Tomaso Aste is professor of complexity science at UCL Computer Science Department. A trained Physicist, has substantially contributed to research in complex systems modeling and complex data analytics. His research activity concerns predictive analytics by using network theoretic and statistical physics tools. He is passionate in the application of Blockchain Technologies to domains beyond digital currencies. He is Scientific Director of the UCL Centre for Blockchain Technologies; Head of the Financial Computing and Analytics Group; Programme Director of the MSc in Financial Risk Management; Vice-Director of the Centre for doctoral Training in Financial Computing & Analytics; Member of the Board of the ESRC LSE-UCL Systemic Risk Centre. Prior to UCL he held positions in UK and Australia. He is consulting for financial institutions, banks and digital-economy startups.
Predictive modeling for a complex world: a data-driven perspective
We all experience complexity in everyday life where simple answers are hard to find and the consequences of our actions are difficult to predict. Understanding and modeling the complex nature of markets, peoples and societies have become a crucial scientific challenge with great practical impact. The current big-data revolution has provided unprecedented access to large amount of data for modeling and forecasting complex systems. However, analyzing, understanding, filtering and making use of such a large amount of data have also become a challenge in itself.
I will present methodologies based on the combination network theory, statistical physics, data science, multiscale analysis and computational methods to unwind complexity and produce robust and meaningful models that are capable to make reliable predictions.
