Speaker
Description
As physicists, we know that experiments can produce massive amounts of data. However, nowadays, we collect information from many different sources. We store different kinds of data points when we run simulations. Experiments do save data beyond what they are designed to provide (for example, metadata or instrument data). And, in complex system science, data comes from everywhere, from social networks to climatological data. Data science approaches can help generate new opportunities and exploit the vast amount of data the research produces. In this seminar, I will present some examples that go from how machine learning explanations can be used to better understand simulations to an augmented reality app that allows us to experiment and explore the human role in segregation.
