Recent advances in computing power have facilitated the use of computational simulations as design guidelines in a range of fields including the semiconductor industry, biosensors, microfluidic devices, and even nano-sized ...

In this dissertation I argue that Machado de Assis's short stories represent an important forum for the articulation and understanding of concepts of race in modern Brazil. I substantiate that claim by analyzing ten of his ...

“Does bigger data lead to better decisions?” has been a frequent question for discussion among many decision makers—data scientists as well as organizational leaders and managers. Educational institutions, finance, and the ...

Classical planning techniques have some serious problems when employed in real-world domains. In classical planning, it is assumed we know the current state of the world and can project that state through a reasonably ...

Accurate star-galaxy classification has many important applications in modern precision cosmology. However, a vast number of faint sources that are detected in the current and next-generation ground-based surveys may be ...

Enhancing building energy efficiency is one of the best strategies to reduce energy consumption and associated CO2 emissions. Recent studies emphasized the importance of occupant behavior as a key means of enhancing building ...

In this thesis, we show how to deploy machine learning techniques such as Gaussian process regression to approximate the European basket option prices. For the underlying asset of European basket option, we assume it follows ...

This thesis presents three distinct machine learning algorithms based on the mathematical formalism and physical idea of diffusion processes. First, the idea of using heat diffusion on a hypersphere to measure similarity ...

We follow a similar approach and use a classifier learning system to generate high performance libraries for matrix-matrix multiplication. Our library generator produces matrix multiplication routines that use recursive ...

Many state-of-the-art deep learning models rely on dynamic computation logic, making them difficult to optimize. In this thesis, we present a hashing based algorithm that is able to detect and optimize computation logic ...