7th IEEE International Workshop on Deep Analysis of Data-Driven Applications (DADA  2025)

The goal of the workshop

The workshop aims to identify challenging and novel applications of deep learning algorithms to address scientific and engineering problems. The workshop fosters deep learning techniques to modeling and analyzing data-driven applications. It creates a venue for researchers from academia and industry to share their intellectual ideas and experiences with respect to challenges, novel problems, innovative applications, and creative solutions regarding the applications of deep learning to scientific and industrial problems driven by data. 

Workshop theme

AI/ML/LLMs-based application systems, and more notably the applications of Large Language Models (LLMs) are becoming the mainstream for software industry. The recent advancement in big data generation and management, LLMs, Transformer-based techniques has created an avenue for decision makers to utilize these huge data collected from many application domains for different purposes. Application developers and data scientists have utilized conventional machine learning techniques for a long time. However, with the advancement of deep learning paradigm, developers and decision makers are able to learn more about their data and then explore and model hidden features for prediction and analysis purposes. The new trends of practices in developing data-driven application systems and decision-making algorithms seek adaptation of deep learning algorithms and techniques in many application domains including AI/ML/LLMs-based software systems and applications and variety of scientific domains. An interesting aspect of adaptation of deep learning algorithms to such problems is that new challenging problems can be identified, and deep learning algorithm can be innovatively adapted to address the discovered problems are explored. 

Scope of the workshop

Researchers and practitioners all over the world, from both academia, research institute, and industry, working  in the area of data analysis and data-driven application domains using deep learning approaches are invited to discuss the state of the art solutions, novel issues, recent developments, applications, methodologies, techniques, experience reports, and tools for the development and use of deep learning in their application  domains. Topics of interest include, but are not limited to, the following applications of deep learning to: 

• Application and theory of Large Language Models (LLMs) 
• Intelligent data analysis  
• AI/ML/LLMs-based software development and analysis 
• Smart businesses and intelligent financial systems and applications 
• Time series modeling and prediction 
• Generative adversarial modeling of problems 
• Natural language processing  
• Security and privacy  
• Attention-based networks  
• Transfer learning
• Transformer-based analysis and applications

Workshop organizer(s)

Akbar Namin
Texas Tech University
Email: akbar.namin@ttu.edu

Program Committee

Yulei Pang
Southern Connecticut State University, USA.

Sima Siami-Namini
Johns Hopkins University, USA.

Akbar Namin
Texas Tech University, USA.

Luis Felipe Gutierrez Espinoza
Microsoft, USA

Prerit Datta
Coastal Carolina University, USA.

Long Nguyen
University of Louisville, USA.

Vinh T. Nguyen
University of Information and Communication Technology, Vietnam.