学术报告新

We have entered an era where deep learning and foundation models are transforming data analysis, increasingly handling prediction tasks that were traditionally the domain of statistical modeling. This rapid shift raises a fundamental question: How should statistics evolve in a landscape dominated by large-scale AI? In this talk, I argue that rather than becoming obsolete, traditional statistical principles are essential for overcoming the natural limits of brute-force scaling. I present a research program driven by a dual perspective: applying statistical thinking to solve engineering bottlenecks in modern AI, and conversely, leveraging AI paradigms to inspire new statistical methodologies. I will illustrate this synergy through three chapters of my research:

- Statistical Efficiency for AI Systems: I first demonstrate how Mixture Reduction grounded in optimal transport addresses computational redundancy, enabling the compression of 3D computer graphics models by 90% while preserving geometric fidelity. I further apply this rigor to Federated Learning, resolving label switching and utilizing Empirical Likelihood to transform central servers into "intelligent routers" that leverage, rather than suppress, data heterogeneity.
- AI Inspires New Statistics: Turning the direction of influence, I explore how In-Context Learning (ICL) redefines statistical inference. We show that foundation models trained via ICL can outperform specialized statistical methods in a wide range of tasks.

This talk aims to demonstrate that the future of data science lies in a deep integration where statistical rigor provides efficiency and trustworthiness to AI, while modern AI systems expand the boundaries of what is statistically possible.