Wanwei Wu

Wanwei Wu studies the fundamental nature of the universe through precision neutrino measurements. His research combines advanced detector technologies, AI/ML, and high-performance computing to enable large-scale data analysis, simulation, and reconstruction for next-generation neutrino experiments.

His AI/ML research focuses on precision neutrino measurements and advanced detector data processing for large-scale neutrino experiments. This includes AI-driven reconstruction and interpretation of neutrino interactions, including particle identification and event characterization, as well as intelligent data reduction techniques such as region-of-interest identification, noise mitigation, and scalable processing of large detector datasets.

He leads the development of input/output, storage, and persistency models for the DUNE data processing framework. Within the DUNE-US Scientific Computing R&D program, he serves as a Level 3 manager for persistency and oversees the development of FORM (Flexible-grained Object Read/write Model), the flexible I/O layer for Phlex-based DUNE data processing. FORM enables scalable access to large detector data objects across multi-petabyte datasets and heterogeneous workflows.