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Mingqi Wu
Mingqi Wu
Data Scientist, Microsoft
Verified email at microsoft.com
Title
Cited by
Cited by
Year
Pseudoclimb and dislocation dynamics in superplastic nanotubes
F Ding, K Jiao, M Wu, BI Yakobson
Physical review letters 98 (7), 075503, 2007
1512007
Weak convergence rates of population versus single-chain stochastic approximation MCMC algorithms
Q Song, M Wu, F Liang
Advances in Applied Probability 46 (4), 1059-1083, 2014
202014
A data-driven workflow for predicting horizontal well production using vertical well logs
J Guevara, M Kormaksson, B Zadrozny, L Lu, J Tolle, T Croft, M Wu, ...
arXiv preprint arXiv:1705.06556, 2017
122017
A hybrid data-driven and knowledge-driven methodology for estimating the effect of completion parameters on the cumulative production of horizontal wells
J Guevara, B Zadrozny, A Buoro, L Lu, J Tolle, J Limbeck, M Wu, D Hohl
SPE Annual Technical Conference and Exhibition, 2018
72018
Bayesian modeling of ChIP-chip data using latent variables
M Wu, F Liang, Y Tian
BMC bioinformatics 10 (1), 1-13, 2009
52009
Stochastic clustering and pattern matching for real-time geosteering
M Wu, Y Miao, N Panchal, DR Kowal, M Vannucci, J Vila, F Liang
Geophysics 84 (5), ID13-ID24, 2019
32019
Population SAMC vs SAMC: Convergence and applications to gene selection problems
M Wu, F Liang
J Biomet Biostat S 1 (2), 2011
22011
Population stochastic approximation mcmc algorithm and its weak convergence
F Liang, M Wu
Technical Report, 2010
22010
Accelerate training of restricted Boltzmann machines via iterative conditional maximum likelihood estimation
FL Mingqi Wu, Ye Luo
Statistics and its interface 12 (3), 377, 2019
12019
Supplementary material for ‘Weak convergence rates of population versus single-chain stochastic approximation MCMC algorithms’
Q SONG, M WU, F LIANG
12013
Process for real time geological localization with stochastic clustering and pattern matching
N Panchal, SMK Sultan, M Wu
US Patent App. 17/264,005, 2021
2021
An interpretable machine learning methodology for well data integration and sweet spotting identification
J Guevara, B Zadrozny, A Buoro, J Tolle, J Limbeck, M Wu, D Hohl
NIPS 2018 Workshop book, 2018
2018
Model-Free Inference for ChIP-Seq Data
M Wu, M Rijnkels, F Liang
Journal of Data Mining in Genomics & Proteomics 5 (2), 1, 2014
2014
Population SAMC vs SAMC: Convergence and Applications to Gene Selection Problems
MW Faming Liang
OMICS Publishing Group, 2013
2013
Population SAMC, ChIP-chip Data Analysis and Beyond
M Wu
Texas A & M University, 2011
2011
Testing Multiple Hypotheses Using Population Information of Samples
M Wu, F Liang
2009
Bayesian Modeling of ChIP-chip Data Using Latent Variables
F Liang, M Wu, Y Tian
2008
Bayesian modeling of ChIP-chip data using latent variables Additional file
M Wu, F Liang, Y Tian
Testing multiple hypotheses using population information of samples (Supplementary materials)
M Wu, F Liang
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Articles 1–19