activity
20202025
most citedCompeting Models: Inferring Exploration Patterns and Information Relevance via Bayesian Model Selection

21 citations · 24 across the 6 of their papers we have counts for

collaborators

7 papers

cs.HC2025

A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance

Shayan Monadjemi, Yuhan Guo, Kai Xu +2

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of aut…

cs.HC2024★ 1 cited

Guided By AI: Navigating Trust, Bias, and Data Exploration in AI-Guided Visual Analytics

Sunwoo Ha, Shayan Monadjemi, Alvitta Ottley

The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of indu…

cs.HC2023★ 1 cited

Human-Computer Collaboration for Visual Analytics: an Agent-based Framework

Shayan Monadjemi, Mengtian Guo, David Gotz +2

The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics ai…

cs.HC2022★ 1 cited

A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias

Sunwoo Ha, Shayan Monadjemi, Roman Garnett +1

The visual analytics community has proposed several user modeling algorithms to capture and analyze users' interaction behavior in order to assist users in data exploration and ins…

cs.HC2022

A Grammar-Based Approach for Applying Visualization Taxonomies to Interaction Logs

Sneha Gathani, Shayan Monadjemi, Alvitta Ottley +1

Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their higher-level motivations, intuitions, and goals. Althoug…

cs.HC2020★ 21 cited

Competing Models: Inferring Exploration Patterns and Information Relevance via Bayesian Model Selection

Shayan Monadjemi, Roman Garnett, Alvitta Ottley

Analyzing interaction data provides an opportunity to learn about users, uncover their underlying goals, and create intelligent visualization systems. The first step for intelligen…

cs.HC2020

Guided Data Discovery in Interactive Visualizations via Active Search

Shayan Monadjemi, Sunwoo Ha, Quan Nguyen +3

Recent advances in visual analytics have enabled us to learn from user interactions and uncover analytic goals. These innovations set the foundation for actively guiding users duri…