Below is a selection of dissertations from the Doctor of Philosophy in Computational and Data Sciences program in Schmid College that have been included in Chapman University Digital Commons. Additional dissertations from years prior to 2019 are available through the Leatherby Libraries' print collection or in Proquest's Dissertations and Theses database.
Dissertations from 2023
Computational Analysis of Antibody Binding Mechanisms to the Omicron RBD of SARS-CoV-2 Spike Protein: Identification of Epitopes and Hotspots for Developing Effective Therapeutic Strategies, Mohammed Alshahrani
Voluntary Action and Conscious Intention, Jake Gavenas
Random Variable Spaces: Mathematical Properties and an Extension to Programming Computable Functions, Mohammed Kurd-Misto
Application of Machine Learning Algorithms for Elucidation of Biological Networks from Time Series Gene Expression Data, Krupa Nagori
Causal Inference and Machine Learning Methods in Parkinson's Disease Data Analysis, Albert Pierce
Causal Inference Methods for Estimation of Survival and General Health Status Measures of Alzheimer’s Disease Patients, Ehsan Yaghmaei
Dissertations from 2022
Computational Approaches to Facilitate Automated Interchange between Music and Art, Rao Hamza Ali
Causal Inference in Psychology and Neuroscience: From Association to Causation, Dehua Liang
Novel Techniques for Quantifying Secondhand Smoke Diffusion into Children's Bedroom, Sunil Ramchandani
Probing the Boundaries of Human Agency, Sook Mun Wong
Dissertations from 2021
Predicting Eye Movement and Fixation Patterns on Scenic Images Using Machine Learning for Children with Autism Spectrum Disorder, Raymond Anden
Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models, Alexander Barrett
Applications of Machine Learning to Facilitate Software Engineering and Scientific Computing, Natalie Best
Exploring Behaviors of Software Developers and Their Code Through Computational and Statistical Methods, Elia Eiroa Lledo
Assessing the Re-Identification Risk in ECG Datasets and an Application of Privacy Preserving Techniques in ECG Analysis, Arin Ghazarian
Machine-Learning-Based Approach to Decoding Physiological and Neural Signals, Elnaz Lashgari
Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning, Shiva Lotfallahzadeh Barzili
Novel Applications of Statistical and Machine Learning Methods to Analyze Trial-Level Data from Cognitive Measures, Chelsea Parlett
Optimal Analytical Methods for High Accuracy Cardiac Disease Classification and Treatment Based on ECG Data, Jianwei Zheng
Dissertations from 2020
Allocation of Public Resources: Bringing Order to Chaos, Lance Clifner
A Computational and Experimental Examination of the FCC Incentive Auction, Logan Gantner
Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning, Kayleigh Hyde
Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations, Oluyemi Odeyemi
On Quantum Effects of Vector Potentials and Generalizations of Functional Analysis, Ismael L. Paiva
Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability, Luciano Rodriguez
Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder, Natalia Rosenfield
Connecting the Dots for People with Autism: A Data-driven Approach to Designing and Evaluating a Global Filter, Viseth Sean
Novel Statistical and Machine Learning Methods for the Forecasting and Analysis of Major League Baseball Player Performance, Christopher Watkins
Dissertations from 2019
Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images, Justin J. Gapper
Estimating Auction Equilibria using Individual Evolutionary Learning, Kevin James
Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique, Chloe Martin-King
Theses from 2017
Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods, Michael Schwartz