Introduction

Multimodal data presents a more comprehensive and natural form of information representation and communication in the real world. Our digital world is multimodal, combining different modalities of data such as text, audio, images, videos, animations, drawings, depth, 3D, biometrics, interactive content, etc. Multimodal data analytics algorithms often outperform single modal data analytics in many real-world problems.

Big Data technology has emerged as a key driver of the new industrial revolution. With the rapid advancement of Big Data technologies and their wide-ranging applications across various sectors, recent research has increasingly focused on multimodal data analysis. In this context, the integration of multimodal AI-driven Big Data has become a highly relevant and timely area of study.

This workshop aims to generate momentum around this topic of growing interest, and to encourage interdisciplinary interaction and collaboration between Natural Language Processing (NLP), computer vision, signal processing, machine learning, robotics, Human-Computer Interaction (HCI), bioinformatics, healthcare, and geospatial computing communities. It serves as a forum to bring together active researchers and practitioners from academia and industry to share their recent advances in this promising area.

Topics

Multimodal data modeling
Multimodal learning
Cross-modal learning
Multimodal Large Language Models (LLMs)
Multimodal data analytics
Multimodal big data infrastructure and management
Multimodal scene understanding
Multimodal data fusion and data representation
Multimodal perception and interaction
Multimodal benchmark datasets and evaluations
Multimodal information tracking, retrieval and identification
Multimodal object detection, classification, recognition, and segmentation
Multimodal AI Generation (text to image, image to text, video to text, text to video, etc.)
Language, vision, and sound (e.g., image/video searching and captioning, visual question answering, visual scene understanding, etc.)
Biometrics data mining (e.g., face recognition, behavior recognition, eye retina and movement, palm vein and print, etc.)
Multimodal applications (autonomous driving, cybersecurity, smart cities, intelligent transportation systems, industrial inspection, medical diagnosis, healthcare, social media, arts, etc.)

Important Dates

Cross-AI Deadlines

See Important Dates for all Cross-AI paper and abstract submission deadlines.

IEEE Big Data Workshop Deadlines

IEEE Big Data Workshop Deadline	IEEE Workshop Milestone
October 19, 2026	Full/Short Paper Submission
October 26, 2026	Poster/Demo Paper Submission
November 2, 2026	Acceptance Notification
November 14, 2026	Pre-recorded Video Submission; Camera-Ready Submission
November 14, 2026	IEEE Author Registration Deadline
December 14–16, 2026	IEEE Big Data Workshops & Cross-AI Symposium 2026

Archival papers submitted to the IEEE Big Data Workshop must meet these deadlines.

Author and Submission Guidelines

Please follow Author and Submission Guidelines to submit your work.

All submissions are made through the AirBalloon conference management system and evaluated through a unified process. Upon acceptance, authors may choose to publish in the IEEE Big Data conference proceedings or the indexed Cross-AI conference proceedings, with the corresponding registration fees. Please refer to the respective official registration pages (Cross-AI registration and the IEEE Big Data 2026 website) for current fees and registration details. At least one author must register for the corresponding event for the paper to be published. Abstract submissions are non-archival: not published in the proceedings; abstract posted on the conference platform and in the Cross-AI non-archival repository; previously published work is posted on the conference platform only.

Committee

Program Chairs

Lindi Liao, George Mason University, USA (Chair)
Yanjia Zhang, IntelliSky, USA (Co-Chair)

Program Committee

Zhiqian Chen, Mississippi State University, USA
Naresh Erukulla, Macy’s Inc., USA
Kaiqun Fu, South Dakota State University, USA (Co-Chair)
Maryam Heidari, George Mason University, USA
Ge Jin, Purdue University, USA
Aishwarya Jadhav, University of California, Berkeley, USA
Ashwin Kannan, Amazon, USA
Achin Kulshrestha, Google Inc., USA
Kevin Lybarger, George Mason University, USA
Ahmad Mousavi, American University, USA
Abhimanyu Mukerji, Amazon, USA
Chen Shen, Google Inc., USA
Arpit Sood, Meta, USA
Sanjeev Singh, Meta, USA
Gregory Joseph Stein, George Mason University, USA
Alex Wong, Yale University, USA
Yingfan Xu, Oklahoma State University, USA
Marcos Zampieri, George Mason University, USA

Multimodal AI Group

This group serves as a forum for notices and announcements of interest to the Multimodal AI (MMAI) community. This includes news, events, calls for papers, calls for collaborations between academia and industry, dataset releases, employment-related announcements, etc.

Welcome to subscribe to the Multimodal AI group.

Previous Workshops

If you are interested in serving on the workshop program committee or paper reviewing, please apply online.