Faculty
Xiaohui Chang
Associate Professor of Business Analytics
Business Analytics

Xiaohui Chang

541-737-6108

xiaohui.chang@oregonstate.edu

Faculty
Xiaohui Chang
Overview
Overview
Background
Publications

Overview

Biography

Xiaohui Chang (pronounced as "Shao-Way Chung") obtained her Ph.D. from the University of Chicago and has been with OSU since 2014.

Her research interests include machine learning, spatial statistics, mHealth, fake news, location-based services, and business performance prediction. She is interested in developing novel and efficient methods for important big-data applications in business analytics, information systems, finance, and environmental science.

Her research work has been published in premier and high-impact journals, such as Journal of the American Statistical Association, Journal of Machine Learning Research, IEEE Transactions on Information Theory, Biometrics, Computational Statistics & Data Analysis, Quantitative Finance, Expert Systems with Applications, Information Technology & People. 

In her spare time, she enjoys outdoor activities, staying healthy and active, learning and discussing issues related to business and education. 

Credentials

Ph.D. Statistics, University of Chicago

Career Interests

Xiaohui Chang (pronounced as "Shao-Way Chung") is a Toomey Faculty Fellow and Associate Professor of Business Analytics in the College of Business, OSU. Dr. Chang earned her bachelor's degrees in Economics and Statistics (Honors) from the University of Chicago, and her Ph.D. in Statistics from the University of Chicago. She joined OSU in 2014. 

Dr. Chang's research interests include machine learning, business analytics, spatial statistics, and spatio-temporal modeling. She is experienced in developing novel and efficient methods for important applications in business analytics, information systems, finance, and environmental science. Her research work has been published in premier and high-impact journals, such as the Journal of the American Statistical Association, Journal of Machine Learning Research, IEEE Transactions, Biometrics, Information Systems Frontier, Expert Systems with Applications, Information Technology & People, Quantitative Finance, and many others. She was awarded the Prominent Scholar Award for Excellence in Research by the College in 2020 and 2021. 

During her time at OSU, Dr. Chang has developed and delivered many business analytics and business statistics classes in a wide range of formats (in-person, online, synchronous). In her classes, Dr. Chang prioritizes student learning of relevant and applicable skills with real-life data and scenarios and adopts creative and innovative tools and results-driven techniques. She was invited by OSU Ecampus to share her tips on how to effectively engage and connect with online students:  and . She was also awarded the Byron L. Newton Excellence in Undergraduate Teaching Award and the Betty & Forrest Simmons Excellence in Graduate Teaching Award in 2018 and 2023, respectively. 

Currently, Dr. Chang is also the Professional Development Coordinator of the Business Analytics program at OSU and the faculty advisor of the , a student-led group that provides a platform for students who are interested in developing their skills and fueling their passion for a data-driven career. 

Background

Education

Ph.D. Statistics, University of Chicago

B.A. (Honors) Statistics and B.A. Economics, University of Chicago

G.C.E. Advanced Level, Raffles Junior College (n.k.a. Raffles Institution), Singapore

Experience

Associate Professor of Business Analytics, College of Business, , Sep. 2020 - Present. 

Assistant Professor of Quantitative Methods, College of Business, , Sep. 2014 - Aug. 2020. 

Adjunct Professor of Statistics, Department of Statistics, , Dec. 2016 - 2020. 

Professional Affiliations

INFORMS, American Statistical Association. 

Service

AI@OSU, (Member: 2023 - Present)

Interdisciplinary Advisory Committee for the AI Program, (Member: 2023 - Present)

Faculty Senate Promotion and Tenure Committee, (Member: 2022 - present)

President’s Commission on the Status of Women,  (Member: 2022 - present)

Promotion and Tenure Committee, College of Business (Member: 2023 - Present)

Graduate Program Council, College of Business (Member: 2022 - 2023)

Professional Development Coordinator of Business Analytics Program (2020 - Present)

OSU Business Analytics Club Faculty Advisor (2020 - Present)

Peer Review of Teaching Committee (Member: 2016 - 2017, 2018 - 2020; Committee Chair: 2020 - 2021)

Peer Review of Research Committee (Member: 2017 - 2020)

Faculty Search Committee for Supply Chain & Logistics Management, Business Analytics 

Honors & Awards

Toomey Faculty Fellow, 2021-present

Betty & Forrest Simmons Excellence in Graduate Teaching Award, College of Business, , 2023.

