Deep Manifold Learning for High-Dimensional Data Analysis: Methods, Theory, and Applications
Date:
Doctoral dissertation defense presenting comprehensive research on deep manifold learning methods for high-dimensional data analysis. The dissertation covered: (1) Theoretical foundations of topology-preserving dimensionality reduction using deep neural networks; (2) Novel architectures including DLME, DMTEV, and DMT-ME for explainable visualization; (3) Applications in single-cell genomics, medical imaging, and drug discovery; (4) Benchmarking studies demonstrating superior performance over classical methods. Committee members included leading experts in AI, computational biology, and medical informatics. Successfully defended with distinction, receiving recognition for significant contributions to both methodology and real-world applications.