NERSCPowering Scientific Discovery Since 1974

Yan Zhang

IMG 0956
Yan Zhang, Ph.D.
Postdoctoral Fellow
Lawrence Berkeley National Laboratory
1 Cyclotron Road
Bldg 59, Room 4024E
Berkeley, CA 94720 US


        Ph.D., Electrical Engineering, Northeastern University, Boston, MA
        B.E., Automation/Control Engineering, Beijing University of Technology, China


       NERSC Postdoctoral Fellow, Lawrence Berkeley National Laboratory, Berkeley, CA
  • Worked on Deep Learning at scale (parallel/distributed), extreme-scale spatial-temporal learn- ing algorithms for multimodal data streams (fMRI brain image) using modal/data parallelism.
  • Analyzed kernel performance of deep neural network applications based on Roofline model and consulted HPC users for optimization strategies/approaches using NVIDIA profiling tools.
  • Served as reviewer for Deep Learning & Data Analytics related scientific research proposals of using the next generation heterogeneous CPUs-GPUs supercomputing system - Perlmutter.
  • Postdoctoral Appointee, Argonne National Laboratory, Lemont, IL
  • Designed deep/transfer learning models (Convolutional Neural Networks) for classification, regression, anomaly detection and feature extraction on electron microscopy/spectroscopy.
  • Developed smart imaging and dynamic sampling methods using both supervised and unsupervised learning to reduce acquisition time and radiation for scanning imaging modalities.
  • Applied convolutional auto-encoders, generative adversarial networks (GANs) and neural style transfer for electron microscopic image synthesis using parallel and distributed GPUs processing.
       Graduate Research Assistant, Northeastern University, Boston, MA
  • Designed classification (SVM, Neural Networks) and regression (Least-square, Lasso, Ridge, Bayesian) models for x-ray scattering data to recognize shape and size of macromolecules.
  • Developed clustering and image processing algorithms to analyze XRD images of biological tissue - feature selection, dimensionality reduction, pattern matching and spectral clustering.
  • Applied data quantization, pair-distance function and Fourier method to simulate diffraction patterns of macromolecules and to build dictionary for parameter estimation of measurements.
       Image Processing Software Intern, Seagate Technology LLC, Bloomington, MN
  • Developed image analysis and pattern recognition algorithms for components detection as well as contaminants and artifacts on AFM images of hard drive recording heads.
       Software Engineer Intern, Rudolph Technologies Inc., Tewksbury, MA
  • Developed computer vision and machine learning algorithms for automatic defect classification on SEM images of semiconductor wafer - edge detection, segmentation in C++. 


  • Father of Molecular-MNIST (2019), arXiv:1911.07644
  • ATPESC (2019), Argonne Training Program on Extreme-Scale Computing, St. Charles, IL
  • Dissertation Completion Fellowship Award (2016), Northeastern University, Boston, MA
  • First-Class Scholarship for Academic Excellence (2009), Beijing University of Technology, China


Journal Articles

  1. Y. Zhang, et al. (2018) “Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling”. Ultramicroscopy, 184:90-97.
  2. Y. Zhang, et al. (2016) “Diffraction pattern simulation of cellulose fibrils using distributed and quantized pair-distances”. Journal of Applied Crystallography, 49(6):2244-2248.
  3. Y. Zhang, et al. (2015) “Breakdown of hierarchical architecture in cellulose during dilute acid pretreatments”. Cellulose, 22(3):1495-1504.
  4. H. Inouye, Y. Zhang, et al. (2014) “Multiscale deconstruction of molecular architecture in corn stover”. Scientific Reports, 4, 3756

Conference/Workshop/arXiv Papers

  1. Y. Zhang, et al. (2019) “A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy”. arXiv:1911.07644.
  2. Y. Zhang, et al. (2018) “U-SLADS: UnSupervised Learning Approach for Dynamic Dendrite Sampling”. arXiv:1807.02233.
  3. Y. Zhang, et al. (2018) “ SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks”. IS&T International Symposium on Electronic Imaging.
  4. Y. Zhang, et al. (2017) “Deep learning, dynamic sampling and smart energy-dispersive spec- troscopy”. Frontiers in Optics, OSA.
  5. Y. Zhang, et al. (2017) “Under-sampling and image reconstruction for scanning electron micro- scopes”. Microscopy and Microanalysis, MSA.
  6. Y. Zhang, et al. (2015) “A new pre-processing method for scanning x-ray microdiffraction patterns”. IEEE Biomedical Circuits and Systems Conference.
  7. Y. Zhang and L. Makowski (2015) “Auto-thresholding edge detector for bio-image processing”. The 41st Northeast Bioengineering Conference, IEEE.
  8. L. Yu, Y. Zhang, et al. (2015) “High performance computing of fiber scattering simulation”. The 8th Workshop on General Purpose Processing using GPUs, ACM.
  9. Y. Zhang, et al. (2014) “Fast simulation of x-ray diffraction patterns from cellulose fibrils using GPUs”. The 40th Northeast Bioengineering Conference, IEEE.