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Advances in Graph Learning and Inference

Graph-based data processing algorithms impact a variety of application domains ranging from transportation networks, artificial intelligence systems, cellphone networks, social networks, and the Web. Nevertheless, the emergent big-data era poses key conceptual challenges: several existing graph-based methods used in practice exhibit unreasonably high running time; several other methods operate in the absence of correctness guarantees. These challenges severely imperil the safety and reliability of higher-level decision-making systems of which they are a part.

A Physics-Aware Learning Framework for Microstructure Design

A key problem in computational material science deals with understanding the effect of material distribution (i.e., microstructure) on material performance. The challenge we consider here is to synthesize microstructures with desired physical and chemical properties, given a finite number of microstructure images, evaluated based on the physical invariances that the microstructure exhibits.

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