About

Peihua Qiu is a professor with a focus on statistical modeling, image analysis, and biomedical data analysis. His research encompasses a variety of topics including observer agreement measurements, multivariate longitudinal binary data, edge detection, image segmentation, and applications in microarray image analysis. He has supervised numerous students and scholars, both at the University of Minnesota and the University of Florida, and has been involved in collaborative research projects supported by NSF grants and China Scholarship Council programs. His work also involves the development of statistical methodologies for early warning systems, spatio-temporal pattern monitoring of infectious diseases, and various image registration and denoising techniques, reflecting a broad expertise in statistical and computational methods for complex data analysis.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Data Mining
• Mathematics
• Statistics
• Econometrics
• Psychology
• Programming language
• Psychiatry
• Neuroscience
• Gerontology
• Medicine

Selected publications

• Nicotinamide riboside combined with exercise to treat hypertension in middle-aged and older adults: a pilot randomized clinical trial

  GeroScience · 2025-08-06 · 4 citations

  articleOpen access

  Aerobic exercise lowers blood pressure (BP) with varying effects in hypertensive adults, potentially due to age-related nicotinamide adenine dinucleotide (NAD) metabolism dysregulation. This pilot randomized clinical trial (RCT) tested the efficacy of combining aerobic exercise with the NAD booster nicotinamide riboside (NR) to enhance BP control. In this double-blinded RCT, 54 sedentary adults (≥ 55 years) with mean daytime systolic BP (SBP) ≥ 130 mmHg were randomized to 6 weeks of 1000 mg/day of NR combined with 3 days/week of supervised 30-min walking exercise (NR + Ex), Placebo combined with the same exercise regimen (PL + Ex), or NR alone (NR). The primary outcome was daytime SBP. Other outcomes included pulse wave velocity (PWV), NAD catabolites, and nighttime BP. The primary comparison was between NR + Ex and PL + Ex. Of 54 participants (mean age 67 years, 61% female), 49 (NR + Ex: n = 15, PL + Ex: n = 16, NR: n = 18) completed all study visits (93% adherence to exercise and 90% to supplementation). NR + Ex (mean change = 5.19 ± 13.2 mmHg) did not reduce SBP more than PL + Ex (- 2.71 ± 10.5 mmHg). NR + Ex (- 0.31 ± 0.77 m/s) showed a trend toward a greater reduction in PWV. Levels of NAD catabolites were higher in NR groups. In a post hoc analysis, NR + Ex showed a trend toward greater nighttime BP reductions (systolic: - 9.6 ± 9.22; diastolic: - 4.51 ± 7.12 mmHg) in participants without antihypertensive medications. NR + Ex was not superior to PL + Ex in reducing BP in hypertensive middle-aged and older. However, trends toward greater nighttime BP reduction in NR + Ex in participants without antihypertensive medication warrant further investigation in a Phase IIb RCT.

  PublisherOA PDFDOI

• Bayesian Pairwise Comparison of High-Dimensional Images

  Journal of Computational and Graphical Statistics · 2025-01-06 · 1 citations

  articleOpen accessSenior author

  (sRPM). The process groups spatially proximal image pixels with similar intensities into clusters, thereby achieving dimension reduction in the large number of pixels. Next, we apply the sRPM-based analytical procedure to compare two images. The image comparison problem is formulated as a hypothesis test involving a univariate metric adaptive to spatial correlations and robust to random variability in the pixel intensities. To handle the computational burden, we foster a two-stage technique for MCMC analysis and hypothesis testing of image pairs. A simulation study analyzes artificial datasets and finds compelling evidence for the high accuracy of sRPM in image comparison. We demonstrate the effectiveness of the technique by statistically analyzing satellite image data.

  PublisherOA PDFDOI

• Trends and Characteristics of Early-Onset Colorectal Cancer in the State of Florida, 2002-2021

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• An improved bisection-type algorithm for control chart calibration

  Statistics and Computing · 2025-04-24

  articleSenior author
  PublisherDOI

• Online monitoring and early detection of influenza outbreaks using exponentially weighted spatial lasso: a case study in China during 2014–2020

