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...
Connecting structure and function in nanoscale engineered materials and devices relies on the analysis of the fundamental arrangement of matter, frequently under dynamic conditions. The demand to image structures at fundamental length scales has touched inorganic materials, biology, and frequently hybrid hard/soft materials with unique phenomena driven by heterogeneous components....
The theory of how humans and machines control and communicate with each other is at the core of the scientific field known as Human-Robot Interaction (HRI). Researchers in this sub-discipline of robotics are therefore particularly interested in developing methods to chuppahreduce the inherent friction in this communication and control channel....
Annual age-adjusted breast cancer incidence rates in the United States have been static for decades. More recently, the development of massively parallel, high throughput DNA sequencing has enabled the cataloging of somatic mutations in cancer. Mutations are non-random and occur within sequence motifs. These motifs provide us with evidence to...
Connected and automated vehicle (CAV) technology is a disruptive transportation development with potentially transformative impacts on society and the economy. CAV systems promise to significantly reduce human-caused road crashes, improve traffic flow performance, and lower pollutant emissions. However, realizing those benefits requires strategic planning for the deployment of CAV systems...
Soft materials such as colloids and polymers often exhibit a variety of mesoscopic structures that are governed merely by weak physical interactions. Due to these intermediate structures, they can be easily taken out of thermal equilibrium by introducing external stimuli such as a shear flow and electromagnetic fields. This thesis...
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...
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 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...
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...
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...
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...
Mammalian transcriptional regulation is well-known to be complex and highly context dependent. Different genetic and epigenetic features, including single nucleotide polymorphisms (SNPs) that function as cis- or trans-expression quantitative trait loci (eQTLs), transcription factor (TF) interaction profile with cis-regulatory elements (CREs), methylation of CpG dinucleotide sequences, and histone modification that...
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...
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...
Recently, machine learning and deep learning, which have made many theoretical and empir- ical breakthroughs and is widely applied in various fields, attract a great number of researchers and practitioners. They have become one of the most popular research directions and plays a sig- nificant role in many fields, such...
Automated driving has become a very popular topic in the recent years and is becoming more and more of a reality. In this new trend, High Definition (HD) maps play an important role in many ways that will provide a safer and more efficient driving experience, especially in terms of...
The ability to control the crystalline ordering and morphology of polymeric nanomaterials is a grand challenge in the field of materials science, which could enable the development of functional materials able to solve long-standing problems in renewable energy and medicine. In this work, we explore a combination of supramolecular chemistry...
Breakthroughs in large-scale biological data collection have resulted in a wealth of -omics (genomics, metabolomics, etc.) datasets in the literature. However, the development of appropriate computational techniques for their analysis is lacking, yet crucial for fully extracting the rich information contained in these datasets. The work in this dissertation describes...
Cancer has long been the second-leading cause of death in the United States and represents the leading cause of death in midlife (age 40-60). While the prognosis for many cancers has vastly improved over the last thirty years, many cancers remain elusive due to the late-onset of symptoms, the specific...
Recent developments have enabled L12-strengthened Co-based superalloys, which have thepotential to surpass Ni-based superalloys as the material of choice for the hottest sections of turbine
blades due to cobalt’s 40 ºC higher melting point. The most-studied branch of Co-based
superalloys are based on the L12 phase Co3(Al,W); however, there is...
Machine learning is seeping into every fabric in various practical domains such as autonomous driving, wearable computing, and smart buildings. However, in the actual development and integration, especially when the learning-based components are frequently included as components of large complex systems where the physical instances can be included as interactable...
Technology that processes text, audio and video, as well as location data, has revolutionized many industries by enabling innovative operations for customer retention. To retain transactions for a platform and viewers for advertisers, this dissertation leverages novel digital tools to analyze consumer behavior, proposes original economic frameworks to guide platform...
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...
Bacterial infections (BI) are a frequent, expensive, and life-threatening condition for critically ill patients. For patients with serious BI, minimizing the time between admission to the
intensive care unit (ICU) and administration of appropriate antibiotic therapy is crucial to
improve prognosis. However, the current gold-standard for identifying the appropriate
antimicrobials...