Aliakbar Davoodi
Full Stack Hardware-AI Developer
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About
I specialize in bridging the gap between complex algorithms and hardware implementation. With an academic and practical background in Control Systems and Machine Learning, I design systems that don't just function—they adapt.
Whether you need a precision PID controller for a custom actuator or an edge-deployed ML model for real-time sensor fusion, I provide end-to-end development. My focus is on writing efficient, low-latency code that maximizes hardware performance without sacrificing reliability.
My Core Expertise Includes:
Embedded Systems: Firmware development (C/C++), RTOS, and microcontrollers (STM32, ESP32, Arduino).
Machine Learning: Predictive modeling, TinyML for edge devices, and data analysis using Python/TensorFlow.
Control Systems: Feedback loop design, system identification, and stability analysis.
Experience
1. Embedded Systems & Firmware Development
Microcontrollers: Proficient with STM32 (ARM Cortex-M), ESP32, Arduino.Languages: Expert in Embedded C and C++; proficient in Python for scripting and testing.
RTOS & Systems: Experience with FreeRTOS for multi-threaded applications and bare-metal development.
Communication Protocols: Deep understanding of I2C, SPI, UART, CAN bus, and Ethernet (TCP/IP).
2. PCB Design & Hardware Engineering
EDA Tools: Advanced user of Altium Designer, KiCad, for multi-layer PCB layouts.Hardware Design: Schematic capture, component selection (BOM management), and power management circuit design.
Prototyping: Signal integrity analysis, EMI/EMC best practices, and hands-on hardware debugging using oscilloscopes and logic analyzers.
Manufacturing: Preparing manufacturing files (Gerbers, NC Drill, Pick-and-Place) and coordinating with PCBA vendors.
3. Control Systems & Robotics
Control Theory: Design and implementation of PID, LQR, and State-Space controllers.Modeling: System identification and simulation using MATLAB/Simulink.
Motion Control: Experience with BLDC motor control, stepper motors, and sensor fusion (IMUs, encoders) using Kalman Filters.
4. Machine Learning & Data Science
Edge AI (TinyML): Deploying optimized neural networks on MCUs using TensorFlow Lite for Microcontrollers.Frameworks: Developing models in Scikit-learn, TensorFlow, and PyTorch.
Specializations: Time-series analysis for predictive maintenance, anomaly detection, and signal processing for sensor data.
