World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5850
World Ranking
12514
National Ranking
5079

Mathematics

D-Index
32
Citations
5488
World Ranking
3150
National Ranking
1263

Overview

Faming Liang is affiliated with Purdue University West Lafayette in the United States. Their research spans the fields of Computer Science and Mathematics, with a significant focus on specialized subfields including Artificial Intelligence, Statistics and Probability, Computer Vision and Pattern Recognition, Molecular Biology, and Statistical and Nonlinear Physics.

The main topics covered in their work include Markov Chains and Monte Carlo Methods, Gaussian Processes and Bayesian Inference, Statistical Methods and Inference, Stochastic Gradient Optimization Techniques, Model Reduction and Neural Networks, Generative Adversarial Networks and Image Synthesis, and Gene Expression and Cancer Classification.

Faming Liang has contributed to various publication venues, with numerous articles appearing on arXiv (Cornell University). Other frequent venues include Statistica Sinica, Journal of Computational and Graphical Statistics, Journal of the American Statistical Association, and Journal of Statistical Computation and Simulation.

Notable recent papers authored by or coauthored with Faming Liang include:

  • "Nearly optimal Bayesian shrinkage for high-dimensional regression," 2022, published in Science China Mathematics
  • "Nonlinear Variable Selection via Deep Neural Networks," 2020, Journal of Computational and Graphical Statistics
  • "Consistent Sparse Deep Learning: Theory and Computation," 2021, Journal of the American Statistical Association
  • "Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection," 2020, Biometrika
  • "Non-convex Learning via Replica Exchange Stochastic Gradient MCMC," 2020, PubMed

Faming Liang frequently collaborates with several coauthors, including Yan Sun, Qifan Song, Sehwan Kim, Wei Deng, and Guang Lin. These collaborations have resulted in numerous joint publications contributing to the overview of their research topics and methodologies.

Best Publications

  • The Multiple-Try Method and Local Optimization in Metropolis Sampling

    Jun S. Liu;Faming Liang;Wing Hung Wong

  • Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples

    Faming Liang;Chuanhai Liu;Raymond J. Carroll

  • Stochastic Approximation in Monte Carlo Computation

    Faming Liang;Chuanhai Liu;Raymond J Carroll

  • Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models

    Faming Liang;Wing Hung Wong

  • Evolutionary Monte Carlo for protein folding simulations

    Faming Liang;Wing Hung Wong

  • Crash Injury Severity Analysis Using Bayesian Ordered Probit Models

    Yuanchang Xie;Yuanchang Xie;Yunlong Zhang;Yunlong Zhang;Faming Liang;Faming Liang

  • Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis

    Xin Luo;Xiao Zang;Lin Yang;Lin Yang;Junzhou Huang

  • A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants

    Faming Liang

  • Statistical and Computational Inverse Problems

    Faming Liang;Jianhua Huang

  • Enhanced construction of gene regulatory networks using hub gene information.

    Donghyeon Yu;Johan Lim;Xinlei Wang;Faming Liang

  • Bayesian neural networks for nonlinear time series forecasting

    Faming Liang

  • Dynamic weighting in Monte Carlo and optimization

    Wing Hung Wong;Faming Liang

  • Estimating uncertainty of streamflow simulation using Bayesian neural networks

    Xuesong Zhang;Faming Liang;Raghavan Srinivasan;Michael Van Liew

  • Bayesian Neural Networks for Selection of Drug Sensitive Genes

    Faming Liang;Qizhai Li;Lei Zhou

  • Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

    Faming Liang;Qifan Song;Kai Yu

  • A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data

    Faming Liang;Yichen Cheng;Qifan Song;Jincheol Park

  • A split‐and‐merge Bayesian variable selection approach for ultrahigh dimensional regression

    Qifan Song;Faming Liang

  • Markov Chain Monte Carlo: Innovations and Applications

    W S Kendall;F Liang;J-S Wang

  • A Generalized Wang–Landau Algorithm for Monte Carlo Computation

    Faming Liang

  • Dynamically Weighted Importance Sampling in Monte Carlo Computation

    Faming Liang

  • Crash Injury Severity Analysis Using a Bayesian Ordered Probit Model

    Yunlong Zhang;Faming Liang;Yuanchang Xie

Frequent Co-Authors

Raymond J. Carroll
Raymond J. Carroll Texas A&M University
Wing Hung Wong
Wing Hung Wong Stanford University
Guang Lin
Guang Lin Purdue University West Lafayette
Jun S. Liu
Jun S. Liu Harvard University
Pieter A. Doevendans
Pieter A. Doevendans Utrecht University
Yunlong Zhang
Yunlong Zhang Texas A&M University
Jason Cong
Jason Cong University of California, Los Angeles
Diederick E. Grobbee
Diederick E. Grobbee Utrecht University
Yolanda van der Graaf
Yolanda van der Graaf Utrecht University
Deborah A. Nickerson
Deborah A. Nickerson University of Washington

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For mathematics graduates seeking to expand their skill set, pursuing a data analytics masters can open doors to high-demand careers in business intelligence, finance, and technology. This specialization leverages mathematical proficiency to analyze large datasets and drive strategic decisions.

Many students interested in leadership roles consider business-focused programs. For those aiming to enter management with flexible entry requirements, exploring the easiest mba programs to get into offers a practical option without sacrificing quality. These programs balance accessibility with comprehensive business education.

Online learning continues to grow, making the easiest online mba programs to get into attractive for working professionals. Such programs combine convenience with curriculum designed to build leadership, strategy, and financial skills relevant to a math background.

For those with entrepreneurial ambitions or aiming for executive roles, a Doctorate in Business Administration can be transformative. Finding the cheapest dba online programs helps balance advanced learning with budget considerations, enabling deeper expertise in business strategy and research.

Best Scientists Citing Faming Liang

Trending Scientists

Recently Published Articles