About Me
Welcome! I am a dedicated Data Scientist and current PhD student, combining a deep background in operations research with advanced expertise in data science. My approach blends rigorous data analysis with innovative machine learning techniques to uncover insights that drive smarter decisions and better outcomes.
My academic and professional journey has equipped me with a profound ability to analyze and interpret large datasets, applying innovative machine learning techniques and data analytics to solve real-world problems. Currently, I am advancing my research in operations research at the Naval Postgraduate School.
My research interests will include:
- The intersection of parallel computing, optimization for integer programming, and statistical machine learning, with applications in the military and aerospace industries. I plan to explore the use of parallel algorithms, such as branch-and-bound and cutting plane methods, to enhance the efficiency of solving large-scale integer programming problems. By leveraging distributed computing frameworks and GPU acceleration, I aim to tackle complex optimization challenges more effectively.
- Integrating machine learning techniques to facilitate data-driven decision-making, feature selection, and model simplification, enhancing the adaptability and robustness of integer programming models. My work will involve applying reinforcement learning to optimize decision-making processes within dynamic environments and developing predictive maintenance and anomaly detection models that are coupled with optimization frameworks for improved resource allocation and risk management.
- Ensuring scalability and real-time processing capabilities, crucial for critical military and aerospace applications. I aim to contribute to advancements in integrating machine learning with optimization, ensuring that these systems remain interpretable and transparent, thereby driving smarter and more efficient decision-making in complex and uncertain environments.