College of Business Prominent Scholar Award, 2021.

College of Business Prominent Scholar Award, 2020.

Byron L. Newton Award for Excellence in Undergraduate Teaching Award, College of Business, , 2018.

Extended Campus Research Fellow, , 2015 - 2017.

Newcomb Associate Award for Excellence in Research, , 2015.

Publications

Academic Journal
Business Analytics

“Efficient and Effective Calibration of Numerical Model Outputs Using Hierarchical Dynamic Models”

Numerical air quality models, such as the Community Multiscale Air Quality (CMAQ) system, play a critical role in characterizing pollution levels at fine spatial and temporal scales, but the model outputs tend to systematically over- or under-estimate pollutants concentrations. In this work, we propose a hierarchical dynamic model that can be implemented to calibrate large-scale grid-level CMAQ model outputs using point-level observations from sparse monitoring stations. Under a Bayesian framework, our model presents a flexible quantification of uncertainties by considering deep hierarchies for key parameters and can also be used to describe the dynamic nature of data structural changes. In addition, we adopt several newly developed techniques, including triangulation of research domain, tapering-based Gaussian kernel, Gaussian graphical model, variational Bayes, and ensemble Kalman smoother, which significantly speed up the entire calibration process. We demonstrate the effectiveness of our model using the daily PM2.5 datasets of China's Beijing-Tianjin-Hebei region, which consists of 68 monitoring stations and 2499 CMAQ 9-km grids. In contrast to the existing methods, our model produces more accurate calibration results, generates maps of higher-quality predictions, and operates at a higher computational efficiency. Overall, this methodology proves to be an effective calibration tool for large-scale numerical model outputs.
Details

1064-1089 pages 2024 Yewen Chen Xiaohui Chang Bohai Zhang Hui Huang View on:

Academic Journal
Business Analytics

“Additive Dynamic Models for Correcting Numerical Model Outputs”


Numerical air quality models are pivotal for the prediction and assessment of air pollution, but numerical model outputs may be systematically biased. An additive dynamic model is proposed to correct large-scale raw model outputs using data from other sources, including readings collected at ground monitoring networks and weather outputs from other numerical models. An additive partially linear model specification is employed for the nonlinear relationships between air pollutants and covariates. In addition, a multi-resolution basis function approximate is proposed to capture the different small-scale variations of biases, and a discretized stochastic
integro-differential equation is constructed to characterize the dynamic evolution of the random coefficients at each spatial resolution. An expectation-maximization algorithm is developed for parameter estimation and a multi-resolution ensemble-based scheme is embedded to accelerate the computation. For statistical inference, a conditional simulation technique is applied to quantify the uncertainty of parameter estimates and bias correction results. The proposed approach is used to correct the biased raw outputs of PM2.5 from the Community Multiscale Air
Quality (CMAQ) system for China’s Beijing-Tianjin-Hebei region. Our method improves the root mean squared error and continuous rank probability score by 43.70% and 34.76%, respectively. Compared to other statistical methods under different metrics, our model has advantages in both correction accuracy and computational efficiency.
Details

107799 pages 2023 Xiaohui Chang Yewen Chen Hui Huang Fangzhi Luo View on:

Academic Journal
Business Analytics

“Combating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media”

Misinformation on social media has become a horrendous problem in our society. Fact-checks on information often fall behind the diffusion of misinformation, which can lead to negative impacts on society. This research studies how different factors may affect the spread of fact-checks over the internet. We collected a dataset of fact-checks in a six-month period and analyzed how they spread on Twitter. The spread of fact-checks is measured by the total retweet count. The factors/variables include the truthfulness rating, topic of information, source credibility, etc. The research identifies truthfulness rating as a significant factor: conclusive fact-checks (either true or false) tend to be shared more than others. In addition, the source credibility, political leaning, and the sharing count also affect the spread of fact-checks. The findings of this research provide practical insights into accelerating the spread of the truth in the battle against misinformation online.
Details

1479-1493 pages 2022 Xiaohui Chang View on:

Media
Business Analytics

“Improving Student Engagement and Connection in Online Learning: Part II, Methodologies and Practices”

The first article in the series appeared last December. Since then, we have received plenty of feedback from other instructors who are actively engaged in online education. Almost all of them agreed that teaching well online remains a challenging task. In this article, I discussed six specific practices that I have found particularly helpful for online teaching and learning.

