Ridwan Mahbub

DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?

Mizanur Rahman, Mohammed Saidul Islam, Ridwan Mahbub, Md Tahmid Rahman Laskar et autres

Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as notebooks, IDEs, terminals, browsers, and databases within real operating environments. Yet existing benchmarks lack real-computer interaction and do not …

Chart Deception in Vision-Language Models: From Vulnerability to Mitigation

Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mizanur Rahman et autres

Information visualizations are widely used to communicate patterns, trends, and outliers, yet deceptive design choices-such as truncated or inverted axes, distorted aspect ratios, inappropriate encodings, and misleading color mappings-can systematically alter interpretation while preserving the underlying data. As Vision-Language Models (VLMs) are …

Chart Deception in Vision-Language Models: From Vulnerability to Mitigation

Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mizanur Rahman et autres

Information visualizations are widely used to communicate patterns, trends, and outliers, yet deceptive design choices-such as truncated or inverted axes, distorted aspect ratios, inappropriate encodings, and misleading color mappings-can systematically alter interpretation while preserving the underlying data. As Vision-Language Models (VLMs) are …

ca (code pays fourni par la source)

Towards Robust and Coherent Data Visualization Generation with Vision–Language Models

Ridwan Mahbub

Visual data stories have emerged as an effective medium for communicating data by integrating visualizations with coherent narratives. Data videos, a popular format of visual data stories, combine animated, chart-centric visualizations with synchronized narration. In this thesis, we introduce a novel task …

DATAREEL: Automated Data-Driven Video Story Generation with Animations

Ridwan Mahbub, Syem Aziz, Mizanur Rahman, Mahir Ahmed et autres

Data videos combine animated visualizations with synchronized narration to communicate quantitative information and are widely used in journalism, education, and public communication. Automatically generating them requires deciding what story to tell, designing effective visualizations, and producing executable animations synchronized with narration. Despite …

DATAREEL: Automated Data-Driven Video Story Generation with Animations

Ridwan Mahbub, Syem Aziz, Mizanur Rahman, Mahir Ahmed et autres

Data videos combine animated visualizations with synchronized narration to communicate quantitative information and are widely used in journalism, education, and public communication. Automatically generating them requires deciding what story to tell, designing effective visualizations, and producing executable animations synchronized with narration. Despite …

DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards

Association for Computational Linguistics 2026, Mahir Ahmed, Shafiq Joty, Aaryaman Kartha et autres

Dashboards are powerful visualization tools for data-driven decision-making, integrating multiple interactive views that allow users to explore, filter, and navigate data. Unlike static charts, dashboards support rich interactivity, which is essential for uncovering insights in real-world analytical workflows. However, existing question-answering benchmarks …

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DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards

Aaryaman Kartha, Ahmed Masry, Mohammed Saidul Islam, Thinh Lang et autres

Aaryaman Kartha, Ahmed Masry, Mohammed Saidul Islam, Thinh Lang, Shadikur Rahman, Ridwan Mahbub, Mizanur Rahman, Mahir Ahmed, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty. Findings of the Association for Computational Linguistics: EACL 2026. 2026.

The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models

Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mizanur Rahman et autres

Information visualizations are powerful tools that help users quickly identify patterns, trends, and outliers, facilitating informed decision-making. However, when visualizations incorporate deceptive design elements—such as truncated or inverted axes, unjustified 3D effects, or violations of best practices—they can mislead viewers and distort …

ca (code pays fourni par la source)

Deploying Tiny LVLM Judges for Real-World Evaluation of Chart Models: Lessons Learned and Best Practices

Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub, Mizanur Rahman et autres

Large Vision-Language Models (LVLMs) with only 7B parameters have shown promise as automated judges in chart comprehension tasks. However, tiny models (<=2B parameters) still perform poorly as judges, limiting their real-world use in resource-constrained settings. To address this, we propose two approaches …

LLM-Based Data Science Agents: A Survey of Capabilities, Challenges, and Future Directions

Mizanur Rahman, Amran Bhuiyan, Mohammed Saidul Islam, Md Tahmid Rahman Laskar et autres

Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use, and multimodal reasoning across text, code, tables, and visuals. This survey presents the …

Learning or Cheating? Assessing Data Contamination in Large Vision-Language Models

Ahmed Masry, M. Firoz Ahmed, Ridwan Mahbub

Large Vision-Language Models (VLMs) have demonstrated remarkable capabilities in tasks that involve both visual and textual understanding, including chart interpretation, document comprehension, and geometric reasoning. However, concerns remain about whether these models truly generalize or if their performance on popular benchmarks is …

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