Interpretable and Scalable Data­Driven Models in Financial Time Series Analysis

The whole project aims to apply Bayesian non-parametrics (BNP) to establish a scalable and transparent framework for financial time series analysis in big data age and provide new insights into the development of interpretable data-driven market risk management. The project is supported by First Venture Funding from the Universityf of Edinburgh Business School.

Identification of Human Mobility Modes using Socio-Spatio-Temporal Predictive Models

The scientific analysis of the regularities observed in individual and collective human movement trajectories is of fundamental relevance to a wide range of areas urban planning, the prevention of epidemics, and natural security issues such as detection of clandestine activity, to name but a few. The ubiquity of mobile phones and location-based social media has enabled the capture of comprehensive, time-resolved individual information, offering a unique opportunity to observe human activity on an unprecedented scale. Indeed, recent theoretical developments suggest that a perfect algorithm can predict a person's whereabouts with almost 90% certainty, given past observations of their location visits. Yet, major gaps remain in our understanding of human mobility dynamics.

EthicalML: Injecting Ethical and Legal Constraints into Machine Learning Models

Our choice as to which movies to watch or novels to read can be influenced by suggestions made by machine learning (ML)-based recommender systems. However, there are some important scenarios where ML systems are deficient. Each of the following scenarios involves a situation where we wish to train an ML system so that it delivers a service. In each case, however, there is an important constraint that must be imposed on the operation of the ML system.