I am a Senior Data Integration Engineer and independent researcher specializing in the intersection of artificial intelligence and supply chain management. With over seven years of experience architecting enterprise data platforms and integration solutions, I have helped global organizations across logistics, healthcare, and technology transform fragmented operational data into reliable foundations for planning and forecasting decisions.
My research focuses on making AI-driven demand forecasting both trustworthy and business-relevant. I am the author of research papers on explainable machine learning for retail demand forecasting and on stockout-aware forecasting methods that correct hidden biases in sales data, a work that bridges advanced modeling techniques with the practical realities of inventory management, replenishment, and service-level performance. I advocate for a decision-first view of AI: models should be judged not only by their accuracy, but by the quality and cost of the business decisions they enable.
As a practitioner-researcher, I combine leading production data systems by day with publishing applied AI research in supply chain analytics and I'm passionate about helping industry teams move from AI experimentation to measurable operational impact.