Hybrid meeting in China, United States, and on Zoom

2022 ISDSA Meeting




May 31–June 1, 2022


About the Meeting

The Annual Meeting of the International Society for Data Science and Analytics (ISDSA) aims to provide a global forum from researchers and practitioners in the field of data science and data analytics to communicate and showcase their latest research. The ISDSA Meeting is for anyone interested in data science and data analytics. Researchers, practitioners, educators, and users come together at the ISDSA Meeting to present new and ongoing work and to discuss future directions for data science and data analytics.

Because of the ongoing pandemic, the 2022 Annual Meeting of ISDSA will be a combination of in-person and virtual talks.

Submit an Abstract

We welcome the submission of theoretical and method papers, applications and case studies, tutorials, and software. Paper submission is open till April 1, 2022. We accept regular 30-minute paper presentations and 10-minute speed paper presentations. If you are interested in participating in the meeting, please submit an abstract now. You will be ask to register as a user to use the online abstract submission system.

Awards

A best paper will be selected to give the best paper award. The award comes with a $500 check and a certificate. We also offer a limit number of travel awards up to $500 to researchers based on need.

Share the meeting

You can share the meeting information with your colleagues by inputting their email and name below.


Workshops

Several workshops are available. You will get a certificate of achievement for each workshop after completing it. To reserve a spot, use the form below each workshop. We will send you instructions on registration and payment.

Workshop 1: Bayesian Longitudinal Data Modeling

Hybrid: In person at University of Notre Dame and online on Zoom
9:00AM - 5:00PM, June 2-3, 2022

Modeling longitudinal data is one of the most active areas of research in social and behavioral sciences because longitudinal research provides valuable insights into change and causal relationships. The application of Bayesian methods in longitudinal research has gained increasing popularity. Taught by four quantitative researchers who are active in developing Bayesian methods and longitudinal data analytical methods, this workshop will teach participants how to analyze longitudinal data using Bayesian statistics. We will introduce the basic idea of Bayes' theorem first and move on to models including multilevel models, growth curve models, growth mixture models, and longitudinal structural equation models. Concrete examples will be provided to illustrate how to compute, report, and interpret Bayesian modeling results with empirical psychological data. Additionally, we will teach the application of Bayesian robust methods for nonnormal data ignorable and non-ignorable missing data.

  • Xin Tong

    Cynthia Tong -- University of Virginia

    Dr. Tong is an associate professor at the University of Virginia. Her research focuses on developing and applying statistical methods in the areas of developmental and health studies. Methodologically, she is interested in Bayesian methodology, growth curve modeling, and robust structural equation modeling with nonnormal and missing data. Substantively, she is interested in analyzing the longitudinal development of cognitive ability and achievement skills.

  • Han Du

    Han Du -- University of California, Los Angeles

    Dr. Han Du is an assistant professor at the University of California, Los Angeles. Her methodological interests have evolved along three inter-related lines: (1) Bayesian methods and statistical computing; (2) longitudinal data analysis and time series analysis; and (3) study design and sample size determination. She also works on developing new meta-analysis and machine learning techniques. From a substantive perspective, she is interested in applying quantitative methods in developmental, clinical, cognitive, educational, and health research.

  • Sarah Depaoli

    Sarah Depaoli -- University of California, Merced

    Dr. Sarah Depaoli is Associate Professor and Area Head of Quantitative Methods, Measurement, and Statistics (QMMS) at the University of California, Merced. Her research focuses on issues surrounding Bayesian estimation of latent variable models. She is the author of the book Bayesian Structural Equation Modeling, which is part of the Methodology in the Social Sciences Series with Guilford Press.

  • Johnny Zhang

    Johnny Zhang -- University of Notre Dame

    Dr. Johnny Zhang is a Professor in Quantitative Psychology at the University of Notre Dame. His research aims to develop better statistical methods and software in the areas of education, health, management and psychology. He has conducted research in the areas of Bayesian methods, Big data analysis, Structural equation modeling, Longitudinal data analysis, Mediation analysis, and Statistical computing and programming. His most recent research involves the development of new methods for social network and text analysis.

Workshop registration

The regular workshop fee is $199. Student registration fee is $99. We provide a limited number of awards to waive the registration fee. Please email us for information.

Application for the travel award is now closed. You can still register for the workshop using the link below.

Register for the workshop before March 31, 2022 to save 50% of the registration fee.

Workshop 2: Introduction to Social Network Analysis (Virtual on Zoom)

May 30, 2022: 8:00AM-11:00AM (US Eastern Time)

Social network analysis is becoming increasingly popular in social, educational, and psychological sciences. This interactive course intends to provide participants with a detailed introduction, practical examples, and demonstration of analyzing social network data using the free software R. Topics covered include (1) Network Data; (2) Network Visualization; (3) Network Statistics; (4) Basic and Advanced Network Models. Especially, we will cover classical models such as Stochastic Block Model, Exponential Random Graph Model, Latent Space Model, and the newly developed techniques such as Latent Factor Space Modeling and Network Mediation Analysis.

  • Haiyan Liu

    Haiyan Liu -- University of California, Merced

    Dr. Haiyan Liu is currently an assistant professor of Quantitative Methods, Measurement, and Statistics at the University of California, Merced. Dr. Liu’s research centers broadly on statistical modeling of psychological and behavioral data such as high-dimensional data, longitudinal data, and categorical data. Her recent research includes social network modeling, Bayesian structural equation modeling, and non-parametric modeling of growth curves.

