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...
Pattern recognition algorithms have been proposed as a way to control powered lower limb prostheses, specifically for transitioning between the different pre-programmed locomotion modes of the prosthesis (e.g., level ground walking, stair ascent, etc.). However, these algorithms cannot track changes in the statistical characteristics of input signals, and do not...
The impacts of many important technologies are limited by the availability of better-performing materials. One factor limiting the ability of engineers to develop better materials is the speed at which they can search through possible formulations and processing schemes. Recently, machine learning algorithms have emerged as a possible route to...
Social media such as Twitter has risen as a powerful new communication medium for disseminating information on news, personal interests, experiences, and opinions. On social media, people talk about their lifestyle, health conditions and symptoms, search information on treatment options, and connect with people who have been through similar medical...
A central question in neuroscience is how the brain plans movements. Here, I apply neural data analysis and machine learning methods to better understand both eye and arm movement planning, in particular focusing on naturalistic settings. First, I built encoding models to investigate the factors that led to neural activity...
In this work, we explore the utility of the three main types of neural networks: feed forward, convolutional, and recurrent. While using these networks, we develop a new way to model multiagent trajectory data, explore the use of multiple activation functions for neurons at each layer of a neural network,...
Machine learning and symbolic reasoning have been two main approaches to build intelligent systems. Symbolic reasoning has been used in many applications by making use of expressive symbolic representations to encode prior knowledge, conduct complex reasoning and provide explanations. Recently, machine learning has enabled various successful applications by learning from...
Heterogeneous materials have been emerging and playing essential roles in various engineering and scientific fields. They usually include multiple phases of materials to create unique properties that are not accessible to their homogeneous counterparts. The traditional design approach in the material science community is to use trial-and-error iteratively, which is...
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...