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Special Session 3:
Multi-modal Machine Learning in Practice: Algorithms and Applications

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This special session aims to highlight cutting-edge research in the development and practical deployment of multimodal machine learning algorithms that integrate and process heterogeneous data types—such as text, image, audio, video, and sensor signals—to address complex, real-world challenges. By leveraging the complementary strengths of multiple modalities, these systems enable more robust, context-aware, and intelligent solutions across a wide range of domains, including healthcare, cybersecurity, robotics, smart environments, transportation, surveillance and so on. The session invites contributions where algorithmic innovations in multi-modal AI systems are developed in the context of real-world applications, leading to tangible improvements in performance, scalability, and interpretability. We particularly welcome demonstrating multi-modal AI in diverse domains such as healthcare, cybersecurity, robotics, smart homes, transportation, and surveillance. We also encourage submissions focusing on educational tools, mobile and web-based AI systems, multi-modal chatbot development, and cross-modal retrieval tasks.

Scope and topics:

Topics of interest include, but are not limited to:

• Vision-language and multi-modal foundation models
• Generative models for multi-modal synthesis
• Multi-modal representation alignment and fusion techniques
• Transfer learning and fine-tuning strategies in multi-modal deep learning
• Cross-modal retrieval and matching (e.g., image-to-text, audio-to-video)
• Domain adaptation and self-supervised learning for multi-modal data
• Explainable, interpretable, and trustworthy multi-modal ML systems
• Applications in cybersecurity, medical imaging, transportation, robotics, and smart environments
• Mobile, web, and edge deployment of multi-modal systems
• Real-time architectures and lightweight multi-modal models for deployment
• Benchmark datasets, framework, and reproducibility in multi-modal ML

Chairs:

• Chair Emails

Dr. Md Belayat Hossain: belayat@cs.siu.edu
Dr. Abdur Rahman Bin Shahid:shahid@cs.siu.edu


• Chair Biographies

Dr. Md Belayat Hossain, Assistant Professor of Computer Science, School of Computing, Southern Illinois University, Carbondale, IL – 62901.
Dr. Abdur Rahman Bin Shahid, Assistant Professor of Computer Science, School of Computing, Southern Illinois University, Carbondale, IL – 62901.

Technical Committee

• Dr. Kento Morita, Mie University, Mie, Japan
• Dr. Hani M Alnami, Jazan University, Jazan, KSA
• Dr. Shahriar Shahabuddin, Oaklahoma State University, OK, USA
• Dr. Nur Imtiazul Haque, University of Cincinnati, OH, USA
• Dr. Hussein Zangoti, Jazan University, Jazan, KSA
• Dr. Khaled R Ahmed, Southern Illinois University, IL, USA
• Dr. Samia Tasnim, University of Toledo, TX, USA
• Dr. Md Farhadur Reza, Eastern Illinois University, IL, USA
• Dr. Shahriar Badsha, Ford Motor Company, MI, USA
• Dr Razieh Ganjee, University of Pittsburgh, Pittsburgh, USA
• Dr. Nur Imtiazul Haque, University of Cincinnati, OH, USA
• Dr. Khaled Mohammed Saifuddin, Northeastern University, MA, USA
• Dr. Alvi Ataur Khalil, Florida International University, FL, USA

Paper Submission Instructions

All papers will be double-blind reviewed and must present original work.

• CMT Submission Site
• Select the track: Special Session 3: Multi-modal Machine Learning in Practice: Algorithms and Applications

Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from:

• IEEE website

Keydates

• Submission due date: August 20, 2025
• Notification of Acceptance: September 10, 2025
• Camera Ready Papers: September 20, 2025
• Pre-registration: September 20, 2025
• Conference: December 3-5, 2025

Registration

In order for your paper to be presented and published in the proceedings, you must register to the conference.

Paper Presentation Instructions

The papers submitted to this track will be presented in person as part of the conference. There is no virtual presentation for this session.

Note: More details about this special session can be explored here.