  Journal of Applied Statistics · 2025-07-25 · 1 citations

  articleOpen accessSenior author

  Influenza poses a persistent public health threat in China, with substantial impacts on health and the economy, especially during seasonal epidemics and emerging outbreaks. Seasonality, local clustering, and serial correlation inherent in influenza data introduce spatio-temporal complexities that traditional statistical process control (SPC) methods cannot adequately capture. This study introduces a novel nonparametric framework for real-time influenza monitoring across 300+ Chinese cities from 2014 to 2020. Reference periods are selected to establish baseline incidence patterns and fit a nonparametric spatio-temporal model to estimate mean and covariance structures. These estimates enable the setting of dynamic outbreak thresholds. Next, exponentially weighted spatial LASSO (EWSL) charting statistics are computed for the monitoring period, prioritizing recent observations and detecting subtle mean shifts in small, clustered regions - well-suited to influenza's progression dynamics. Charting statistics exceeding control limits trigger timely outbreak warnings. Results demonstrate that our method consistently outperforms alternative methods, and existing literature corroborates that its early signals correspond to actual outbreaks - including those for H7N9 strains, influenza A and B viruses, and the initial spread of COVID-19. These findings highlight the potential of our approach as an effective epidemic monitoring tool, addressing complex spatio-temporal patterns and supporting timely, data-driven public health interventions.

  PublisherOA PDFDOI

• A general framework for monitoring mixed data

  Journal of Quality Technology · 2025-06-05 · 1 citations

  articleSenior authorCorresponding

  Modern applications of statistical process monitoring involve checking the stability of multivariate processes with mixed data types, such as a combination of continuous, ordinal, and categorical quality variables. Appropriate statistical modeling for such data is often challenging, especially when the observed data are serially correlated, which explains why there is only a limited existing discussion on sequential monitoring of processes with mixed data. This paper introduces a general methodology to solve the problem. The main idea behind our approach is to sequentially transform the original observed data into continuous data through innovative data pre-processing, achieved by encoding the ordinal and categorical variables into continuous numerical variables using dummy and score variables and data transformation and decorrelation. Numerical studies show that the proposed method is effective in monitoring mixed data, in comparison with some state-of-the-art existing methods. The new method is illustrated in a case study involving online monitoring of hotel customers' behaviors. Computer codes in Julia for implementing the proposed methodology are provided in the supplemental material.

  PublisherDOI

• Explainable AI for trustworthy intelligent process monitoring

  Computers & Industrial Engineering · 2025-07-29 · 14 citations

  articleOpen access

  Statistical control charts are often based on assumptions that do not hold in complex, high-dimensional and dynamic environments. To counter these weaknesses, control charts based on artificial intelligence (AI) techniques have emerged as a powerful alternative in recent years. However, their black-box nature limits transparency, interpretability and trustworthiness that are essential to realize Industry 5.0. To address that issue, this Short Communication discusses the necessity of embedding explainable artificial intelligence (XAI) in AI-based control charts. Incorporating XAI provides a solution by enhancing the interpretability of AI-based control charts while maintaining their high predictive accuracy. This paper also identifies key challenges in embedding XAI and outlines future research directions for responsible and trustworthy AI-based process monitoring. • This paper addresses the black-box nature of artificial intelligence (AI) based control charts. • It highlights the necessity of embedding explainable AI (XAI) techniques into AI-based control charts. • It reviews recent AI-based control charts and XAI techniques. • Illustrative scenarios on how XAI can improve decision-making, compliance and trust in industrial settings. • Benefits, challenges and future directions of XAI-based control charts are discussed.

  PublisherDOI

• Trends and characteristics of early-onset colorectal cancer in the state of Florida, 2002–2021

  Cancer Epidemiology · 2025-09-14

  article
  PublisherDOI

• A Dynamic Screening System for Early Detection of Multiple Interconnected Events

  Technometrics · 2025-09-12 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• A Change-Point-Detection Chart for Detecting Process Mean Drifts with an Application for Monitoring the Shape of the Salton Sea

  2025-08-09

  book-chapterSenior author
  PublisherDOI

Recent grants

• Statistical Analysis of Image Restoration and Its Applications in Magnetic Resonance Imaging

  NSF · $140k · 2007–2011

• Image Segmentation for cDNA Microarray Data and Jump-Preserving Surface Estimation

  NSF · $90k · 2004–2008

• New Methods for Sequential Monitoring of Longitudinal Patterns

  NSF · $120k · 2014–2018

• Intensity-Based Image Registration and 3-D Image Denoising

  NSF · $150k · 2010–2013

• Longitudinal Modelling and Sequential Monitoring of Image Data Streams

  NSF · $180k · 2019–2023

Frequent coauthors

• Ryan Suk

  University of Florida

  37 shared

• Timothy Wilkin

  Weill Cornell Medicine

  36 shared

• Andrew G. Sikora

  The University of Texas MD Anderson Cancer Center

  36 shared

• Ashish A. Deshmukh

  Medical University of South Carolina

  36 shared

• Alan G. Nyitray

  Medical College of Wisconsin

  36 shared

• Jagpreet Chhatwal

  Massachusetts General Hospital

  36 shared

• Kalyani Sonawane

  Medical University of South Carolina

  36 shared

• Elizabeth Y. Chiao

  Scripps MD Anderson Cancer Center

  36 shared