Hydraulic fracturing involves the injection of large volumes of water at significant pressure to break the low permeability shale rock. The goal is to create flowpaths which are then partially filled with proppant (sand) to ensure the fractures remain open to allow for oil and gas production. This process results in small releases of energy (earthquakes) which are termed "microseismic events". Ideally, these mini-earthquakes can be used as passive sources to enable imaging of the subsurface region. Operators would then be able to make decisions about future stimulation procedures and to determine reservoir reserves. Unfortunately, the microseismic source location is highly uncertain. Our goal of this project is to understand this uncertainty in the context of full waveform inversion.
The prompt detection and forecasting of infectious diseases with rapid transmission and high virulence are critical in the effective defense against these diseases. Despite many promising approaches in modern surveillance methodology, the lack of observations for near real-time forecasting is still the key challenge obstructing operational disease prediction and control. In contrast, novel non-traditional data sources, such as social media, create a new momentum for real-time epidemiological forecasting and have potential to revolutionize modern bio-surveillance capabilities by predicting an event (e.g. infectious outbreak) before its typical manifestation and before patient-healthcare interaction.
Nowadays, especially in view of the recent Ebola outbreak, it is broadly recognized that the cross disciplinary nature of infectious diseases modeling and forecasting does not fit within a single discipline. This implies an ever increasing need for an interdisciplinary education in applied mathematics, statistics, information systems and epidemiology. This project seeks to fill this gap between the disciplines by providing joint graduate supervision and supporting novel, highly interdisciplinary research projects at the interface of infectious disease surveillance and forecasting, applied statistics, environmental sciences and social network analysis.