
Cheng Mao
استادیار · statistical inference on random graphs
Georgia Institute of Technologyمعرفی
Cheng Mao is an Assistant Professor in the School of Mathematics at the Georgia Institute of Technology (Georgia Tech), with office location in Skiles 102C and mailing address at 686 Cherry Street, Atlanta, GA 30332 USA. His position is active as of 2025, with no indication of part-time status or retirement.
Education:
- B.S. and M.A. in Mathematics, University of California, Los Angeles (UCLA), 2013
- Ph.D. in Mathematics and Statistics, Massachusetts Institute of Technology (MIT), 2018 (Advisor: Philippe Rigollet)
- Postdoctoral Researcher, Yale University, 2018-2019 (with Yihong Wu)
Cheng Mao's research centers on the mathematics of data science, specifically statistical inference for random graphs. His work bridges mathematical statistics, applied probability, and theoretical computer science to address fundamental problems in graph matching, community detection, and matrix estimation under structural constraints like total positivity. He investigates algorithmic efficiency, information-theoretic limits, and the detection-recovery gap in planted models.
His publication trends (2020-2025) reveal deep specialization in spectral methods for graph matching (e.g., Erdős-Rényi graphs, noise robustness) and combinatorial approaches to planted structures (dense cycles, subhypergraphs). Key themes include leveraging low-degree polynomials for detection, tree counting for network correlation, and method-of-moments techniques for permutation learning. Matrix estimation under Monge and total positivity constraints forms another consistent thread.
Scientific Awards:
- No scientific awards mentioned in the provided text
Cheng Mao advises three Ph.D. students at Georgia Tech: Jingyi (Joy) Zhang (co-advised with Debankur Mukherjee), Shenduo Zhang, and Abhishek Dhawan (Ph.D. 2024, now postdoc at UIUC, co-advised with Anton Bernshteyn). Timothy Wee joined as a postdoc in 2024. The text contains no references to research grants or external funding sources.
No laboratories, research teams, or collaborative groups are specified in the source material.
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