Faming Liang
Mathematics · Purdue University West Lafayette
Publications
255
Citations
5,591
Est. group size
~3
Recurring co-author estimate
Active years
30
Publishing since 1997
Faming Liang works in statistics and computational mathematics, developing methods for Markov chain Monte Carlo simulation, Bayesian modeling, and statistical inference for high-dimensional data. Much of the recent work combines these classical statistical tools with deep neural networks, addressing problems like uncertainty quantification, causal inference with missing data, and treatment effect estimation in complex, high-dimensional settings. This research is largely methodological, aimed at making machine learning models more statistically rigorous and interpretable, with applications touching clinical data analysis and brain imaging.
Publication output has been relatively steady over the past decade, with a peak around 2020 and 2023, followed by a moderate decline in the most recent years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Conformal Prediction in Clinical Artificial Intelligence
CHEST Journal · 2026
- Stochastic Neural Networks for Causal Inference with Missing Confounders
arXiv (Cornell University) · 2026
- Stochastic Neural Networks for Causal Inference with Missing Confounders
arXiv (Cornell University) · 2026
- Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
arXiv (Cornell University) · 2026
- Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
arXiv (Cornell University) · 2026
- Sublinearly structured deep neural networks achieve feature learning consistency for compositional functions
Statistical Learning and Data Science · 2026
- Extended fiducial inference for individual treatment effects via deep neural networks
Statistics and Computing · 2025
- Uncertainty Quantification for Large-Scale Deep Neural Networks via Post-StoNet Modeling
Statistica Sinica · 2025
- Sparse Graphical Models for High‐Dimensional Data
Wiley StatsRef: Statistics Reference Online · 2025
- Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks
arXiv (Cornell University) · 2025
- Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling
arXiv (Cornell University) · 2025
- Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing
Statistics in Medicine · 2024
- Extended fiducial inference: toward an automated process of statistical inference
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2024
- Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior
Journal of Data Science · 2024
- Fast Value Tracking for Deep Reinforcement Learning
arXiv (Cornell University) · 2024
- arXiv (Cornell University)×36
- Figshare×8
- PubMed×4
- Statistics and Computing×4
- Statistics and Its Interface×4
- Nianqiao Ju
Mathematics · Purdue University West Lafayette
- Vinayak Rao
Computer Science · Purdue University West Lafayette
- Ruqi Zhang
Mathematics · Purdue University West Lafayette
- Qifan Song
Mathematics · Purdue University West Lafayette
- Antik Chakraborty
Computer Science · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile