Date of Award
5-1-2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
College/School/Department
Department of Computer Science
Dissertation/Thesis Chair
Chinwe Ekenna
Committee Members
Petko Bogdanov, Shaghayegh Sahebi, Sam Jacobs
Keywords
High-dimensional data, Machine learning, Motion Planning, Protein Interaction
Subject Categories
Computer Sciences
Abstract
Processing and analyzing high-dimensional data, particularly in domains like protein research and robotics, introduces unique challenges. In protein analysis, high-dimensional data often arises from complex molecular structures and their interactions. Analyzing protein data involves intricate computational models and algorithms that must deal with the large size of molecular datasets and the need to extract biologically relevant information. This makes it challenging to discern significant protein structure-activity relationships, understand complex biological interactions, or predict protein behavior accurately. Similarly, in robotics, high-dimensional data is prevalent when dealing with sensory inputs, the dimensionality of the robot, or the complexity of the operational environments. High-dimensional motion planning is notoriously computationally intensive and presents challenges related to path optimization, collision avoidance, and real-time decision-making. In this research, we address these problems by introducing machine learning approaches that are able to process high dimensional data and feature analyzing techniques to extract underlying relationships within the data to solve protein interaction and motion planning problems.
Recommended Citation
Tran, Tuan Nguyen Anh, "Advancing Learning Models For High-Dimensional Data: From Molecular Modeling To Motion Planning" (2024). Legacy Theses & Dissertations (2009 - 2024). 3372.
https://scholarsarchive.library.albany.edu/legacy-etd/3372
