I am a Mechanical Engineering graduate from the Technion – Israel Institute of Technology, specializing in Robotics and Control Systems. I’m passionate about Robotics, Autonomous Systems, Advanced Control, AI, and Machine Learning—fields where I aim to solve complex technical challenges through innovation.
I'm currently an Engineering Intern at Fusmobile, where I work across both mechanical and software applications. I develop internal tools to automate tasks in production and management, and I'm also involved in hands-on assembly tasks that integrate mechanics, electronics, and embedded systems such as Arduino.
At the Technion's Flow Control Lab, I served as a Research Assistant designing and testing motor-pump systems for wind-powered desalination. My work in system dynamics and control theory supports the development of more efficient renewable energy solutions, with publication plans underway.
Previously at NUFiltration, I optimized water filtration systems for both industrial and humanitarian use. I also gained experience testing MEMS sensors at STMicroelectronics, where I worked with the micro-scale technologies that power modern electronics.
Designed and built a self-balancing robot using Lego Spike, starting with deriving equations of motion and formulating a state-space model. Validated the design through MATLAB simulations, static/dynamic sensor testing, and iterative hardware prototyping. Achieved close alignment between simulated predictions and physical performance, demonstrating robust control system integration.
Implemented GPT-2 (124M parameters) in PyTorch from scratch, replicating its tokenization, positional embeddings, and self-attention mechanisms. Achieved 26% HellaSwag accuracy (vs. OpenAI's 28.92%) with just 2 days of training on a single NVIDIA A600 GPU—compared to OpenAI's month-long cluster (NVIDIA V100s). This work demonstrates how strategic architectural choices can yield near-benchmark performance under resource constraints. Future work includes fine-tuning for code completion and IDE integration, leveraging these efficiency insights.
Developed and trained a Deep Q-Learning agent to autonomously play Snake, demonstrating core RL principles: state-action mappings, reward function design, and ε-Greedy exploration. Achieved consistent scores of 60+ within 80 training episodes, showcasing rapid convergence in constrained environments.
Collaborated with a 4-member software team to integrate inertial sensors (MPU6050), gps sensors (Neo-6M), pressure sensors (MPL3115A2), and triple-axis magnometometer (MMC5603) into a student-built rocket. Developed firmware for Arduino Nano and STM32 to process real-time flight data, ensuring stable trajectory tracking. Contributed to the team’s goal of achieving precise altitude control.