Evaluate effect of chemical compounds on plants using statistics and machine learning

To understand the effects of various substances on plants in terms of yield and disease severity (phytopathology), we need to evaluate both statistical significance and biological relevance when conducting biological experiments. Biological relevance refers to the nature and size of biological changes or differences seen in studies that would be considered relevant, while Statistically significance […]

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Non-convex learning with stochastic algorithms

In recent years, deep learning has led to unprecedented advances in a wide range of applications including natural language processing, reinforcement learning, and speech recognition. Despite the abundance of empirical evidence highlighting the success of neural networks, the theoretical properties of deep learning remain poorly understood and have been a subject of active investigation. One […]

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Modelling the Dependence between Loss Frequency and Loss Rate

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 banks’ risk management. In this project, we analyze and model the dependence between loss frequency and […]

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Development of a model for computational sea ice monitoring – Year Two

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 […]

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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 […]

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Combining deep learning neural networks and spatiotemporal models for prediction and inference of residential house prices for property assessment

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. This approach relies on third party cost […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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