I enjoy building embedded systems from the ground up. Most of my work involves firmware development, hardware-software integration, and experimentation with system-level programming. I'm also curious about how AI and automation can be applied in edge computing, signal processing, and real-world decision-making.

This GitHub is a collection of personal experiments, course projects, and professional tools I've developed over the years.

• 🧠 AI & Data Science: PINNs, reinforcement learning, model tracking, and edge ML.
• ⚙️ Embedded Systems: STM32, ESP32, nRF52, bare-metal programming, RTOS, Zephyr.
• 🧪 Testing & CI: Ceedling, Docker, Jenkins, data pipelines, TDD.
• 🧰 Toolchains: Yocto, Linux kernel modules, Buildroot, QEMU.
• 📡 Connectivity: BLE, MQTT, LoRa, SBUS, I2C/SPI abstraction.
• 📊 Visualization & Analysis: Data loggers, sensors, dashboards, stream processing.
• 📚 Learning Projects: FPGA, VHDL/Verilog, operating systems, Python scripting.

• Yocto BSP & kernel modules — YOCTO, IMD
• STM32 RTOS + OS experiments — test-stm32-os, STM32F4-BARE-METAL
• Peripheral drivers (I2C, SPI, GPIO expanders) — port_spi, mcp23s17

• Physics-Informed Neural Networks (PINNs) — IA-PINN
• Reinforcement learning for finance — stock-trading-DQL
• NLP experiments — NLP
• ML service comparison — ml_services_comparison

• TDD with Ceedling — TDD-MOCK, TDD-LED
• Valgrind in Docker — Valgrind
• Build systems & automation — Test-makefile, zephyr-docker

🕒 I work on these projects during my free time and use GitHub to document, learn, and share.