Timur Fakhrtdinov

• Developed a new RL training pipeline, achieving 3 times faster training compared to the previous system. • Designed and implemented a novel reward system for reinforcement learning agents, resulting in the team top-performing model. • Conducted statistical analysis and backtesting to evaluate RL agent performance and support iterative improvements. • Implemented techniques from recent RL research papers to enhance model performance and training stability.

• Improved forecast accuracy and reduced electricity surplus by 30% through optimization insights. • Developed a machine learning model to predict electricity prices, enabling 3% average savings through consumption forecast adjustments. • Automated forecasting with pipelines for preprocessing, training, and evaluation — saving time and manual effort. • Designed and taught hands-on Python lessons for 20+ school students as part of an outreach program, guiding them toward building basic ML project prototypes.