Theses Doctoral

Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications

Sun, Ruoxi

Developments in modern bio-imaging techniques have allowed the routine collection of a vast amount of data from various techniques. The challenges lie in how to build accurate and efficient models to draw conclusions from the data and facilitate scientific discoveries. Fortunately, recent advances in statistics, machine learning, and deep learning provide valuable tools. This thesis describes some of our efforts to build scalable Bayesian models for four bio-imaging applications: (1) Stochastic Optical Reconstruction Microscopy (STORM) Imaging, (2) particle tracking, (3) voltage smoothing, (4) detect color-labeled neurons in c elegans and assign identity to the detections.


  • thumnail for Sun_columbia_0054D_15550.pdf Sun_columbia_0054D_15550.pdf application/pdf 9.52 MB Download File
  • thumnail for thesis_appendix_RuoxiSun.pdf thesis_appendix_RuoxiSun.pdf application/pdf 3.24 MB Download File

More About This Work

Academic Units
Biological Sciences
Thesis Advisors
Paninski, Liam
Ph.D., Columbia University
Published Here
October 28, 2019