A reconciliation of the top-down and bottom-up approaches to risk capital allocations

Two overarching approaches to allocate the aggregate risk capital stand out nowadays. These are the top-down approach that entails that the allocation exercise is imposed by the corporate centre, and the bottom-up approach that implies that the allocation of the aggregate risk to business units is informed by these units. Briefly, the top-down allocations start with the aggregate risk capital that is then replenished among business units according to the views of the centre, thus limiting the inputs from the business units.

Risk assessment and population viability for the critically endangered Taiwanese white dolphin

The Taiwanese white dolphin is a subspecies only found in Taiwan. Since 2008 it has been listed as Critically Endangered in the Red List of Threatened Species of the International Union for Conservation of Nature (IUCN), meaning that it is globally recognized as facing an extremely high risk of extinction. The dolphins face a plethora of human human threats, such as fisheries mortality and habitat degradation. This research aims understand how different threats may impact the persistence of this small population, both temporally and spatially.

Graph-based learning and inference: models and algorithms

Learning from relational data is crucial for modeling the processes found in many application domains ranging from computational biology to social networks. In this project, we propose to work on developing modeling techniques that combine the advantages of the approaches found in two fields of study: Machine Learning (through graph neural networks) and Statistical Learning (through statistical relational learning methods).

Development of Smart Analytics Software for Remote Water Quality Assessment

The management of quality of drinking water systems, river and lakes is a significant environmental challenge. In this research project, we plan to develop low-cost real-time water quality monitoring and analytics software, which can be used to analyze and predict water quality in remote lakes, rivers, drinking water plants and other water bodies. The Aquahive remote water monitoring system developed by the Aquatic Life Ltd., a Canadian company, will be deployed to capture the physical, chemical, and biological characteristics of the water quality in real-time.

Machine Learning in Business Valuation Using Merger and Acquisition Data

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).

Privacy Guarantees and Risk Identification: Statistical Framework and Methodology

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.

Variational methods for pipeline safety and data analysis

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.

Bond Pricing AI Improvement

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.

Modeling and simulation methods for assessing casino player behaviour

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.

Equivariant Siamese Neural Networks

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.