Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 17 of 17 for “"Data Scientist"”.
-
A system for storage and analysis of machine learning operations
Data scientists go through an iterative process when building machine learning models. This process includes operations like feature selection, cross validation, model fitting, and evaluation, which are repeated until a sufficiently accurate model is produced. This thesis describes ModelDB Server …
-
Designing a Domain-Specific Accelerator for Graph Pattern Mining
Graph pattern mining (GPM) is used in a variety of domains such as bioinformatics, e-commerce and social sciences. GPM is a computationally intensive problem with an enormous amount of coarse-grain parallelism and therefore, attractive for hardware acceleration. Unfortunately, existing GPM …
-
Coordination of lower limb movement utilizing the agonist-antagonist myoneural interface
The agonist-antagonist myoneural interface is a novel surgical construct that shows promise as a method of providing persons with amputation proprioceptive sensation of movement and force. This thesis aims to quantify the volitional coordination capabilities of the agonist- antagonist myoneural …
-
Body driven cognition : writing to the body to influence the mind
To build effective HCI interventions on cognitive processes, we must build off of updated and inclusive cognitive models. Recent research in psychology distinguishes levels of consciousness into a tripartite model - conscious, unconscious, and meta-conscious. HCI technologies largely focus on the …
-
Scaling collaborative open data science
Large-scale, collaborative, open data science projects have the potential to address important societal problems using the tools of predictive machine learning. However, no suitable framework exists to develop such projects collaboratively and openly, at scale. In this thesis, I discuss the …
-
Visualizations for model tracking and predictions in machine learning
… is often an exploratory and iterative process. A data scientist frequently builds and trains hundreds of models with different parameters and feature sets in order to find one that meets the desired criteria. However, it can be difficult to keep track of all the parameters and metadata that are …
-
Big data : evolution, components, challenges and opportunities
… the evolution and current state of the "Big Data" industry, and to understand the key components, challenges and opportunities of Big Data and analytics face in today business environment, this is analyzed in seven dimensions: Historical Background. The historical evolution and milestones in …
-
A systematic approach for architecture-level energy estimation of accelerator designs
… continue bringing energy eciency improvements to data and computation-intensive applications. To enable the fast exploration of the accelerator design space, architecture-level energy estimators, which perform energy estimations without requiring complete hardware description of the designs, are …
-
Deep learning for distributed circuit design
In this thesis, we present deep learning models for designing distributed circuits. Today, designing distributed circuits is a slow process that can take months from an expert engineer. Our model both automates and speeds up the process. The model learns to simulate the electromagnetic (EM) …
-
Few-shot text classification with distributional signatures
… by a significant margin across six benchmark datasets (20.0% on average in 1-shot classification).
-
Wonderland : constructionist science learning in mixed reality
Science concepts lie at the heart of our everyday experiences, yet people feel disconnected from science because of the abstract way it is taught in schools. We wanted people to learn science concepts in the real world in playful ways, and used Mixed Reality (MR) to allow people to visualize and …
-
On the equivalence of sparse statistical problems
… algorithms for SLR. Experiments on simulated data show that these algorithms perform well.
-
Informing Therapeutic Approaches For Infectious Disease And Heart Disease, Focusing On Cell-Free Synthesis Of Bacteriophages And Calcium Transport By Serca
… on the computational side, working as a Data Scientist for the National Institute of Allergy and Infectious Disease, where my new position begins in September.
-
Mixed-precision NN accelerator with neural-hardware architecture search
Neural architecture and hardware architecture co-design is an effective way to enable specialization and acceleration for deep neural networks (DNNs). The design space and its exploration methodology impact efficiency and productivity. However, both architecture designs are challenging. We first …
-
Evolutionary deep learning
… that automatically determines whether a given data science problem is of classification or regression type, successfully choosing the correct problem type with more than 95% accuracy. Together these algorithms show that a great deal of the current "art" in the design of deep learning networks - …
-
Exploring Data Science at Institutions of Higher Education: Competencies, Skills, Proficiencies, and Professional Experiences
… proficiencies, and professional experiences of data scientists at higher education institutions. The specific population of interest was higher education administrators and staff professionals identified as data scientists. This study was informed by the following guiding research questions: (1) …
-
Artificial Intelligence in Labor Market Matching
… non-technical workers can use AI to upskill into data science, however those skills do not persist in absence of AI assistance. My first chapter investigates the association between writing quality in resumes for new labor market entrants and whether they are ultimately hired. I show this …