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16 results on '"Panavas, Liudas"'

1. Set Visualizations for Comparing and Evaluating Machine Learning Models

Author

Panavas, Liudas, Crnovrsanin, Tarik, Fygenson, Racquel, Conway, Eamon, Millard, Derek, Buchler, Norbou, and Dunne, Cody

Subjects
Computer Science - Human-Computer Interaction
Abstract

Machine learning practitioners often need to compare multiple models to select the best one for their application. However, current methods of comparing models fall short because they rely on aggregate metrics that can be difficult to interpret or do not provide enough information to understand the differences between models. To better support the comparison of models, we propose set visualizations of model outputs to enable easier model-to-model comparison. We outline the requirements for using sets to compare machine learning models and demonstrate how this approach can be applied to various machine learning tasks. We also introduce SetMLVis, an interactive system that utilizes set visualizations to compare object detection models. Our evaluation shows that SetMLVis outperforms traditional visualization techniques in terms of task completion and reduces cognitive workload for users. Supplemental materials can be found at https://osf.io/afksu/?view_only=bb7f259426ad425f81d0518a38c597be., Comment: Submitted to ACM Transactions on Computer-Human Interaction

Published
2025

2. But Can You Use It? Design Recommendations for Differentially Private Interactive Systems

Author

Panavas, Liudas, Snoke, Joshua, Tyagi, Erika, Bowen, Claire McKay, and Williams, Aaron R.

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Cryptography and Security, Statistics - Applications
Abstract

Accessing data collected by federal statistical agencies is essential for public policy research and improving evidence-based decision making, such as evaluating the effectiveness of social programs, understanding demographic shifts, or addressing public health challenges. Differentially private interactive systems, or validation servers, can form a crucial part of the data-sharing infrastructure. They may allow researchers to query targeted statistics, providing flexible, efficient access to specific insights, reducing the need for broad data releases and supporting timely, focused research. However, they have not yet been practically implemented. While substantial theoretical work has been conducted on the privacy and accuracy guarantees of differentially private mechanisms, prior efforts have not considered usability as an explicit goal of interactive systems. This work outlines and considers the barriers to developing differentially private interactive systems for informing public policy and offers an alternative way forward. We propose balancing three design considerations: privacy assurance, statistical utility, and system usability, we develop recommendations for making differentially private interactive systems work in practice, we present an example architecture based on these recommendations, and we provide an outline of how to conduct the necessary user-testing. Our work seeks to move the practical development of differentially private interactive systems forward to better aid public policy making and spark future research.

Published
2024

3. Development of a Part Criticality Index in Inventory Management

Author

Saidy, Clint, Panavas, Liudas, Harik, Ramy, Bayoumi, Abdel-Moez, Khoury, Joseph, Rannenberg, Kai, Editor-in-chief, Sakarovitch, Jacques, Series editor, Goedicke, Michael, Series editor, Tatnall, Arthur, Series editor, Neuhold, Erich J., Series editor, Pras, Aiko, Series editor, Tröltzsch, Fredi, Series editor, Pries-Heje, Jan, Series editor, Whitehouse, Diane, Series editor, Reis, Ricardo, Series editor, Furnell, Steven, Series editor, Furbach, Ulrich, Series editor, Winckler, Marco, Series editor, Rauterberg, Matthias, Series editor, Ríos, José, editor, Bernard, Alain, editor, Bouras, Abdelaziz, editor, and Foufou, Sebti, editor

Published
2017
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4. Investigating the Visual Utility of Differentially Private Scatterplots

Author

Panavas, Liudas, Crnovrsanin, Tarik, Adams, Jane Lydia, Ullman, Jonathan, Sargavad, Ali, Tory, Melanie, and Dunne, Cody

Abstract

Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses—but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection.

Published
2024
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5. Anonymous Title

Author

Syeda, Uzma Haque, primary, South, Laura, additional, Raynor, Justin, additional, Panavas, Liudas, additional, Saffo, David, additional, Morriss, Thomas, additional, Dunne, Cody, additional, and Borkin, Michelle, additional

Published
2023
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6. Vis Repligogy: Towards a Culture of Facilitating Replication Studies in Visualization Pedagogy and Research

Author

Borkin, Michelle, Raynor, Justin, South, Laura, Panavas, Liudas, Saffo, David, Dunne, Cody, Morriss, Tommy, and Uzma Haque Syeda

