Jia-Yi Yang

Praxist enables R&D agents to build on validated findings, drastically reducing costs while achieving superior performance in complex engineering challenges.

A single model can effectively learn from heterogeneous attributed graphs, outperforming specialized approaches in critical graph learning tasks.

Force GNNs to explicitly reason about graph-level concepts and you get state-of-the-art performance in both classification and interpretability.