Simon Zhang

I am a Postdoctoral Scholar in the Computer Science and Engineering Department at the Ohio State University.

I received my Ph.D. in Computer Science from Purdue University formally advised by Professor Tamal Dey and my M.S. from the Department of Computer Science and Engineering at the Ohio State University. I received my B.S. in Computer Science from Cornell University.

My research focus is in the areas of machine learning, algorithms, graph/connected data, data compression, topological data analysis, and high performance computing.

news

Jan 30, 2025	I will be joining Dr. Guo’s lab as a Postdoctoral Scholar.
Dec 15, 2024	I graduated from Purdue University with my Ph.D. in Computer Science.

publications

Computing and Learning on Combinatorial Data

Simon Zhang

Purdue University Graduate School, 2025

Bib PDF

TMLR

Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking

Simon Zhang, Cheng Xin, and Tamal K. Dey

Transactions on Machine Learning Research, 2024

Bib HTML Code

@article{zhang2024expressive,
  title = {Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking},
  author = {Zhang, Simon and Xin, Cheng and Dey, Tamal K.},
  journal = {Transactions on Machine Learning Research},
  issn = {2835-8856},
  year = {2024},
}

ICML

Adversarial Data Augmentations for Out-of-Distribution Generalization

Simon Zhang, Ryan P DeMilt, Kun Jin, and 1 more author

Second Workshop on Spurious Correlations, Invariance and Stability (ICML 2023), 2023

Bib

@article{zhangadversarial,
  title = {Adversarial Data Augmentations for Out-of-Distribution Generalization},
  author = {Zhang, Simon and DeMilt, Ryan P and Jin, Kun and Xia, Cathy H},
  journal = {Second Workshop on Spurious Correlations, Invariance and Stability (ICML 2023)},
  year = {2023}
}

LoG

GEFL: extended filtration learning for graph classification

Simon Zhang, Soham Mukherjee, and Tamal K Dey

In Learning on Graphs Conference, 2022

Bib HTML Code

@inproceedings{zhang2022gefl,
  title = {GEFL: extended filtration learning for graph classification},
  author = {Zhang, Simon and Mukherjee, Soham and Dey, Tamal K},
  booktitle = {Learning on Graphs Conference},
  pages = {16--1},
  year = {2022},
  organization = {PMLR},
}

ALENEX

Approximating 1-Wasserstein Distance between Persistence Diagrams by Graph Sparsification∗

Tamal K Dey, and Simon Zhang

In 2022 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX), 2022

Bib HTML Code

@inproceedings{dey2022approximating,
  title = {Approximating 1-Wasserstein Distance between Persistence Diagrams by Graph Sparsification∗},
  author = {Dey, Tamal K and Zhang, Simon},
  booktitle = {2022 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX)},
  pages = {169--183},
  year = {2022},
  organization = {SIAM},
}

SoCG

GPU-Accelerated Computation of Vietoris-Rips Persistence Barcodes

Simon Zhang, Mengbai Xiao, and Hao Wang

In 36th International Symposium on Computational Geometry (SoCG 2020), 2020

Bib HTML Code

@inproceedings{zhang2020gpu,
  title = {GPU-Accelerated Computation of Vietoris-Rips Persistence Barcodes},
  author = {Zhang, Simon and Xiao, Mengbai and Wang, Hao},
  booktitle = {36th International Symposium on Computational Geometry (SoCG 2020)},
  year = {2020},
  organization = {Schloss-Dagstuhl-Leibniz Zentrum f{\"u}r Informatik},
}

ICS

HYPHA: a framework based on separation of parallelisms to accelerate persistent homology matrix reduction

Simon Zhang, Mengbai Xiao, Chengxin Guo, and 3 more authors

In Proceedings of the ACM International Conference on Supercomputing, 2019

Bib PDF Code

@inproceedings{zhang2019hypha,
  title = {HYPHA: a framework based on separation of parallelisms to accelerate persistent homology matrix reduction},
  author = {Zhang, Simon and Xiao, Mengbai and Guo, Chengxin and Geng, Liang and Wang, Hao and Zhang, Xiaodong},
  booktitle = {Proceedings of the ACM International Conference on Supercomputing},
  pages = {69--81},
  year = {2019},
  organization = {ACM},
}

SoCC

DOT: a matrix model for analyzing, optimizing and deploying software for big data analytics in distributed systems

Yin Huai, Rubao Lee, Simon Zhang, and 2 more authors

In Proceedings of the 2nd ACM Symposium on Cloud Computing, 2011

Bib PDF

@inproceedings{huai2011dot,
  title = {DOT: a matrix model for analyzing, optimizing and deploying software for big data analytics in distributed systems},
  author = {Huai, Yin and Lee, Rubao and Zhang, Simon and Xia, Cathy H and Zhang, Xiaodong},
  booktitle = {Proceedings of the 2nd ACM Symposium on Cloud Computing},
  pages = {1--14},
  year = {2011},
}