Lab

Structure-Aware Learning and Modeling Lab

結構導向學習與建模研究室（別名：格物致知研究室）

Uncover the structure of things to expand the bounds of insight.

Main Mission

Our research begins with intuitions and observations, uses mathematics and theory to describe and analyze them, translates the resulting understanding into applicable methods, and uses empirical evidence to refine the intuitions and theories behind those methods.

Why

Important scientific and engineering problems live in structured spaces shaped by geometry, symmetry, hierarchy, constraints, and measurable observables. We seek to build AI methods that can use these structures to move beyond data-driven methods toward meaningful generation, exploration, and discovery.

How

We work in a loop connecting intuition, mathematics, computation, and evidence. Starting from structural intuitions and scientific observations, we use mathematics and theory to describe and analyze the underlying structures. We translate this understanding into implementable methods, and use empirical results to refine both the methods and the understanding behind them.

What

We develop foundations and algorithms for representing and generating data through Structure-Aware Learning and Modeling. We evaluate our methods through demanding problems in scientific discovery and engineering.

Principal Investigator