Introduction

The rise of agentic AI marks a fundamental shift in how Big Data is leveraged and governed. By autonomously reasoning over vast and complex datasets, agentic AI moves Big Data beyond storage and analytics toward self-directed intelligence, enabling real-time insight generation, adaptive decision-making, and continuous evolution of data-driven systems at global scale.

AAI-BD is the first workshop in the world dedicated to this emerging area, bringing together leading researchers, practitioners, and industry experts to explore architectures, algorithms, and applications of autonomous AI systems for Big Data. It provides a high-impact forum to advance research, foster cross-disciplinary collaboration, and define the future of agentic AI in large-scale data-driven environments.

The workshop focuses on novel AI models, multi-agent systems, distributed AI architectures, and edge AI integration, with applications across domains such as finance, healthcare, IoT, robotics, smart cities, transportation, cybersecurity, environmental science, supply chain management, GIS, and creative and physical domains including arts, music, dance, and sports. It aims to spark discussion on scalable and autonomous AI systems, attract high-quality submissions bridging theoretical foundations and practical implementations, and highlight emerging challenges and transformative opportunities in the design, deployment, and evaluation of agentic AI systems.

Topics

This open call for papers invites original contributions on theory, methods, architectures, and applications of agentic AI for Big Data. Submissions may focus on technical, conceptual, or applied research, including but not limited to the areas below.

Technical topics and methods

Architectures for agentic AI on Big Data
Autonomous multi-agent systems for data-intensive tasks
AI agents using distributed or NoSQL databases as memory
Integration of LLMs, generative AI, and multimodal AI in autonomous agents
Edge AI for real-time autonomous decision-making
Distributed and federated agentic AI systems
Agentic AI for autonomous data curation and cleaning
Explainability, interpretability, and trust in agentic AI systems
Evaluation frameworks, benchmarks, and performance metrics for agentic AI
Resource-efficient AI for large-scale data processing
Human-AI collaboration in autonomous systems
Security, privacy, and ethical considerations in agentic AI
Reinforcement learning and planning in multi-agent Big Data environments
Simulation-based testing for autonomous AI in data-rich contexts

Applications

Robotics: Multi-robot coordination, autonomous exploration, adaptive task planning
Arts & Entertainment: AI-generated music, dance choreography, sports analytics, performance optimization, creative AI applications
Cybersecurity: Autonomous threat detection, anomaly identification, automated response systems
Digital Content & Social Media: Automated content analysis, misinformation detection, recommendation systems
Finance: Automated trading, risk assessment, fraud detection, portfolio optimization
Healthcare: Clinical decision support, autonomous medical imaging analysis, patient monitoring
Internet of Things (IoT): Intelligent sensor networks, real-time edge processing, predictive maintenance
Transportation: Autonomous vehicle coordination, route optimization, traffic prediction, logistics planning
Smart Cities: Traffic management, energy optimization, urban planning using Big Data
Geographic Information Systems (GIS): Autonomous spatial data analysis, urban modeling, mapping, environmental monitoring
Supply Chain & Logistics: Autonomous inventory management, demand prediction, route optimization
Scientific Research: Autonomous analysis of large-scale experimental data, genomics, high-energy physics datasets
Environmental Science: Climate modeling, ecosystem monitoring, disaster prediction and response
Education: Personalized learning platforms, intelligent tutoring systems, data-driven curriculum optimization

Important Dates (AoE)

Milestone	Deadline for IEEE Big Data Publishing	Deadline for Cross-AI (IEEE, EI-indexed, or non-archival)
Full/Short Paper Submission	~~October 19, 2026~~ October 26, 2026 (extended)	November 2, 2026
Poster/Demo Paper Submission	October 26, 2026	November 9, 2026
Abstract Submission (Poster-Only, Demo-Only, Lightning Talk, Industry Talk, Previously Published Work)	—	November 9, 2026
Acceptance Notification	Before November 12, 2026	Initial acceptance no later than November 23, 2026
Camera-Ready (archival)/Revised Versions (non-archival)	November 26, 2026	November 30, 2026
Pre-recorded Video/Presentation Materials	November 26, 2026	November 30, 2026
Author Registration Deadline	November 25, 2026	November 30, 2026

Submission Guidelines

Before submitting, read the Author Guidelines, the Submission Guidelines, the Publication and Media Hosting guidelines, and all policies, then complete the Checklist for Initial Submission. Submissions that do not follow the guidelines and policies are desk-rejected under the Desk Rejection Policy.

Full and Short Papers are reviewed double-blind.

All submissions, including IEEE Big Data workshop papers and all Cross-AI submissions, must be made through the new AirBalloon conference management system. Only submissions made there enter the review process.

Submit →

Registration

All submissions are evaluated through a unified process. At least one author of each accepted submission must register and present the work. There are two separate registrations, depending on where the work is published:

Published in	Register with	Deadline	Archival Paper Fees
Cross-AI 2027 Proceedings by IEEE, EI-indexed Cross-AI 2026 Proceedings, or non-archival	Cross-AI registration	November 30, 2026	From $200 (details)
IEEE Big Data 2026 proceedings	IEEE Big Data 2026 registration	November 25, 2026	Estimated from $850 (details)

Register with the registration that matches where each accepted work is published. IEEE Big Data registration covers only attending the online Cross-AI Symposium 2026. Cross-AI publication and media hosting require Cross-AI registration. Authors with accepted papers in both IEEE Big Data and Cross-AI must register for both.

Committee

Program Chairs

Lindi Liao, George Mason University, USA (Chair)
Callie Liao, Stanford University, USA (Co-Chair)

Program Committee

Zhiqian Chen, Mississippi State University, USA
Kaiqun Fu, Texas Christian University, USA
Maryam Heidari, George Mason University, USA
Fanchun Jin, Google Inc., USA
Ge Jin, Purdue University, USA
Ahmad Mousavi, American University, USA
Chen Shen, Google Inc., USA
Gregory Joseph Stein, George Mason University, USA
Alex Wang, Yale University, USA
Yanjia Zhang, Intellisky, USA
Zhenwei Zhang, University of Maryland, USA

External Industrial Reviewers

TBD

To serve on the workshop program committee or as a reviewer, please apply online. Applicants may select multiple workshops relevant to their expertise. Approved applicants may be invited to review papers. Those providing high-quality reviews will be appointed to the Program Committee, and others will be acknowledged as external reviewers.