2025 ISDSA Meeting

Workshops

Four workshops will be available. You will get a certificate of achievement for each workshop after completing it. Each workshop is 3-hour long and taught online through Zoom.

Workshop: Introduction to Graph Machine Learning: Methods and Applications

Time: TBA
Location： Virtual on Zoom (Register to get the link)

Graph-structured data is central to many real-world problems, encompassing domains such as recommender systems, social network analysis, and computational biology. This workshop offers a comprehensive introduction to graph machine learning, covering foundational concepts and state-of-the-art techniques. Participants will learn about graph representation learning and graph neural networks, with a focus on practical applications in the aforementioned areas. Through lectures and discussions, attendees will gain insights into how graph machine learning is transforming diverse fields. This workshop is designed for researchers, practitioners, and students seeking to understand and apply graph-based methodologies to solve complex challenges.

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  Jundong Li -- University of Virginia

  Jundong Li is an Assistant Professor at the University of Virginia with appointments in Department of Electrical and Computer Engineering, Department of Computer Science, and School of Data Science. Prior to joining UVA, he received his Ph.D. degree in Computer Science at Arizona State University in 2019 under the supervision of Dr. Huan Liu, M.Sc. degree in Computer Science at University of Alberta in 2014, and B.Eng. degree in Software Engineering at Zhejiang University in 2012. His research interests are generally in data mining and machine learning, with a particular focus on graph machine learning, trustworthy/safe machine learning, and more recently on large language models. He has published over 150 papers in high-impact venues (including KDD, WWW, WSDM, NeurIPS, ICML, ICLR, IJCAI, AAAI, ACL, EMNLP, NAACL, SIGIR, CIKM, ICDM, SDM, ECML-PKDD, CSUR, TPAMI, TKDE, TKDD, TIST, etc), with over 14,000 citation count. He has won several prestigious awards, including SIGKDD Rising Star Award (2024), PAKDD Early Career Research Award (2023), PAKDD Best Paper Award (2024), NSF CAREER Award (2022), SIGKDD Best Research Paper Award (2022), JP Morgan Chase Faculty Research Award (2021 & 2022), and Cisco Faculty Research Award (2021), among others. His group's research is generously supported by NSF (CAREER, III, SaTC, SAI, S&CC), DOE, ONR, Commonwealth Cyber Initiative, Jefferson Lab, JP Morgan, Cisco, Netflix, and Snap.

Workshop: Analytic Methods for Process Data in Large-Scale Assessments

Time: TBA
Location： Virtual on Zoom (Register to get the link)

The use of computers as a delivery platform not only enables the development of innovative item types but also facilitates the collection of a broader range of records in log files throughout human-machine interactions. These granular records, often referred to as process data, are typically stored as an ordered sequence of multi-type, time-stamped events. This rich source of data supports the exploration and identification of informative features from problem-solving processes beyond response data. They are used to investigate when and how respondents engage in solving interactive tasks (Goldhammer et al., 2014; He et al., 2021, 2023), to identify test-taking strategies across population subgroups, such as age, gender, and educational attainment (Eichmann et al., 2020; Liao et al., 2019), and to shape future test and item design (Zhang et al., 2023).

This training session introduces the fundamental structure and analytic methods of process data, aiming to support the incorporation of response process information in large-scale assessments. The training will combine theoretical instructions on methodologies to analyze process data through sequence-based and feature-based approaches, along with programming code demonstrations, and case study illustrations using process data in Program for the International Assessment of Adult Competencies (PIAAC) and National Assessment of Educational Progress (NAEP).

This training session will be conducted in two sections, focusing on four subtopics: (1) creating and selecting informative features from process data in large-scale assessments, (2) extracting and selecting gram-based features from clickstream sequences, (3) computing sequence distance to identify pairwise sequence similarity and conduct sequence clustering, and (4) identifying and interpreting meaningful patterns from process data. Section 1 will provide an overview of process data structure, preprocessing, and methods for extracting information from process data through sequence mining techniques. Section 2 will introduce feature-based process data analysis, covering how to create and select process variables and integrate content experts’ insights into these variables. Each section will be supported by case studies as illustrations.

This training session will enable participants to understand process data structure, gain basic knowledge about sequence- and feature-based methods for analyzing process data, and how to choose appropriate methods for their own research. All program codes will be included in the training package for participants’ self-learning and further programming needs.

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  Qiwei He -- Georgetown University

  Dr. Qiwei He is Associate Professor in the Data Science and Analytics Program, and Founder and Director of the AI-Measurement and Data Science Lab at Georgetown University. Her research focuses on advancing methodologies in sequence mining, text mining, psychometric modeling, and machine learning on new data sources such as process data and textual data collected in digital-based assessments in education, psychology, psychiatry, and public health. She was an OECD Thomas Alexander Research Fellow and recipient of NCME Annual Award (2023), Jason Millman Award (2019), and Alicia Cascallar Award (2017) for her contribution to developing and applying new methods in process data analysis.

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  Susu Zhang -- University of Illinois Urbana-Champaign

  Dr. Susu Zhang is an Assistant Professor of Psychology and Statistics at the University of Illinois Urbana-Champaign (UIUC). Her research involves latent variable modeling for testing and learning data, and the analysis of complex process data to inform educational measurement. She is the co-author of the R package ProcData for exploratory process data analysis and the recipient of NCME Alicia Cascallar Award (2022) for her outstanding work in integrating process data in psychometric models.

Workshop: Structural Equation Modeling with Text Data

Time: TBA
Location： Virtual on Zoom (Register to get the link)

Text data are increasingly abundant in social science research, yet they are often underutilized in analysis. In this workshop, I will demonstrate how to conduct structural equation modeling (SEM) using both quantitative and qualitative data. The session will begin with an overview of SEM, covering its fundamental concepts and applications. Next, I will explore various approaches to extracting insights from text data, including dictionary-based methods, AI-driven sentiment analysis, and word embeddings. Following this, I will discuss how to integrate text data with other data types within the SEM framework. To illustrate these methods, I will use teaching evaluation data as a case study. Finally, I will showcase how to use the R package TextSEM and the online application BigSEM, developed in my lab, to conduct SEM with text data. Participants will leave the workshop equipped with practical tools and techniques for harnessing the potential of text data in their research.

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  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.

Registration

To register, click the link here. It will take you to our meeting registration and management website.

Meeting Registration

If you are not an ISDSA member, you can register to become a member now. New membership till the end of 2025 is free with meeting registration.

Workshop Registration

The price is for all four workshops.

Meeting participants: Free
Students: $25
All others: $50

For each workshop, we can only accommodate 200 participants. To register, please use the form below or our registration platform before July 1, 2025.

Refund Policy

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

Hotel

The meeting is taking place at the Hilton Washington DC National Mall The Wharf. We have reserved a book of rooms at a reduced rate ($245 plus tax per night). The rooms can be booked after Feb 1, 2025.

Sponsors

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

Sponsoring Journals
Selected papers will be published in Journal of Behavioral Data Science.