Abstract

As we enter an era dominated by artificial intelligence and quantum computing, the role of statistics is more vital than ever — not only in making sense of data, but in shaping algorithms that drive intelligent decision-making under uncertainty. This talk presents a statistical lens on algorithmic intelligence, grounded in recent work on hybrid AI-quantum models, digital twins, and optimization in complex systems such as finance, healthcare, and logistics. I will share insights from applied collaborations involving AI-driven forecasting, reinforcement learning, and quantum circuit learning — highlighting how statistical thinking ensures robustness, interpretability, and transferability in modern learning systems. By integrating statistical modeling with quantum-enhanced computation and AI-based decision support, we can envision a new generation of intelligent systems that are both powerful and principled. This talk reflects on the methodological challenges and practical opportunities at this interdisciplinary frontier, offering a roadmap for statisticians to engage with the technologies of tomorrow.