Ning Zhang

About Me

I am a DPhil in the statistics department at the University of Oxford (Computational Discovery CDT), and my supervisors are Prof. Mihai Cucuringu and Prof. Xiaowen Dong. Prior to that, I received my MASc in Electrical and Computer Engineering from the University of British Columbia, and BSc in Physics from Nankai University. [Curriculum Vitae]
Contact: ning.zhang[at]stats[dot]ox[dot]ac[dot]uk

Research

My research interest lies in the intersection of statistics and computation. Currently, I am focusing on better understanding computational tasks on graphs using tools such as probability theory, statistics, spectral methods, optimization, etc. My research involves proposing data-driven algorithms together with mathematical proofs, and at the same time, I seek to understand the nature of the problems and algorithms through the lens of those proofs. I am excited to see the transformation of ideas across different research fields.

Publications and preprints

• Maximum Likelihood Estimation on Stochastic Blockmodels for Directed Graph Clustering
  Mihai Cucuringu, Xiaowen Dong and Ning Zhang.
  [arXiv][Code]

• On the Feasible Region of Efficient Algorithms for Attributed Graph Alignment
  Ziao Wang, Ning Zhang, Weina Wang, and Lele Wang.
  IEEE Transactions on Information Theory [arXiv]

• Attributed Graph Alignment
  Ning Zhang, Weina Wang, and Lele Wang.
  IEEE International Symposium on Information Theory (ISIT 2021) [arXiv]

• Investigating the depolarization property of skin tissue by degree of polarization uniformity contrast using polarization-sensitive optical coherence tomography
  Xin Zhou, Sina Maloufi, Daniel C. Louie, Ning Zhang, Qihao Liu, Tim K. Lee, and Shuo Tang.
  Biomedical Optics Express (2021)

• A spatially constrained deep convolutional neural network for nerve fiber segmentation in corneal confocal microscopic images using inaccurate annotations
  Ning Zhang, Susan Francis, Rayaz A. Malik and Xin Chen.
  IEEE International Symposium on Biomedical Imaging (ISBI 2020). [Code]
  
  

Talks

• Nov. 2023: Maximum Likelihood Estimation on Stochastic Blockmodels for Directed Graph Clustering at the 12th International Conference on Complex Networks and their Applications
• Jun. 2021: Attributed graph alignment at IEEE International Symposium on Information Theory;

Teaching

• Michaelmas term 2023, Probability and Statistics for Network Analysis
• Spring 2022, STAT321 Stochastic Signals and Systems
• Fall 2021, STAT321 Stochastic Signals and Systems
• Spring 2021, STAT321 Stochastic Signals and Systems
• Fall 2020, STAT321 Stochastic Signals and Systems
• Spring 2020, ELEC291 Electrical Engineering Design Studio I