Workshop registration

Without meeting registration, the regular workshop fee is $99 and the student registration fee is $49. With meeting registration, the regular workshop fee is $49 and the student registration fee is $25. We provide a limited number of awards to waive the registration fee. Please email us for information.

With the support from the College of Management of the National Taiwan Normal University, we are offering this workshop for free. We will refund you if you have paid the registration fee. To register for the workshop, click the link here. During registration, choose the free option ($0). Meeting registration is required after April 1, 2022.

Workshop 3: Statistical Power Analysis for Structural Equation Modeling (Virtual on Zoom)

May 30, 2022: 1:00PM-4:00PM (US Eastern Time)

This workshop teaches researchers how to conduct a statistical power analysis to determine the sample size for a planned study when applying structural equation modeling. Participants will learn statistical power analysis for mediation analysis, path analysis, and structural equation modeling using both traditional methods and Monte Carlo methods. Free software WebPower (https://webpower.psychstat.org) will be used to illustrate how to conduct power analysis in practice.

  • Johnny Zhang

    Johnny Zhang -- University of Notre Dame

    Dr. Johnny Zhang is a Professor in Quantitative Psychology at the University of Notre Dame. His research aims to develop better statistical methods and software in the areas of education, health, management and psychology. He has conducted research in the areas of Bayesian methods, Big data analysis, Structural equation modeling, Longitudinal data analysis, Mediation analysis, and Statistical computing and programming. His most recent research involves the development of new methods for social network and text analysis.

Workshop registration

Without meeting registration, the regular workshop fee is $99 and the student registration fee is $49. With meeting registration, the regular workshop fee is $49 and the student registration fee is $25. We provide a limited number of awards to waive the registration fee. Please email us for information.

To register for the workshop, click the link here.

Workshop 4: A Gentle Introduction to Machine Learning (Virtual on Zoom)

June 2, 2022: 8:00AM-11:00AM (US Eastern Time)

This workshop is designed to introduce the fundamentals and practical applications of modeling data using data mining and machine learning methods. It surveys a series of topics starting from exploratory data analysis, unsupervised learning, supervised learning and other learning methods to uncover the patterns and structure of data targeting practical problem solving and identifying solutions. Students will learn by hands-on programming including such methods as regression, classification, logistic models, non-linear models, tree-based models, association rules, neural network and support vector machines. New developments in learning such as model visualization, deep learning and interpretable machine learning will also be introduced and illustrated.

Click here for a topics covered in this workshop.

  • Haiyan Liu

    Karl Ho -- University of Texas at Dallas

    Dr. Karl Ho is currently an associate professor University of Texas at Dallas. His research interests span both domains of data analytics and political studies, with a regional focus on East Asia. The unifying theme of my research is to use data science methods to explain and prescribe social and political problems. Areas of research topics include democratization, political economy, public policy and human rights aiming at building public goods and improving policies, governance and representation. He is the Director of Graduate Studies the University of Texas at Dallas (UTD) Political Science program. He is also the founding faculty member of the UTD Social Data Analytics and Research Master’s program and host of the Data Analytics Colloquium

Workshop registration

Without meeting registration, the regular workshop fee is $99 and the student registration fee is $49. With meeting registration, the regular workshop fee is $49 and the student registration fee is $25. We provide a limited number of awards to waive the registration fee. Please email us for information.

With the support from the College of Management of the National Taiwan Normal University, we are offering this workshop for free. We will refund you if you have paid the registration fee. To register for the workshop, click the link here. During registration, choose the free option ($0). Meeting registration is required after April 1, 2022.

Meeting Registration

Registration to attend the meeting is required. We will send you the receipt of registration through email, so make sure your email address is provided and correct.

Regular registration for members: $199
Student registration for members: $99
Regular registration for non-members: $299
Student registration for non-members: $149

If you are not an ISDSA member, please register to become a member now (first year of membership is free).

You can register for both the meeting and the workshops on the registration page here: Meeting and Workshop Registration

Refund Policy

For any reason that prevents you from attending the workshop, you can request for a refund by contacting us. The refund amount depends on the time you make the request.
Before May 1: Refund full registration fee minus 5% of processing fee (charged by the third-party)
After May 1 & before May 25: Refund 80% of full registration fee.
After May 25: no refund.

Sponsors

The meeting is jointly supported by the International Society for Data Science and Analytics, the National Taiwan Normal University, the Nanjing University of Posts and Telecommunications, and the University of Notre Dame.

Organizers

  • Hawjeng Chiou
    Distinguished Professor
    National Taiwan Normal
    University

  • Karl Ho
    Associate Professor
    University of Texas at Dallas
     

  • Wen Qu
    Junior Associate Professor
    Fudan University
     

  • Jiashan Tang
    Professor
    Nanjing University of Posts
    and Telecommunications

  • Ke-Hai Yuan
    Professor
    University of Notre Dame
     

  • Zhiyong Zhang
    Professor
    University of Notre Dame
     

  • Contact information

    Telephone: 1-574-400-5868
    Email: meeting@isdsa.org
    Mail: P.O.Box 1471, ISDSA, Granger, IN 46530