Practice 1: Adopt a variety of communication methods
Practice 2: Create a Q&A Discussion Board
Practice 3: Estimate the amount of time taken for each assignment
Practice 4: Ensure timely replies
Practice 5: Synchronize assignments with the Canvas calendar
Practice 6: Reorganize course content
Details

2022 OSU Ecampus Xiaohui Chang

Academic Journal
Business Analytics

“Improving Mobile Health Apps Usage: A Quantitative Study on mPower Data of Parkinson's Disease”

Purpose
The emergence of mobile health (mHealth) products has created a capability of monitoring and managing the health of patients with chronic diseases. These mHealth technologies would not be beneficial unless they are adopted and used by their target users. This study identifies key factors affecting the usage of mHealth apps based on user usage data collected from an mHealth app.

Design/methodology/approach
Using a data set collected from an mHealth app named mPower, developed for patients with Parkinson’s disease (PD), this paper investigated the effects of disease diagnosis, disease progression, and mHealth app difficulty level on app usage, while controlling for user information. App usage is measured by five different activity counts of the app.

Findings
The results across five measures of mHealth app usage vary slightly. On average, previous professional diagnosis and high user performance scores encourage user participation and engagement, while disease progression hinders app usage.

Research limitations/implications
The findings potentially provide insights into better design and promotion of mHealth products and improve the capability of health management of patients with chronic diseases.

Originality/value
Studies on the mHealth app usage are critical but sparse because large-scale and reliable mHealth app usage data are limited. Unlike earlier works based solely on survey data, this research used a large user usage data collected from an mHealth app to study key factors affecting app usage. The methods presented in this study can serve as a pioneering work for the design and promotion of mHealth technologies.
Details

399–420 pages 2020 Xiaohui Chang View on:

Academic Journal
Business Analytics

“Realized Volatility Forecasting and Volatility Spillovers: Evidence from Chinese Non-Ferrous Metals Futures”

We study the prediction of realized volatility of non-ferrous metals futures traded on the Shanghai Futures Exchange from March 2011 to December 2017. A dynamic model averaging model is employed to combine multiple prediction models using time-varying weights based on individual model performance. Empirical results also reveal that models incorporating volatility spillovers across metals are important for forecast combinations, and short-term spillovers have a stronger impact than long-term spillovers. This approach offers the best forecasting performance and allows users to identify the most dominant model at any given time and demonstrate when and how volatility transmission from another metal is valuable for forecasting. We also find evidence of distinct trading behaviors in emerging and developed markets.
Details

2713–2731 pages 2020 Donghua Wang Yang Xin Xiaohui Chang Xingze Su View on:

Academic Journal
Business Analytics

“Noise Accumulation in High Dimensional Classification and Total Signal Index”

Great attention has been paid to Big Data in recent years. Such data hold promise for scientific discoveries but also pose challenges to analyses. One potential challenge is noise accumulation. In this paper, we explore noise accumulation in high dimensional two-group classification. First, we revisit a previous assessment of noise accumulation with principal component analyses, which yields a different threshold for discriminative ability than originally identified. Then we extend our scope to its impact on classifiers developed with three common machine learning approaches—random forest, support vector machine, and boosted classification trees. We simulate four scenarios with differing amounts of signal strength to evaluate each method. After determining noise accumulation may affect the performance of these classifiers, we assess factors that impact it. We
conduct simulations by varying sample size, signal strength, signal strength proportional to the number predictors, and signal magnitude with random forest classifiers. These simulations suggest that noise accumulation affects the discriminative ability of high-dimensional classifiers developed using common machine learning methods, which can be modified by sample size, signal strength, and signal magnitude. We developed the measure total signal index (TSI) to track the trends of total signal and noise accumulation.
Details

1-23 pages 2020 Miriam Elman Jessica Minnier Xiaohui Chang Dongseok Choi View on: