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arda-tuz/README.md
Hüseyin Arda Tuz - Artificial Intelligence Engineering and Computer Engineering at TOBB University of Economics and Technology, Ankara, Türkiye

Let's Talk: email ardatuz4@gmail.com Professional Profile: LinkedIn


About Me

I build end-to-end intelligent systems that connect machine learning research with reliable, testable software. I am a senior Artificial Intelligence Engineering student at TOBB ETÜ, pursuing a double major in Computer Engineering and maintaining a GPA above 3.0 in both programs. I am particularly interested in defense technologies, autonomous systems, and applied AI that must operate reliably in complex real-world environments. I aim to contribute to advanced engineering programs at the intersection of artificial intelligence, software systems, robotics, perception, autonomous platforms, and defense technologies.

I am most interested in the point where an AI model stops being a notebook experiment and becomes a measurable, dependable engineering system.

  • AI & Perception: computer vision, machine learning, behavioral modeling, RAG, forecasting, generative AI integration
  • Systems & Data: backend services, relational and time-series storage, real-time telemetry, Spark pipelines, analytical applications
  • Engineering Practice: modular architecture, explicit data contracts, testing, reproducibility, graceful failure, technical documentation

Featured Engineering Projects

AI-Powered Personal Financial Assistant: five-day Mamba-2 forecasts, FinBERT sentiment, Monte Carlo risk and portfolio optimization behind one chat interface
Adaptive Boss Fight: a Unity boss whose abilities, narration and music adapt to how the player fights
dotsteward: reproduce your developer setup on every machine from a private repository

Technology Stack

Technology stack. Languages: Python, Java, C#, JavaScript, TypeScript, SQL. AI, ML and Perception: PyTorch, scikit-learn, OpenCV, Hugging Face, YOLO, RAG (retrieval-augmented generation). Backend and Data: FastAPI, Django, PostgreSQL, TimescaleDB, Apache Spark, DuckDB. Frameworks: Next.js, React, Streamlit, Unity. Platforms and Tools: Docker, Git, Linux, Google Cloud, Nginx, Jupyter.

Also used: PySpark, Mamba-2, FinBERT, ChromaDB.


Contribution graph drawn as a snake

Interested in building intelligent systems that are not only impressive in a demo, but reliable under real engineering constraints.

Pinned Loading

  1. Bitirme2025/AI_Financial_Assistant Bitirme2025/AI_Financial_Assistant Public

    Institutional-grade financial intelligence with chat bot user interface, redesigned for everyday investors

    Python 2 1

  2. Chlorine-Graduation-Group/Adaptive_Boss_Fight Chlorine-Graduation-Group/Adaptive_Boss_Fight Public

    An AI-driven boss encounter that studies how you fight, then changes its tactics, voice, and soundtrack around you.

    C# 2

  3. Amazon-Product-Recommendation-Using-Spark Amazon-Product-Recommendation-Using-Spark Public

    A gate-controlled recommendation platform that turns Amazon product records into auditable multi-model rankings and an interactive analytics experience.

    Python

  4. dotsteward dotsteward Public

    Your whole dev setup on every machine. Install a coding agent like Claude Code or Codex, point it at your personal dotsteward repo, and it reproduces your tools, versions, settings and agent skills…

    Shell 2