
Le Niu developed a complete K-Means clustering demo with visualization for the hasktorch/hasktorch repository, focusing on practical machine learning workflows in Haskell. The project involved implementing core algorithmic components such as data generation, distance calculation, and iterative centroid refinement, all structured using functional programming principles. Le Niu added a new Cabal executable target to streamline building and running the demo, and integrated plotting functionality to visualize clustering results, enhancing interpretability for users. This work provided a concrete example of algorithm integration in Haskell, supporting onboarding and demonstrating how data visualization and algorithm implementation can be combined in a real-world ML context.
June 2025: Delivered an end-to-end K-Means Clustering Demo with Visualization for hasktorch/hasktorch. The implementation includes data generation, distance calculation, iterative centroid refinement, a new Cabal executable target, and plotting functionality to visualize clustering results. This work enhances practical ML workflow demonstrations, improves onboarding for users integrating clustering workflows with the library, and showcases a concrete example of algorithm integration in Haskell.
June 2025: Delivered an end-to-end K-Means Clustering Demo with Visualization for hasktorch/hasktorch. The implementation includes data generation, distance calculation, iterative centroid refinement, a new Cabal executable target, and plotting functionality to visualize clustering results. This work enhances practical ML workflow demonstrations, improves onboarding for users integrating clustering workflows with the library, and showcases a concrete example of algorithm integration in Haskell.

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