Data-Driven Inverse Optimization with Incomplete Information

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In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

In this manuscript we consider the problem of kernel classification. While worst-case bounds on the decay rate of the prediction error with the number of samples are known for some classifiers, they often fail to accurately describe the learning curves of ...

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model a ...

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method woul ...

A correct representation of the lightning current is crucial when the electromagnetic field radiated to a point of interest has to be computed. Based on the engineering models of Transmission Line type, such representation involves the knowledge of the ret ...

We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

Data-Driven Inverse Optimization with Incomplete Information

Chattez avec Graph Search

Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Distributionally Robust Linear Quadratic Control

Error scaling laws for kernel classification under source and capacity conditions

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Deep learning-based galaxy image deconvolution

On the reconstruction of the attenuation function of a return-stroke current from the Fourier Transform of finite-duration measurements

TOPO-Loss for continuity-preserving crack detection using deep learning