General Lab Information

Xiaogang Yang

Computational Scientist, Data Analysis & Workflow Integration, National Synchrotron Light Source II

Xiaogang Yang

Brookhaven National Laboratory

National Synchrotron Light Source II
Bldg. 741
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-3449
yangxg@bnl.gov

Xiaogang Yang is a computational scientist in the Data Analysis & Workflow Integration group of the Data Science & Systems Integration division at NSLS-II. He earned his PhD in Chemical Engineering from Delft University of Technology in the Netherlands and has over 15 years of experience in X-ray imaging research. Before joining BNL in 2021, Xiaogang was a tenured scientist at Petra III, Deutsches Elektronen-Synchrotron (DESY), Germany, where he led and contributed to various theoretical and algorithmic developments in X-ray imaging, including advanced tomography, XRF, and ptychography, with a strong focus on deep learning solutions. His career in computational R&D for synchrotron X-ray science began at Argonne National Laboratory, where he pursued AI-driven methods for synchrotron data processing and analysis.

Research | Education | Appointments | Publications | Awards

Research Activities

  • Model-based deep neural network framework for data reconstruction
  • AI-driven data process solutions for X-ray scattering and imaging
  • Intelligent Acquisition and Reconstruction for Hyperspectral Tomography System: Solving Tensor Tomography

Education

Delft University of Technology, Delft, The Netherlands

  • 2009 - 2015,  Ph.D., Chemical Engineering

Professional Appointments

National Synchrotron Light Source II (NSLS-II), Brookhaven National Laboratory, Upton NY

  • Nov. 2021 – Present, Computational Scientist

Petra III, Deutsches Elektronen-Synchrotron (DESY), Hamburg, Germany

  • 2019 - 2021, Tenured Scientist
  • 2017 - 2019, Staff Scientist

Advanced Photon Science (APS), Argonne National Lab, Lemont, IL

  • 2015 - 2017, Postdoctoral Researcher 

Selected Publications

  • Yang X, Schroer C (2021) Strategies of Deep Learning for Tomographic Reconstruction. 2021 IEEE International Conference on Image Processing (ICIP) 3473–3476. https://doi.org/10.1109/icip42928.2021.9506395
  • Yang X, Kahnt M, Brückner D, et al (2020) Tomographic reconstruction with a generative adversarial network. Journal of Synchrotron Radiation 27:486–493. https://doi.org/10.1107/s1600577520000831
  • Yang X, De Andrade V, Scullin W, et al (2018) Low-dose x-ray tomography through a deep convolutional neural network. Scientific Reports 8:. https://doi.org/10.1038/s41598-018-19426-7
  • Yang X, De Carlo F, Phatak C, Gürsoy D (2017) A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography. Journal of Synchrotron Radiation 24:469–475. https://doi.org/10.1107/s1600577516020117
  • Shashank Kaira C, Yang X, De Andrade V, et al (2018) Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning. Materials Characterization 142:203–210. https://doi.org/10.1016/j.matchar.2018.05.053
  • Yang X, Hailu D, Kulvait V, et al (2024) Physics-informed generative network for phase retrieval from a single intensity measurement. https://doi.org/10.1364/opticaopen.26367208.v1

Awards & Recognition

Director's Award of outstanding contributions at Deutsches Elektronen-Synchrotron (DESY) in 2019

Xiaogang Yang

Brookhaven National Laboratory

National Synchrotron Light Source II
Bldg. 741
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-3449
yangxg@bnl.gov

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