I am a researcher in Industrial Engineering with extensive expertise in end-to-end problem solving, combining data science, optimization, and decision analytics. My work spans the full pipeline, from data collection and wrangling, through statistical and machine learning modeling, to optimization under uncertainty, decision analysis, and results visualization, ultimately generating actionable recommendation frontiers for complex real-world problems.

My GitHub highlights a collection of research projects, algorithms, and applications in supply chain logistics, time series modeling, and stochastic optimization. You can also explore my research publications on Google Scholar.

I enjoy translating complex challenges into rigorous mathematical and computational frameworks and advanced machine learning techniques. Explore my repositories and feel free to connect if you'd like to collaborate or discuss innovative ideas!

Currently, I am learning:

• Transformer architectures and advanced ML models to enhance time-series forecasting within optimization frameworks.
• Agentic AI tools for generating optimization solutions with dynamic forecast updates, bridging AI planning with stochastic optimization.