Anh T. Bui

Anh Tuan Bui, PhD

Assistant Professor of Statistics

Department of Statistical Sciences & Operations Research

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.

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)

My broad research interests lie in the interface between statistics and machine learning, for analyzing data from manufacturing, healthcare, and other enterprise systems. The ultimate goal is developing methodologies that help improve the understanding, quality, and productivity of these systems. A summary of my past and current research is provided below.

Statistical/machine learning methodology: This research line develops statistical and machine learning methods that are broadly applicable for a wide range of applications. The emphasis is on data reduction problems, such as dimension reduction, sampling, denoising, and outlier detection. These methods are crucial for improving the performance of machine learning models and often the go-to tool for knowledge discovery.

(ǂ indicates shared contributions)

Muldagi, S., & Bui, A.T (2025b). Alyke: A Supervised Data Splitting Approach. Under review.
Muldagi, S., & Bui, A.T (2025a). Scalable Data Twinning. Under review.
Bui, A.T. (2025). Conditional Multidimensional Scaling for Incomplete Conditioning Data. Under review.
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.
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., 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., & 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.

Statistical process control: This research line develops statistical methods for controlling the quality of manufacturing processes. The first emphasis is on online monitoring/detection of abnormal behaviors in material surfaces, which can lead to damage, reduced lifetime, or compromising aesthetics of the manufactured materials if undetected. Previous methods could only address this problem when prior knowledge of the abnormal behaviors is available. This research theme develops approaches and software for detecting these behaviors, without requiring such prior knowledge. The second emphasis is on offline/retrospective studies of the sources of manufacturing variation, which has been a primary concern in most manufacturing processes. This problem is especially challenging when the sources of the variation are unknown or previously unidentified. This research theme develops knowledge discovery approaches for identifying and understanding the variation sources.

Li, L., & Bui, A.T. (2025). Variation Analysis for Custom Manufacturing Processes. Quality Engineering, 37, 37-51. DOI. Full text.
Bui, A.T. (2024). Root Cause Analysis of Manufacturing Variation from Optical Scanning Data. Annals of Operations Research, 339, 111–130 . DOI.
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., & 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.

Biostatistics in 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 patients after liver transplant remains a serious problem. This calls for more research efforts to lengthen the lifespan of liver-transplanted patients and reduce healthcare related costs. This research line studies metabolic flexibility impairment and fibrosis biomarker models for liver transplant patients.

(# indicates shared supervision/contribution as senior author)

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. Accepted.
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. In press. 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.
Hassouneh R., Flynn, S., Shen, S., Tseng, M., Bui, A.T., Pham, J., Park, D., Matherly, S., Bruno, D., Lee, S., Kumaran, V., Patel, V., Muthiah, M., Sharma, A., & Siddiqui, M.S., (2024). Impact of Liver Transplantation on Adipose Tissue Compartments and Its Association With Metabolic Sequela. Transplantation, 108, 235-241. DOI.
Siddiqui, M.S., Parmar, D., Shaikh, F., Forsgren, M., Patel, S., Bui, A.T., Boyett, S., Patel, V., & Sanyal, A.J. (2023). Saroglitazar Improves Nonalcoholic Fatty Liver Disease and Metabolic Health in Liver Transplant Recipients. Liver transplantation, 29, 979-986. 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.
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.
Hassouneh, R., Patel, S., Shen, S., Bui, A.T., Syed, T., Flynn, S., Patel, V., Muthiah, M.D., Sharma, A., Bhati, C., Siddiqui, M.S. (2022) Glomerular Filtration Rate Early After Liver Transplantation Independently Predicts Atherosclerotic Events. Liver Transplantation, 28, 1186–1195. DOI.
Siddiqui M.S., Patel, S., Forsgren, M., Bui, A.T., Shen, S., Syed, T., Boyett, S., Chen, S., Sanyal, A., Wolver, S., Kirkman, D., Celi, F., and Bhati C. (2022). Differential Fuel Utilization in Liver Transplant Recipients and its relationship with development of NAFLD. Liver International, 42, 1401–1409. 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 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)