time betting is left as an exercise for the interested.

Natural Language Processing (NLP) has seen an explosion of interest in recent years, with many industrial applications relying on key technological developments in the field. However, Natural Language Understanding (NLU) - which requires the machine to get beyond processing strings and involves a semantic level - is particularly challenging due to the pervasive ambiguity of language. In this talk I will first introduce NLP and NLU and the key issues in the field, and will then move on to present recent research in my group on multilingual NLU, including work on BabelNet, our multilingual encyclopedic dictionary, and tasks such as multilingual word sense disambiguation and semantic role labeling. The key goal we aim at is to scale across languages easily and achieve state-of-the-art performance thanks to the integration of deep learning and explicit knowledge. I will also show several demos and discuss the technological transfer to Babelscape, a successful Sapienza startup company which brings to the market innovative tools for multilingual concept and entity extraction, enterprise knowledge graph creation and multilingual semantic search.