AI Neural Network Optimizer Study

• Description: Conducted a comprehensive study on 1D neural network optimization techniques, focusing on enhancing performance for large-scale datasets.

• Outcome: Achieved a 15% improvement in model convergence speed.

Waste Management Data Analysis

• Description: Analyzed waste management records using Excel and R to identify key inefficiencies and opportunities for improvement.

• Outcome: Delivered actionable insights that guided sustainability practices, reducing waste misclassification by 20%.

Dynamic Mode Decomposition (DMD) for Data Pattern Analysis

• Description: Implemented Dynamic Mode Decomposition (DMD) to analyze temporal data patterns and extract key features from high-dimensional datasets. The project focused on understanding dynamic systems with fluid dynamics and environmental monitoring applications.

• Outcome: Successfully extracted dominant modes from time-series data, improving the understanding of system behaviour and forecasting future states.