Business valuation deals with the estimation of a company’s value, using information from markets and the company’s financial statements. Such valuation is important when assessing mergers and acquisitions (M&A) of companies or the sale of an owner’s share in a business. Three different approaches are commonly used for business valuation: the income approach (estimating future income), the asset-based approach (valuating the current assets), and the market approach (comparing with similar businesses).
A risk-based approach to anonymization includes an assessment of the risk that an attack to reveal or uncover personal information will be realized, known as threat modelling, against the risk that an attack on the data will be successful (e.g., a re-identification). We wish to incorporate the provable guarantees of differential privacy into this assessment of risk, to produce safe data in context of the environment in which it will be used. We also need adapt the methods of statistical disclosure control to such an updated approach.
This project will explore the non-invasive ways to find potential leaks in buried gas distribution pipelines using sound propagation. When there is a sound source at one point of the pipeline, the nature of the sound coming to another point of the pipeline will depend on the properties of the surrounding soil, properties of the pipe and its integrity. We will study the mechanics of sound propagation in a buried pipeline surrounded by soil, using methods of modern mechanics. We will also use similar methods to formulate best practices of data analysis.
The fixed-income market consists of government and corporate bonds and other debt instruments which are used to finance operations and capital investments. The bond market remains heavily reliant on exchanges of information between counterparties and as a result information on prices is decentralized and market participants operate with different levels of information. The objective of this research project is to create improved Artificial Intelligence models which will allow market participants to better manage trading activities, manage risk, or make portfolio funding allocations.
The goal of this research is to use data from casino player tracking systems to build a model for how players move around on a casino slot floor. We will use this model to perform simulations of this same movement. Segmentation of players into groups of similar value and/or characteristics will help to reduce the computation complexity of this task. Therefore, the student will also aim to devise a method of player segmentation.
The world we live in is ripe with symmetry. From the bilateral symmetry we see in humans to the symmetries which are used to describe fundamental particles in physics. Most modern machine learning methods however do not have an inherent modeling of symmetry in them. By developing algorithms which do have an explicit modeling of symmetry we can decrease the amount we need to teach these algorithms, making them much cheaper to create. We propose a network that can compare images in such a way that it is not affected by changing the orientation of objects in the image.
Property valuation is a crucial economic service that is used by local governments for the distribution of property taxes in order to fund local services. The current and widely adopted cost approach to valuation is based on estimating the land value and the depreciated cost of the building.
The proposed research project focuses on the development of a novel model for the computation of sea ice parameters in near real- time relying on satellite data. The interdisciplinary team will investigate solutions for high performance computing to monitor sea ice and calculate ice parameters with the high spatial resolution. This project includes R&D activities in sea ice modeling, calculating parameters of ocean interaction with sea ice and designing algorithms for satellite data processing and analysis.
Deep neural networks are effective at image classification and other types of predictive tasks, achieving higher accuracy than conventional machine learning methods. However, unlike these other methods, the predictions are less interpretable. While accuracy may be enough for applications where errors are not costly, for real world applications, we want to also know when the predictions are more likely to be correct. Estimating the likelihood that a prediction is correct is called confidence, or uncertainty.
Lending to various companies and individuals is a core business of banks. This lending activity comes with credit risk, namely the risk that some borrowers default and fail to make required payments. Estimating credit risk accurately is important for banksâ risk management. In this project, we analyze and model the dependence between loss frequency and loss rate of defaulting customers. The reason for the dependence comes from the underlying economic cycle: in an economic downturn, losses occur both more frequently and more severely than in an economic boom.