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,...
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...
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...
In recent decades, metal additive manufacturing has seen rapid advancements, offering promising applications across various industries. However, addressing existing challenges in metal AM, such as process stability, defect avoidance, and quality control, is essential for fully exploiting its potential in fabricating parts with a desired geometry, as well as tailored...