Abstract

Data visualization is a young and growing field with many unresolved, debated, and untested theories that need to be tested, validated, and strengthened. Replication studies are a common way to achieve this validation by investigating the credibility, rigor, and generalizability of previously published research. The execution of replication studies is crucial to strengthen the field of data visualization and to ensure that the field’s foundations are solid with accurate methods and theories. Although visualization researchers acknowledge the epistemological significance of replications and its necessity to establish trust and reliability, the field has made little progress to support the publication of such studies and importantly provide methods to the community to encourage replications. In this paper, we contribute Vis Repligogy, a novel framework to incorporate replication studies within visualization course curricula that not only teaches students replication and evaluation methodologies but also results in executed replication studies to validate prior work. To validate the feasibility of the framework, we present the results of four replication studies that were conducted in a graduate data visualization course that implemented the Vis Repligogy framework. We also provide a meta-analysis of the four replication studies that replicated and validated, a seminal work in data visualization investigating the effects of visual embellishments on chart interpretability and memorability. Finally, we reflect on our experience of conducting the replication studies through the Vis Repligogy framework and reflect on the experience to provide useful recommendations for future use of the framework. We envision that this framework will encourage instructors to conduct replications in their own courses and will help facilitate more replication studies in the visualization community to support a culture shift where replications are commonplace.

Published
2023
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7. Does spatial sound help reduce search times in VR more than on PC?

Author

Panavas, Liudas

Subjects
Engineering, Social and Behavioral Sciences
Abstract

Virtual reality and immersive analytics are becoming more prevalent as HMD’s are improving and becoming more cost effective. In the future, immersive analytics may become ubiquitous and a common part of the data analysis and visualization workflow. Researchers will have to quickly identify and locate specific data points in the immersive environment. The task of visual search may also be extended beyond data visualization and be applied to many different applications as users are required to look for out of view objects. In this study we will see if spatial auditory cues will be more effective in an immersive environment than a desktop environment.

Published
2022
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8. Children's Graphical Perception

Author

Panavas, Liudas, Dunne, Cody, Worth, Amy, Borkin, Michelle, Cordes, Sara, and Crnovrsanin, Tarik

Subjects
FOS: Psychology, genetic structures, Visual Literacy, Early Childhood Education, Data Visualization, Developmental Psychology, Psychology, Social and Behavioral Sciences, Children, psychological phenomena and processes, Graphical Perception, Education
Abstract

When decoding graphs, both children and adults use the elementary tasks found in graphical perception. These principles and rankings have been widely studied to curate design recommendations for making effective graphs. This has proven fruitful for adults, the generally-studied population, but we do not know if these same findings apply to children as few studies have investigated their visual perception in regards to graphs. This leaves a gap where we need to understand if the graphical perception studies we conducted on adults apply to children as well to best inform the research that deals with educating and designing graphs for children. We hope to find guidelines for data visualization design that are generalizable across ages.

Published
2022
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16. Illuminating the Landscape of Differential Privacy: An Interview Study on the Use of Visualization in Real-World Deployments.

Author

Panavas L, Sarker A, Bartolomeo SD, Sarvghad A, Dunne C, and Mahyar N

Abstract

As Differential Privacy (DP) transitions from theory to practice, visualization has surfaced as a catalyst in promoting acceptance and usage. Despite the potential of visualization tools to support differential privacy implementation, their development is limited by a lack of understanding of the overall deployment process, practitioner challenges, and the role of visual tools in real-world deployments. To narrow this gap, we interviewed 18 professionals from various backgrounds who regularly engage with differential privacy in their work. Our objectives were to understand the differential privacy implementation process and associated challenges; explore the actors (individuals involved in differential privacy implementation), how they use or struggle to use visualization; and identify the benefits and challenges of using visualization in the implementation process. Our results delineate the differential privacy implementation process into five distinct stages and highlight the main actors alongside the diverse visualization applications and shortcomings. We find that visualizations can be used to build foundational differential privacy knowledge, describe implementation parameters, and evaluate private outputs. However, the visualization strategies described often fail to address the diverse technical backgrounds and varied privacy and accuracy concerns of users, hindering effective communication between the different actors involved in the implementation process. From our findings, we propose three research directions: visualizations for setting and evaluating noise addition, evaluation of uncertainty visualization related to trust in differential privacy, and research focused on pedagogical visualizations for complex data science topics. A free copy of this paper and all supplemental materials are available at https://osf.io/qhyzt/?view_only=1a5c7d7553c840ab9f125d88bc13946f.

Published
2024
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