معرفی
Dr. Leigh Whitehead is a Senior Research Associate at the University of Cambridge's Department of Physics, affiliated with the High Energy Physics sector and the Neutrino Group. His work centers on neutrino oscillation experiments, particularly the DUNE and ProtoDUNE projects, with a focus on deep learning techniques for event reconstruction.
He obtained his PhD from the University of Warwick in 2012, researching neutrino oscillations on the T2K experiment. He has held positions as a Research Associate at University College London (MINOS, CHIPS collaborations) and a Research Fellow at CERN (2016-2019) before joining Cambridge in 2019.
His research interests include:
- Neutrino interaction classification using convolutional neural networks
- Pandora event reconstruction framework development
- Applications of graph neural networks and generative adversarial networks in neutrino physics
- Deep learning integration for detector simulation and data analysis
- Long-baseline neutrino oscillation experiments
Key article trends highlight his pioneering work in applying advanced machine learning methods to neutrino oscillation studies, particularly in optimizing detector simulations and improving interaction classification accuracy across international collaborations like DUNE and MINOS.
Scientific awards include:
- Research Fellowship at CERN (2016)
He actively convenes the DUNE Far Detector Simulation and Reconstruction group, contributes to interdisciplinary collaborations, and engages in training the next generation of physicists through his advisory roles.
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