Anh T. Bui

Anh Tuan Bui, PhD

Associate Professor of Statistics

Department of Mathematics and Statistics

Virginia Commonwealth University

Office: Grace E. Harris Hall, Room 4132, 1015 Floyd Avenue, Richmond, VA 23284

Email: buiat2@vcu.edu

Welcome

Thank you for visiting my webpage. I am looking for graduate students with statistics/machine learning background or with a strong passion on these topics. Undergraduate/high-school students are encouraged to contact me to explore research opportunities in statistics, machine learning, and/or healthcare.

Education

Ph.D., Industrial Engineering & Management Sciences, Northwestern University

M.S., Industrial and Management Engineering, Pohang University of Science and Technology

B.S., Electrical Engineering, Hanoi University of Science and Technology

Research (Google Scholar)

I am a bio/engineering statistician specializing in machine learning/artificial intelligence (ML/AI). My broad research interests are developing methodology, solutions, and software for improving the understanding and quality of complex systems such as manufacturing and health care. My past and current research can be categorized into two major lines. The first line (ML/AI) mainly revolves around data reduction related problems. The second line (Liver Transplant) contributes to the ongoing efforts to lengthen the lifespan of liver-transplanted patients via statistics and ML/AI.

ML/AI: Modern data offer unprecedented opportunities for innovation and efficiency in research and across industries. However, they are often large, complex, and potentially contaminated. Hence, data reduction is crucial for ensuring accuracy, scalability, and meaningful insights that might be obscured by data redundancy. This research line aims to address data reduction related problems, particularly representation learning (RL), dimension reduction (DR), and data sampling (DS). While DR and DS focus on reducing the d and the n of data (respectively), RL finds compact and manageable representations of the data, for example via models, dissimilarities, or statistics.

(* indicates a PhD student under my supervision)

(± indicates an undergrad student)

(ǂ indicates shared contribution)

Bui, A.T. (2026). Conditional Multidimensional Scaling for Incomplete Conditioning Data. Journal of Multivariate Analysis, 214, 105620. arXiv.
Muldagi, S.*, & Bui, A.T. (2026). Scalable Data Twinning. Pattern Recognition Letters, 199, 34–39. DOI. Full text.
Li, L.*, & Bui, A.T. (2025). Variation Analysis for Custom Manufacturing Processes. Quality Engineering, 37, 37-51. DOI. Full text.
Bui, A.T. (2024). Dimension Reduction with Prior Information for Knowledge Discovery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 3625–3636. DOI.
Ling, X., Bui, A.T.ǂ, & Brooks, J.P.ǂ (2024). Kernel L1-Norm Principal Component Analysis for Denoising. Optimization Letters, 18, 2133–2148. DOI.
Bui, A.T. (2024). Root Cause Analysis of Manufacturing Variation from Optical Scanning Data. Annals of Operations Research, 339, 111–130. DOI.
Zhang, K., Bui, A.T., & Apley, D.W. (2023). Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors. Technometrics, 65, 137–149. DOI. Full text. This paper receives the Frank Wilcoxon Award.
Bui, A.T. (2022). A Closed-Form Solution for Conditional Multidimensional Scaling. Pattern Recognition Letters, 164, 148–152. DOI. Full text.
Bui, A.T., & Apley, D.W. (2022). Analyzing Nonparametric Part-to-part Variation in Surface Point Cloud Data. Technometrics, 64, 457–474. DOI. Full text.
Bui, A.T., & Apley, D.W. (2022). Robust Monitoring of Stochastic Textured Surfaces. International Journal of Production Research, 60, 5071–5086, DOI. Full text.
Bui, A.T., & Apley, D.W. (2021). spc4sts: Statistical Process Control for Stochastic Textured Surfaces in R. Journal of Quality Technology, 53, 219–242. DOI. Full text.
Bui, A.T., & Apley, D.W. (2019). An Exploratory Analysis Approach for Understanding Variation in Stochastic Textured Surfaces. Computational Statistics & Data Analysis, 137, 33−50. DOI. Full text.
Bui, A.T., Im, J.-K., Apley, D.W., & Runger, G.C. (2019). Projection-Free Kernel Principal Component Analysis for Denoising. Neurocomputing, 357, 163–176. DOI. Full text.
Bui, A.T., & Apley, D.W. (2018). Monitoring for Changes in the Nature of Stochastic Textured Surfaces. Journal of Quality Technology, 50, 363−378. DOI. Full text. This paper receives the Lloyd S. Nelson Award.
Bui, A.T., & Apley, D.W. (2018). A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces. Technometrics, 60, 1−13. DOI. Full text.
Bostanabad, R., Bui, A.T., Xie, W., Apley, D.W., & Chen, W. (2016). Stochastic Microstructure Characterization and Reconstruction via Supervised Learning. Acta Materialia, 103, 89−102. DOI. Full text.
Bui, A.T., & Jun, C.-H. (2012). Learning Bayesian Network Structure using Markov Blanket Decomposition. Pattern Recognition Letters, 33, 2134−2140. DOI. Full text.
Bui, A.T., & Jun, C.-H. (2012). An Improved Iterative Procedure for Outlier Detection in Time Series. Journal of the Korean Institute of Industrial Engineers, 38, 17−24.

Liver transplant: The only therapy to enhance survival for patients with liver disease at the end stage is liver transplantation. However, the low long-term survival rate of LT patients remains a serious problem. This research line aims to address this problem and improve the quality of life for LT patients via statistics and ML/AI. There are currently two main themes in this research line: biomarker models and metabolic health.

