Deep Learning for High-Dimensional Data Analysis and Visualization

Forum presentation on advanced deep learning techniques for analyzing and visualizing high-dimensional scientific data. Covered dimensionality reduction methods including deep manifold learning, variational autoencoders, and diffusion-based approaches. Discussed challenges in preserving local and global structure, handling non-linear manifolds, and maintaining interpretability. Presented applications across diverse domains: single-cell genomics (tens of thousands of genes), medical imaging (volumetric data), and materials science (molecular dynamics). Demonstrated how topology-aware neural networks can reveal hidden patterns in complex datasets while providing biologically or physically meaningful representations.