About

Control systems, robotics, and applied AI.

I am a Control Systems Engineer and Roboticist who turns control theory into reliable, field-ready systems.

Background

I am a Control Systems Engineer and Roboticist who turns control theory into reliable, field-ready systems. I focus on aerial robotics, intelligent control, multi-agent systems and decentralized cognition. I design end-to-end experiments that move from simulation to real system tests. I learn whatever is needed to make it happen, implement it, validate it in simulation and on the bench, then bring it onto real hardware. If it excites me, I document it well and keep pushing it further. I emphasize reproducible engineering, with well-written READMEs, quick-start examples, and measured performance reports so others can reproduce and build on my results.

Current focus

What I'm working on: scalable control architectures and swarm-ready primitives, low-latency embedded drivers, and compact perception-to-control pipelines that run on constrained hardware. I prioritize deterministic behavior, measurable performance, and safety margins that make experiments repeatable outside the lab.

Open-source approach

OpenSource approach: I try my best to develop better and better software. I develop modular, efficient, and affordable software - MATLAB, Python, C/C++ libraries and drivers designed for low overhead and easy integration. My projects use permissive licensing, concise examples, and automated tests to lower the barrier to entry and enable real-world adoption by hobbyists and practitioners alike or at least try my best to do it.

View the full project list

Tech stack

Languages

MATLAB Python C C++ Julia Processing 3 (Java)

Frameworks & libraries

Simulink Simscape Simscape Multibody Robotics Toolbox Symbolic Math Toolbox TensorFlow/Keras PyTorch Scikit-learn Pandas OpenCV MediaPipe PyBullet

Embedded systems and electronics

Raspberry Pi Arduino AVR STM32 KiCad

Collaboration style

How I like to collaborate: cross-disciplinary and pragmatic. I work closely with people in perception, neuroscience-inspired decision models, and systems engineering; I value clear interfaces, shared tests, and short feedback loops so research becomes deployable engineering. I prefer small, focused iterations with public checkpoints and documented outcomes.

Collaboration principles

  • Clear interfaces and shared tests
  • Short feedback loops and rapid iteration
  • Public checkpoints with documented outcomes
  • Pragmatic cross-discipline alignment