Call for Chapters

Sustainable AI: Methods and Scientific Applications

To be published by World Scientific Publishing

Editors: J. Senthilnath, Xiaoli Li, and Yung-Hsiang Lu

While generative AI has revolutionized various domains, its widespread adoption poses challenges due to the substantial computational resources it demands. Concerns about the environmental ramifications, stemming from high energy consumption and carbon emissions, have spurred the emergence of sustainable AI—a paradigm emphasizing the optimization of computational efficiency for impactful outcomes.

This book advocates for a transition towards sustainable AI, urging researchers to explore strategies that minimize energy usage and environmental impact. Its major focus is on surveying fundamental capabilities of Sustainable AI, focusing on data-efficient learning, resource-efficient learning, and hybrid learning approaches. Through a comprehensive examination of technical hurdles in sustainable AI research and implementation, the aim is to render sustainable AI a practical and attainable solution for scientific endeavors.

The advanced capabilities of sustainable AI hold immense potential across diverse scientific use cases. Specifically, the book delves into scientific applications such as semiconductor failure analysis, solar cell optimization, catalyst screening, and the development of low-carbon technologies. By elucidating these use cases, it seeks to underscore the tangible benefits and real-world applicability of sustainable AI methodologies.

Topics of interest include, but are not limited to, the following:

Introduction to Sustainable AI
  • Sparse data regime
  • Learning-based methods
  • Optimization techniques
  • Efficient learning
    • Data-efficient learning
  • Active learning
  • Semi-supervised learning
  • Foundational models
  • Prompt-based learning
  • Transfer Learning
  • Domain adaptation
  • Domain generalization
    • Resource-efficient learning
  • Self-evolving architecture
  • Online learning
  • Online continual learning
  • Knowledge Distillation
  • Network pruning and quantization
  • Bayesian optimization
    • Hybrid-efficient learning
  • Evolving unsupervised representation
  • Autonomous deep model compression
  • Physics-embedded model compression
  • Kalman filter embedded recurrent learning
    • Sustainable AI for Scientific Applications
  • Sustainable AI for catalyst screening
  • Sustainable AI for solar cell optimization
  • Sustainable AI for semiconductor failure analysis
  • Sustainable AI for low-carbon technologies

    • Submission Procedure

    Interested authors are invited to submit a brief one-page summary of the proposed chapter clearly identifying the main objectives of their research before May 1, 2024. Authors of the accepted proposals will be notified and provided with detailed guidelines. Full chapters are to be submitted by Aug 31, 2024. All manuscripts will be thoroughly reviewed. All the chapter authors of this book are entitled to get access to the electronic version of their work.

    In addition, we are pleased to inform you that some of the selected papers will be published in the journal “World Scientific Annual Review of Artificial Intelligence”.

    The proposals are to be submitted electronically to the editors (J_Senthilnath@i2r.a-star.edu.sg, xlli@i2r.a-star.edu.sg, and yunglu@purdue.edu).

    Important Dates

    Guest Editors