Pure MLX implementations of UMAP, t-SNE, PaCMAP, TriMap, DREAMS, CNE, MMAE, and NNDescent for Apple Silicon. Metal GPU for computation and video rendering.

R wrappers to connect Python dimensional reduction tools and single cell data objects (Seurat, SingleCellExperiment, etc...)

PaCMAP in pure MLX for Apple Silicon. Pure GPU, no scipy/numba.

This is a dimensionality reduction project in the course DD2470 Advanced Topics in Visualization and Computer Graphics at KTH Royal Institute of Technology, Stockholm (2024), made by Linnéa Gustafsson.

Visualization and embedding of large datasets using various Dimensionality Reduction (DR) techniques such as t-SNE, UMAP, PaCMAP & IVHD. Implementation of custom metrics to assess DR quality with complete explaination and workflow.

Interactive 3D visualization of AI embedding spaces. Real-time bias probes, semantic analogies, nearest-neighbor search, and cluster analysis. Built with React Three Fiber and FastAPI.

Automated detection, visualization and suppression of hallucination-associated neurons in open-source LLMs — LLM mechanistic interpretability research tool

Winning one of the DACON competition

Accelerate high-speed dimensionality reduction on Apple Silicon with pure Metal GPU implementations of UMAP, t-SNE, PaCMAP, and more.