Special Track – Emerging AI Technologies and Big Data


The convergence of emerging artificial intelligence (AI) technologies and the proliferation of big data has revolutionized various industries, from healthcare to finance, from manufacturing to entertainment. This dynamic intersection presents a large number of opportunities and challenges worthy of scholarly inquiry and discussion. We invite researchers, practitioners, and scholars from diverse disciplines to contribute to this track focusing on the synergistic relationship between emerging AI technologies and big data analytics.


Track Chairs:

Priyanka Chawla, Guanqiu Qi



  1. Generative AI and Big Data:
    • Applications of generative adversarial networks (GANs) and variational autoencoders (VAEs) in generating synthetic data for training models.
    • Ethical considerations and implications of using generative AI in data augmentation and synthesis.

  2. Stable Diffusion Models and Big Data:
    • Exploring the applications of stable diffusion models in handling and analyzing large-scale datasets.
    • Scalability challenges and solutions for implementing stable diffusion models in big data environments.

  3. Reinforcement Learning and Big Data:
    • Leveraging reinforcement learning algorithms for extracting insights from massive datasets.
    • Case studies and applications of reinforcement learning in optimizing big data processing pipelines.

  4. Federated Learning and Distributed Big Data:
    • Federated learning approaches for training AI models across distributed big data sources.
    • Privacy-preserving techniques and security considerations in federated learning environments.

  5. Deep Learning Architectures for Big Data Analytics:
    • Novel deep learning architectures tailored for analyzing and extracting knowledge from big data.
    • Performance optimization techniques for deep learning models in handling massive datasets.

  6. Hybrid AI Approaches for Big Data Challenges:
    • Integration of symbolic AI and machine learning techniques for addressing complex big data problems.
    • Hybrid AI systems for anomaly detection, pattern recognition, and predictive analytics in big data contexts.

  7. Real-world Applications and Case Studies:
    • Success stories and lessons learned from deploying AI technologies in conjunction with big data analytics.
    • Industry-specific applications of emerging AI technologies and big data integration.