Posts by Collection

Glanceable Data Visualizations for Older Adults: Establishing Thresholds and Examining Disparities Between Age Groups

Published in CHI Conference on Human Factors in Computing Systems (CHI), 2024

A replication study examining glanceable smartwatch visualizations for older adults and identifying age-related differences in visualization perception.

Recommended citation: While, Z., Blascheck, T., Gong, Y., Isenberg, P., & Sarvghad, A. (2024). "Glanceable Data Visualizations for Older Adults: Establishing Thresholds and Examining Disparities Between Age Groups." Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems.

GerontoVis: Data Visualization at the Confluence of Aging

Published in Eurographics Conference on Visualization (EuroVIS), 2024

A position paper introducing GerontoVis, a new subfield focused on designing and evaluating data visualizations for older adults.

Recommended citation: While, Z., Crouser, R. J., & Sarvghad, A. (2024). "GerontoVis: Data Visualization at the Confluence of Aging." Computer Graphics Forum, 43(3).

Dark Mode or Light Mode? Exploring the Impact of Contrast Polarity on Visualization Performance Between Age Groups

Published in IEEE Visualization and Visual Analytics (VIS), 2024

An empirical investigation of how contrast polarity influences data visualization performance across younger and older adult populations.

Recommended citation: While, Z., & Sarvghad, A. (2024). "Dark Mode or Light Mode? Exploring the Impact of Contrast Polarity on Visualization Performance Between Age Groups." 2024 IEEE Visualization and Visual Analytics (VIS), 211-215.

Toward Understanding the Experiences of People in Late Adulthood with Embedded Information Displays in the Home

Published in 1st Workshop on Accessible Data Visualization (AccessViz), IEEE Visualization and Visual Analytics (VIS), 2024

A qualitative study exploring how older adults experience and interact with embedded information displays on everyday household appliances.

Recommended citation: While, Z., Wheeler-Klainberg, H., Blascheck, T., Isenberg, P., & Sarvghad, A. (2024). "Toward Understanding the Experiences of People in Late Adulthood with Embedded Information Displays in the Home." 1st Workshop on Accessible Data Visualization (AccessViz), 2024 IEEE Visualization and Visual Analytics (VIS), 14-18.

Toward Filling a Critical Knowledge Gap: Charting the Interactions of Age with Task and Visualization

Published in CHI Conference on Human Factors in Computing Systems, 2025

An empirical study examining how age interacts with visualization type and analytical task performance, providing evidence and guidance for aging-inclusive visualization design.

Recommended citation: While, Z., & Sarvghad, A. (2025). "Toward Filling a Critical Knowledge Gap: Charting the Interactions of Age with Task and Visualization." Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems.

Can a Neural Encoding Model Replicate an fMRI Visualization Study?

Published in VISxVISION Workshop, IEEE Visualization and Visual Analytics (VIS), 2026

Evaluating whether Meta’s Tribe V2 neural encoding model can reproduce neural contrasts from a prior fMRI-based visualization study, exploring the potential of in-silico neuroimaging for visualization research.

Recommended citation: Orang, E. N., & While, Z. (2026). "Can a Neural Encoding Model Replicate an fMRI Visualization Study?" VISxVISION Workshop, 2026 IEEE Visualization and Visual Analytics (VIS).

Reflections on Working with Older Adults in Visualization Research

Published in 3rd Workshop on Accessible Data Visualization (AccessViz), IEEE Visualization and Visual Analytics (VIS), 2026

Reflections and methodological takeaways from human-subject studies with older adults to guide and encourage broader inclusion in visualization research.

Recommended citation: While, Z. (2026). "Reflections on Working with Older Adults in Visualization Research." 3rd Workshop on Accessible Data Visualization (AccessViz), 2026 IEEE Visualization and Visual Analytics (VIS).

CSCI 4851: Data Science and Machine Learning (Fall 2025)

Undergraduate Course, Youngstown State University, 2025

Undergraduate Course, Instructor
CSCI 4851 introduces students to the data science pipeline and machine learning. Topics include data acquisition, preprocessing, visualization, feature engineering, classification, clustering, deep learning, recommendation systems, and real-world data analysis.

CSCI 6951: Data Science and Machine Learning (Fall 2025)

Graduate Course, Youngstown State University, 2025

Graduate Course, Instructor
CSCI 6951 introduces students to the data science pipeline and machine learning. Topics include data acquisition, preprocessing, visualization, feature engineering, classification, clustering, deep learning, recommendation systems, and real-world data analysis.

CSIS 2610: Programming and Problem-Solving (Fall 2025)

Undergraduate Course, Youngstown State University, 2025

Undergraduate Course, Instructor
CSIS 2610 introduces students to problem-solving methods and programming using C++. Topics include algorithms, control structures, functions, arrays, pointers, file processing, and object-oriented programming.

CSIS 2610L: Programming and Problem-Solving Lab (Fall 2025)

Undergraduate Laboratory Course, Youngstown State University, 2025

Undergraduate Laboratory Course, Instructor
CSIS 2610L provides hands-on programming experience in C++ through guided lab activities, coding exercises, debugging practice, and collaborative problem solving.

CSIS 3731: Human-Computer Interaction (Fall 2025)

Undergraduate Course, Youngstown State University, 2025

Undergraduate Course, Instructor
CSIS 3731 introduces students to the principles and practices of human-computer interaction, including user-centered design, usability evaluation, prototyping, accessibility, and interaction design.

CSCI 4851: Data Science and Machine Learning (Spring 2026)

Undergraduate Course, Youngstown State University, 2026

Undergraduate Course, Instructor
CSCI 4851 introduces students to the data science pipeline and machine learning. Topics include data acquisition, preprocessing, visualization, feature engineering, classification, clustering, deep learning, recommendation systems, and real-world data analysis.

CSCI 4852: Deep Learning (Spring 2026)

Graduate Course, Youngstown State University, 2026

Undergraduate Course, Instructor
CSCI 4852 introduces students to the foundations and practice of deep learning. Topics include neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative models, model optimization, and real-world applications.

CSCI 6951: Data Science and Machine Learning (Spring 2026)

Graduate Course, Youngstown State University, 2026

Graduate Course, Instructor
CSCI 6951 introduces students to the data science pipeline and machine learning. Topics include data acquisition, preprocessing, visualization, feature engineering, classification, clustering, deep learning, recommendation systems, and real-world data analysis.

CSCI 6952: Deep Learning (Spring 2026)

Graduate Course, Youngstown State University, 2026

Graduate Course, Instructor
CSCI 6952 introduces students to the foundations and practice of deep learning. Topics include neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative models, model optimization, and real-world applications.

CSIS 3700: Data Structures and Objects (Spring 2026)

Undergraduate Course, Youngstown State University, 2026

Undergraduate Course, Instructor
CSIS 3700 introduces students to object-oriented programming and fundamental data structures. Topics include classes, abstract data types, linked structures, stacks, queues, trees, graphs, algorithm analysis, and software design.

CSIS 3700L: Data Structures and Objects Lab (Spring 2026)

Undergraduate Laboratory Course, Youngstown State University, 2026

Undergraduate Laboratory Course, Instructor
CSIS 3700L provides hands-on experience implementing and analyzing data structures in C++. Topics include classes, dynamic memory, linked lists, stacks, queues, trees, graphs, and the C++ Standard Template Library.