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sinarahmani82/README.md

This is Sina Rahmani

Email GitHub Degree Target

🎓 Academic Background & Focus

I hold a B.Sc. in Computer Engineering (Software) from Islamic Azad University, Qazvin Branch (QIAU). I am actively preparing for Graduate Studies (M.Sc. / Ph.D.) in Europe (Belgium, Netherlands, Finland) and Canada.

My primary technical and research focus lies at the intersection of Robust & Explainable Machine Learning, Reproducible AI Systems, and High-Performance Data Pipelines. My goal is bridging the gap between rigorous scientific research papers and production-grade, well-architected software implementations.

🔬 Research Interests

  • Explainable & Trustworthy AI (XAI): Model interpretability, algorithmic fairness, and uncertainty quantification (aligned with European trustworthy AI initiatives).
  • Modern Information Retrieval & LLM Systems: Hybrid Graph-RAG architectures and knowledge-grounded semantic retrieval.
  • Reproducible Deep Learning: Algorithm benchmarking, model compression/pruning, and systematic empirical evaluation.
  • Scalable Data Systems: High-throughput streaming, memory-efficient data processing (Polars, DuckDB), and clean architectural design.

🛠️ Technical Stack

Programming & Environments:
Python (Proficient) • SQL • C++ (Algorithms/Data Structures) • Bash/Linux

Machine Learning & Data Science:
PyTorch • Scikit-Learn • Hugging Face • Captum (XAI) • Weights & Biases (W&B) • Polars • Pandas • NumPy

Software Engineering & Systems:
FastAPI (Async) • Docker / Docker Compose • PostgreSQL • Redis • Neo4j • DuckDB • Kafka

Testing & Engineering Standards:
Git • GitHub Actions (CI/CD) • Pytest • Type Annotations & Mypy • Ruff / Clean Architecture

📌 Featured Implementations & Research Projects

Explore my pinned repositories below for in-depth codebases, formal write-ups, benchmarks, and reproducibility pipelines:

  1. [Paper Reproduction]: Self-contained PyTorch reproduction of a peer-reviewed paper with ablation studies, parameter efficiency metrics, and complete tracking logs.
  2. [Graph-RAG System]: Knowledge Graph-enhanced retrieval-augmented generation framework designed to mitigate hallucinations in scientific literature extraction.
  3. [Explainable Medical Diagnostics]: Deep learning diagnostic pipeline integrating SHAP and Integrated Gradients for verifiable visual and tabular decision support.
  4. [High-Throughput Analytics Pipeline]: Streaming analytics pipeline leveraging Polars and DuckDB for low-memory, high-velocity analytical processing.

📈 GitHub Analytics & Activity

Sina's GitHub Streak Top Languages

📬 Academic Contact

I welcome discussions regarding academic opportunities, graduate research assistantships, and prospective supervisor matchings.

  • 📧 Direct Email: sinarahmani82@gmail.com
  • 💬 GitHub Discussions / Issues: Feel free to open an issue or inquiry in any of my public repositories.

Pinned Loading

  1. academic-graph-rag academic-graph-rag Public

    A lightweight Hybrid GraphRAG pipeline combining Knowledge Graph traversal with dense vector retrieval for multi-hop scientific literature discovery.

    Python 1

  2. vit-pruning-benchmark vit-pruning-benchmark Public

    A modular PyTorch implementation of Vision Transformer (ViT) from scratch, evaluating unstructured magnitude pruning trade-offs on CIFAR-10.

    Python 1

  3. trustworthy-clinical-xai trustworthy-clinical-xai Public

    An auditable Clinical Decision Support pipeline integrating Feature Attribution (XAI) and Algorithmic Fairness auditing under the EU AI Act compliance standards.

    Python 1

  4. high-throughput-stream-analytics high-throughput-stream-analytics Public

    An ultra-fast, memory-efficient streaming telemetry analytics pipeline built with Polars and DuckDB, featuring out-of-core aggregations and real-time anomaly detection benchmarks.

    Python 1

  5. production-mlops-service production-mlops-service Public

    A production-ready asynchronous ML inference microservice built with FastAPI, Clean Architecture, and real-time statistical Data Drift monitoring.

    Python 1

  6. asynctrace-cli asynctrace-cli Public

    A lightweight, zero-dependency asynchronous latency and memory profiling CLI tool for Python async services, featuring distribution tracking and markdown report generation.

    Python 1