Data Science and related fields like Artificial Intelligence, Machine Learning, and Statistics provide indispensable research methods for understanding a wide variety of phenomena from large datasets. However, as methodical and empirical as these methods aim to be, there are many subjective and discretionary choices that the data scientist must make...
Stroke affects millions of people each year and although modern medicine has improved chances of survival after stroke, it has not yet been able to affect a change in repairing damaged neural tissue leaving one to two-thirds of survivors with chronic disability in their affected upper-extremity; specifically, hemiparesis, hypertonicity, loss...
Zebrafish (Danio rerio) are heavily studied because they share a similar genetic structure to humans. The skin patterns of zebrafish are comprised of horizontal stripes of different colored pigment cells. Accurately quantifying the cell size of various pigment cells in relation to their location on the skin is a crucial...
Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it to alternating current in the 100 kHz to 800 kHz frequency range and heating it until it is destroyed via coagulative necrosis. Ablation treatment is gaining momentum especially in cancer research, where the undesired...
While optimization has received much attention in the machine learning community, most of them consider unconstrained supervised learning models such as neural networks and support vector machine. In this dissertation, we introduce a new class of optimization problems called scale invariant problems that include interesting unsupervised learning models such as...
This thesis focuses on applications of recurrent neural networks (RNNs) for three aspects of sequential classification. In the first chapter, a novel method to generate synthetic minority data generation to improve imbalanced classification is discussed. Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic...
Deep neural networks have achieved remarkable success in the past decade on tasks that were out of reach prior to the era of deep learning. Amongst the myriad reasons for these successes are powerful computational resources, large datasets, new optimization algorithms, and modern architecture designs. Most of the reasons are...
The study of employee engagement and its consequences in the workplace has gained traction in the business world over the past decade, with dramatic claims of the direct consequences of engagement including lower absenteeism, higher sales, improved productivity, and increased profitability for organizations that are more engaged (The Gallup Organization,...
The world is awash in data and much of artificial intelligence focuses on learning models of the underlying structure in this data or the mechanisms governing its evolution. Both neural and symbolic models have weaknesses that make these models sub-optimal from a use perspective. Much of this data is in...
Supervised learning model is one of the most fundamental machine learning models. It can provide powerful capability of prediction by learning complex patterns hidden in many, sometimes thousands, predictors. It can also be used as a building block of other machine learning tasks, like unsupervised learning and reinforcement learning. Such...