(* indicates a PhD student under my supervision)

(⁑ indicates an internal medicine resident under my mentoring)

(⁂ indicates a medical student under my mentoring)

(# indicates shared contribution as senior authors)

Selected Articles

Kim, A.⁑, Li, L.*, Siddiqui, M.S.#, & Bui, A.T.# (2026). Reply: Weight Trajectories Post-Liver Transplantation Using Generative AI Methodology Further Explained. Liver Transplantation. Accepted.
Kim, A.⁑, Li, L.*, Siddiqui, M.S.#, & Bui, A.T.# (2026). Reply: The application of generative artificial intelligence in liver transplantation weight trajectory. Liver Transplantation, 32:E50-E51
Kim, A.⁑, Li, L.*, Hamraz, R., Nguyen, M.⁑, Razzaq, R., Patel, V., Ashgharpour, A., Yakubu, I., Garland, Parmar, K., Muthiah, M., Lim, W.H., Ng, C.H., Patel, S., Siddiqui, M.S.#, & Bui, A.T.# (2026). Defining Weight Trajectory After Liver Transplantation Using Generative Artificial Intelligence. Liver Transplantation, 32:890-900.
Parmar, K., Bui, A.T., Momin, T., Patel, V., Siddiqui, M.S. (2024). Metabolic Flexibility Predicts Response to Saroglitazar in Liver Transplant Recipients with Metabolic Dysfunction Associated Steatotic Liver Disease. Transplantation, 109, e222-e223. DOI.
Baral, A., Garg, S., Nguyen, M., Razzaq, R., Ang, A., Khan, H., Vainer, D., Arshad, T., Patel, V., Roche, G., Muthiah, M., Bui, A.T.#, & Siddiqui, M.S.# (2024). Vibration Controlled Transient Elastography Based Parameters Predicts Clinical Outcomes in Liver Transplant Recipients. Clinical Gastroenterology and Hepatology, 22, 2424-2431.e4. DOI.
Arshad, T., Vainer, D., Khan, H., Baral, A. Garg, S., Ang, A., Patel, V., Kumaran, V., Bruno, D., Lee, S., Sharma, A., Muthiah, M., Bui, A.T.#, & Siddiqui, M.S.# (2024). Development of Clinical Algorithm Utilizing Vibration Controlled Transient Elastography to Detect Advanced Hepatic Fibrosis in Liver Transplant Recipients. Digestive Diseases and Sciences, 69, 1844-1851. DOI.
Bui, A.T., Chaudhari, R., Bhati, C., Wolver, S., Patel, S., Boyett, S., Evans, M.C., Kamal, H., Patel, V., Forsgren, M., Kirkman, D., Siddiqui, M.S. (2024). Reduced Metabolic Flexibility is a Predictor of Weight Gain Among Liver Transplant Recipients. Liver Transplantation, 30, 192-199. DOI.
Arshad T., Bhati, C.S., Bui, A.T., Tseng, M., Austin, M., Evans, M.-C., Syed, T., Muthiah, M., Patel, V., & Siddiqui, M.S. (2023). Evaluation of Liver Stiffness Measurement Based Scores in Liver Transplant Recipients. Liver Transplantation, 29, 196-205. DOI.
Siddiqui, M.S., Bui, A.T., Syed, T., Tseng, M., Hassouneh, R., Bhati, C.S., Liver Transplant Biomarker Group (2023). Performance of Vibration Controlled Transient Elastography and Clinical Prediction. Clinical Gastroenterology and Hepatology, 21, 1100-1102. DOI.

Teaching

STAT 791: Advanced Machine Learning
STAT 791: Nonparametric Learning
STAT 636: Machine Learning Algorithms
STAT 625: Applied Multivariate Analysis
STAT 621: Nonparametric Statistical Methods
SYSM 682/683: Systems Seminar II/III
STAT 534: Statistical Data Science I
STAT 514: Mathematical Statistics II
STAT 435: Industrial Statistics
STAT 423: Nonparametric Statistics
STAT 210: Basic Practice of Statistics

Software & Data

Software:

cml R package: Performs conditional manifold learning, conditioning on available manifold information. Available on CRAN.
spc4sts R package: Provides statistical process control tools for stochastic textured surfaces. Available on CRAN.

Data:

textile R package: Contains real textile images with and without local defects, at a scale that shows their weave patterns. Available on CRAN.
textile2 R package: Contains 350 real textile images (of size 500x500 pixels). They were taken at non-overlapping and adjacent regions of two swatches of the same textile material, at a scale that shows their weave patterns. Available on Mendeley.
textile3 R package: Contains a set of 100 textile images (500x500 pixels) that contains two systematic variation patterns. These variation patterns were created by digitally contracting 100 images of the same textile material in the horizontal and vertical directions. Available on figshare.

Awards

Frank Wilcoxon Award (the American Society for Quality)
Lloyd S. Nelson Award (the American Society for Quality)
Pritsker Doctoral Dissertation Award (3rd place, the Institute of Industrial & Systems Engineers)
Mary G. and Joseph Natrella Scholarship (the Statistical American Association)
Terminal Year Fellowship (Northwestern University)
Benjamin K. Sachs Graduate Fellowship (Northwestern University)
Vietnam Education Foundation Fellowship (the United States government)