dsu-cs/csc702_fall2025
Sep 2025 – Oct 2025
2 Months active
Languages Used
JSONJupyter NotebookMarkdownPythonCSV
Technical Skills
Data AnalysisData EngineeringData PreprocessingData ScienceDeep LearningDocumentation
skkampshoff
PROFILE
Skkampshoff
During two months on the dsu-cs/csc702_fall2025 repository, Srikar Kampalli developed an end-to-end machine learning pipeline for chess, spanning data ingestion, preprocessing, model training, and evaluation. He implemented a Transformer-based ChessDecoder and a functional chess bot, enabling reproducible, config-driven experimentation. His work included hyperparameter optimization using scikit-optimize, advanced text processing, and integration of Jupyter Notebooks for experiment tracking. Leveraging Python, PyTorch, and Pandas, Srikar established robust workflows that accelerated model iteration and improved reproducibility. The codebase improvements, documentation updates, and automated testing contributed to maintainability and positioned the team for scalable, production-ready chess AI development and evaluation.
Overall Statistics
Feature vs Bugs
94%Features
Repository Contributions
31Total
Bugs
1
Commits
31
Features
16
Lines of code
254,094
Activity Months2
Your Network
20 peopleShared Repositories
20
Aman SinghMember
Aman SinghMember
Ashar SiddiquiMember
Adam BradleyMember
Aman SinghMember
charlieking108Member
Jared SoundyMember
JosephMember
kesslermike-wqMember
Work History
October 2025
10 Commits • 3 Features
Oct 1, 2025October 2025 highlights: End-to-end Chess ML pipeline established from data ingestion to model evaluation, with a Transformer-based ChessDecoder and a functional chess bot. Delivered reproducible, config-driven workflows and automated testing to accelerate experimentation and reduce integration risk. This work positions the team to iterate on chess strategies and benchmarks with a production-ready evaluation setup.
10 Commits • 3 Features
Oct 1, 2025October 2025 highlights: End-to-end Chess ML pipeline established from data ingestion to model evaluation, with a Transformer-based ChessDecoder and a functional chess bot. Delivered reproducible, config-driven workflows and automated testing to accelerate experimentation and reduce integration risk. This work positions the team to iterate on chess strategies and benchmarks with a production-ready evaluation setup.
October 2025
September 2025
21 Commits • 13 Features
Sep 1, 2025September 2025 monthly summary for dsu-cs/csc702_fall2025: Delivered a cohesive experimental stack enabling rapid model iteration, robust evaluation, and reproducibility across projects. The work emphasized business value through shorter experimentation cycles, clearer performance insights, and scalable workflows ready for deployment and hand-off. Major codebase improvements and documentation updates also support onboarding and maintainability.
September 2025
21 Commits • 13 Features
Sep 1, 2025September 2025 monthly summary for dsu-cs/csc702_fall2025: Delivered a cohesive experimental stack enabling rapid model iteration, robust evaluation, and reproducibility across projects. The work emphasized business value through shorter experimentation cycles, clearer performance insights, and scalable workflows ready for deployment and hand-off. Major codebase improvements and documentation updates also support onboarding and maintainability.
Activity
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Quality Metrics
Correctness81.4%
Maintainability81.4%
Architecture77.2%
Performance73.0%
AI Usage30.4%
Skills & Technologies
Programming Languages
CSVJSONJupyter NotebookMarkdownPython
Technical Skills
Chess AIChess ProgrammingData AnalysisData EngineeringData EntryData LoadingData ManagementData PreprocessingData ScienceDeep LearningDocumentationGensimGensim LibraryHyperparameter OptimizationHyperparameter Tuning
Repositories Contributed To
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