Scholar
Ming Gao
Google Scholar ID: 3ag2nJkAAAAJ
The University of Chicago
Citations & Impact
Citations
158
H-index
6
i10-index
5
Publications
11
Co-authors
0
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- [{'PaperTitle': 'KL-BSS: Rethinking optimality for neighbourhood selection in structural equation models', 'Authors': 'M Gao, WM Tai and B Aragam', 'Status': 'Reject & Resubmit invited at JRSS-B, 2025'}, {'PaperTitle': 'Optimal structure learning and conditional independence testing', 'Authors': 'M Gao, Y Wang and B Aragam', 'Status': 'Submitted, 2025'}, {'PaperTitle': 'Optimality and computational barriers in variable selection under dependence', 'Authors': 'M Gao and B Aragam', 'Status': 'Submitted, 2025'}, {'PaperTitle': 'Optimizing return forecasts: A Bayesian intermediary asset pricing approach', 'Authors': 'M Gao and C Zhang', 'Status': 'Submitted, 2024 (Presented at SFS Cavalcade North America 2025)'}, {'PaperTitle': 'Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior', 'Authors': 'J Du, T Chen, M Gao and J Wang', 'Status': 'PNAS, 2024'}, {'PaperTitle': 'Optimal estimation of Gaussian (poly)trees', 'Authors': 'Y Wang, M Gao, WM Tai, B Aragam and A Bhattacharyya', 'Status': 'AISTATS, 2024'}, {'PaperTitle': 'Multivariate change point detection for heterogeneous series', 'Authors': 'Y Guo, M Gao and X Lu', 'Status': 'Neurocomputing, 2022'}, {'PaperTitle': 'Optimal estimation of Gaussian DAG models', 'Authors': 'M Gao, WM Tai and B Aragam', 'Status': 'AISTATS, 2022'}, {'PaperTitle': 'Efficient Bayesian network structure learning via local Markov boundary search', 'Authors': 'M Gao and B Aragam', 'Status': 'NeurIPS, 2021'}, {'PaperTitle': 'Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families', 'Authors': 'G Rajendran, B Kivva, M Gao and B Aragam', 'Status': 'NeurIPS, 2021'}, {'PaperTitle': 'A polynomial-time algorithm for learning nonparametric causal graphs', 'Authors': 'M Gao, Y Ding and B Aragam', 'Status': 'NeurIPS, 2020'}]
Background
- PhD student in Econometrics and Statistics at the University of Chicago Booth School of Business, with research interests including structural equation models, variable selection, and Bayesian networks.
Miscellany
- Hobbies: Working out, tea, sunset and red leaves.
Co-authors