Rate My Professor Jianming Bian

Bian's research focuses on Intensity Frontier programs, particularly major neutrino experiments such as NOvA, DUNE, FLArE, and Super-Kamiokande. His group performs neutrino oscillation analyses aimed at determining the neutrino mass ordering and CP violation phase, neutrino-electron elastic scattering measurements to constrain neutrino fluxes, and develops AI/ML technologies including deep learning for kinematic reconstruction of particle energy, direction, and vertex, Bayesian learning for oscillation parameter inference, and transformers for neutrino flavor tagging and final-state particle identification. He holds key leadership positions, including founding co-chair of the DUNE AI/ML Forum Committee, convener of NOvA’s Reconstruction and Deep Learning group, and Team Leader for the purity monitoring system at DUNE, which ensures liquid-argon quality for the experiment's physics program. Bian's interdisciplinary group at UCI comprises physics graduate students and postdocs, as well as students from computer science and statistics departments collaborating on deep-learning algorithms and statistical tools. Notable recent publications include “Search for Accelerator-Produced Sub-GeV Dark Matter with the NOvA Near Detector” (arXiv:2507.10754, submitted to PRL, 2025), “Spatial and Temporal Evaluations of the Liquid Argon Purity in ProtoDUNE-SP” (arXiv:2507.08586, accepted by JINST, 2025), “Particle hit clustering and identification using point set transformers in liquid argon time projection chambers” (JINST 20 P07030, 2025), and contributions to DUNE white papers on software, computing, detectors, and the science program (arXiv:2503 series, 2025).