Yousef Reda

Deep RLfor Multi-Robot Coordination

• • Automated benchmarking of 4 deep RL algorithms (PPO,SAC,A2C,DQN) produced statistical comparison reports adopted as the basis for algorithm selection by the research team achieving 92% success rate in fixed-target tasks.

• • Built experiment tracking dashboards with query-optimized data pipelines, cutting manual reporting time and surfacing insights for research decisions.

• • Designed and simulated auxetic soft actuator using FEA, achieving 40% improvement in recovery efficiency for robotic applications

• • Built an end-to-end ML pipeline with automated monitoring, experiment tracking (DVC + MLflow), and performance dashboards, Applied CNNs and transfer learning achieving measurable accuracy improvements through hyperparameter optimization and A/B testing of model architectures.

• • Automated data versioning and pipeline workflows using DVC; reduced debugging time by 40% through metrics logging and model registry, delivered performance dashboards enabling non-technical stakeholders to interpret model outcomes.

Quality Assurance Automation

• • Automated a manual 45-min measurement process end-to-end; 89% cycle time reduction with real-time quality dashboards replacing all manual reporting

• • Architected structured reporting system with formatting, eliminating manual transcription errors and enabling real-time quality monitoring dashboards.