Functional electronic materials are difficult to design due to the complex interplay among chemistry, atomic structure, and electrical properties. This dilemma is further amplified in transition metal compounds which can defy the band-theory description of non-correlated electrons. Exploring the vast possible design space completely with experiments or first-principles simulations is...
Biology is entering the exciting world of big data. Modern high-throughput experimental techniques often produce large datasets that aim to capture complex relationships often found in biological systems. While these larger data sets contain vast amounts of useful information, the answers are often locked behind a wall of numbers. As...
Unstructured data like text is plentiful and possibly contains valuable insights leading to a better decision-making process. Manually obtaining these insights can be costly and time-consuming. Text mining, also known as Text analytics, is developed to derive meaningful information from textual data. It is widely applied in various domains such...
This dissertation uses several interrelated methods derived from corpus linguistics, statistics, and machine learning to infer a number of historically significant voice-leading schemas in a corpus of eighteenth-century Neapolitan solfeggi (exercises for voice with bass accompaniment). The goal of this work is to gain insights not only into the characteristics...
Moving away from fossil fuels requires environmentally friendly and economically viable alternative energy sources. A wide adoption of new technologies for energy production and storage depends on better performing materials. Computational methods, such as electronic structure calculations and machine learning, hold the promise to work in conjunction with traditional experimentation...
Machine learning has been widely applied to solve intricate problems in finance. Yet in options theory, machine learning methods are less visited due to the structural complexity of the derivatives market. This dissertation focuses on using machine learning algorithms to obtain optimal decisions for three distinct option-related problems. In the...
DNA methylation in repetitive elements (RE) suppresses their mobility and maintains genomic stability, and decreases in it are frequently observed in tumor and/or surrogate tissues. Averaging methylation across RE in the genome is widely used to quantify global methylation. Methylation of RE in humans is considered a surrogate for global...