Professor and academic leader in Data Science, Machine Learning, and Applied Statistics with a focus on business decision-making and productive AI systems. My work operates at the intersection of statistical rigor, machine learning implementation, and organizational strategy. My research and academic leadership concentrate on bridging the gap between theoretical modeling and real-world deployment, particularly in MLOps, predictive modeling, and AI-driven decision frameworks. I am especially interested in how advanced analytics can support defensible, measurable, and strategically aligned decisions in complex environments. I lead graduate and undergraduate programs in Data Science and Machine Learning, fostering hybrid profiles that integrate technical depth with managerial perspective. My academic approach emphasizes model validation, uncertainty quantification, governance in AI systems, and the translation of analytics into business impact. Current research interests include: – MLOps and production-grade machine learning – Applied statistical modeling for strategic decision-making – AI governance and responsible AI – Generative AI integration in enterprise environments – Data-driven organizational transformation I am open to academic and industry collaborations focused on applied research, innovation in analytics education, and real-world implementation of AI systems.