Exploring machine learning and statistical methodologies for large-scale data analysis and pattern extraction.
Multi-disciplinary techniques combining statistical analysis, machine learning, and domain-specific research
Developing probabilistic models to interpret complex datasets and extract meaningful insights.
Applying deep learning and traditional algorithms to predict outcomes and identify hidden patterns.
Translating real-world problems into mathematical representations for analysis and optimization.
Insights and breakthroughs from extensive data modeling and computational experiments
Achieved 98.5% accuracy in time-series forecasting across 12+ industries using novel ensemble methods.
Developed an outlier detection system that reduces false positives by 47% in industrial IoT applications.
Peer-reviewed research outputs from this investigation
Published in the Journal of Advanced Computational Sciences
Conference on Machine Learning Applications, October 2023