Call for Workshop Papers

1st International Youth Cloud and AI System Innovation Workshop

Introduction

The 1st International Youth Cloud and AI System Innovation Workshop aims to provide a collaborative platform for young researchers, students, and early-career professionals to explore cutting-edge developments in cloud computing and artificial intelligence systems. This workshop seeks to foster innovation, knowledge exchange, and interdisciplinary collaboration among the next generation of technologists working at the intersection of cloud infrastructure and AI applications.

The workshop will bring together undergraduate students, graduate students, doctoral candidates, and young professionals (typically under 35 years of age) from academia and industry to present original research, share practical experiences, and discuss emerging challenges in designing, deploying, and optimizing cloud-based AI systems. Participants will have opportunities to engage with peers, receive feedback from experienced mentors, and build international networks that support future collaboration.

Key objectives include advancing technical knowledge in cloud and AI system design, promoting best practices for scalable and efficient AI deployments, encouraging innovative solutions to real-world problems, and cultivating the next generation of leaders in cloud and AI technology.

Topics

The workshop welcomes submissions and presentations on all aspects of cloud computing and AI system innovation, including but not limited to:

Software Systems for Cloud, AI and All: Distributed systems architectures, microservices and service-oriented architectures, cloud-native application development, database systems for AI workloads, stream processing and real-time systems, API design and management, system reliability and fault tolerance, performance monitoring and profiling tools, cloud resource scheduling and management systems, hybrid and multi-cloud orchestration platforms

Cloud Infrastructure for AI: Distributed training architectures, GPU/TPU cluster management, serverless computing for machine learning, containerization and orchestration for AI workloads, edge-cloud collaboration for AI applications

AI System Design and Optimization: Model compression and quantization techniques, efficient neural architecture design, automated machine learning (AutoML) systems, inference optimization and acceleration, energy-efficient AI computing

Scalable AI Applications: Large language models and foundation model deployment, computer vision systems at scale, real-time AI analytics and processing, federated learning and distributed AI, multimodal AI systems

Cloud-Native AI Development: MLOps and AI development pipelines, model versioning and lifecycle management, AI observability and monitoring, cloud-based data engineering for AI, collaborative AI development platforms

Security, Privacy, and Ethics: Secure AI model deployment, privacy-preserving machine learning, trustworthy AI systems, fairness and bias mitigation, responsible AI development practices

Emerging Technologies and Innovations: Quantum-classical hybrid systems, neuromorphic computing in the cloud, AI for cloud resource optimization, sustainable and green AI computing, novel AI accelerators and hardware

Any other interesting topics are also welcome to submit.

Important Dates
  • Submission deadline 30th April 2026
  • Notification deadline  15th May 2026
  • Camera-ready deadline 30th May 2026

Accepted and presented papers will be published alongside the main conference proceedings as a sub-section/chapter. Paper formats should, therefore, correspond to the templates of the publisher of the main conference.

How to Submit a Paper in Confy:
  1. Go to Confy+ website.
  2. Log in or sign up as a new user.
  3. Select your desired track.
  4. Click the ‘Submit Paper’ link within the track and follow the instructions.

Alternatively, go to the Confy+ homepage and click on “Open Conferences.”

Submission Guidelines:

  • All papers must be submitted in English. 
  • Submitted PDFs should be anonymized.

  • Previously published work cannot be submitted, nor can it be concurrently submitted to any other conference or journal. These papers will be rejected without review. 
  • Papers must follow the Springer formatting guidelines (available in the Author’s Kit section). 
  • Authors must read and agree to the Publication Ethics and Malpractice Statement.
  • As per new EU accessibility requirements, going forward, all figures, illustrations, tables, and images should have descriptive text accompanying them. Please refer to the document below, which will assist you in crafting Alternative Text (Alt Text)

HOW TO WRITE GOOD ALT TEXT

Papers should be submitted through EAI ‘Confy+‘ system, and have to comply with the Springer format (see Author’s kit section).

  • Workshop papers should be 6-11 pages in length.

All conference papers undergo a thorough peer review process prior to the final decision and publication. This process is facilitated by experts in the Technical Program Committee during a dedicated conference period. Standard peer review is enhanced by EAI Community Review which allows EAI members to bid to review specific papers. All review assignments are ultimately decided by the responsible Technical Program Committee Members while the Technical Program Committee Chair is responsible for the final acceptance selection. You can learn more about Community Review here.

Full information: https://www.springernature.com/gp/policies/book-publishing-policies

AI Authorship Policy

Large Language Models (LLMs), such as ChatGPT, do not currently satisfy our authorship criteria. Notably an attribution of authorship carries with it accountability for the work, which cannot be effectively applied to LLMs. We thus ask that the use of an LLM be properly documented in the Acknowledgements, or in the Introduction or Preface of the manuscript.

The use of an LLM (or other AI-tool) for “AI assisted copy editing” purposes does not need to be declared. In this context, we define the term “AI assisted copy editing” as AI-assisted improvements to human-generated texts for readability and style, and to ensure that the texts are free of errors in grammar, spelling, punctuation and tone. These AI-assisted improvements may include wording and formatting changes to the texts, but do not include generative editorial work and autonomous content creation. In all cases, there must be human accountability for the final version of the text and agreement from the authors that the edits reflect their original work. This reflects a similar stance taken on the AI generative figures policy, where it was acknowledged that there are cases where AI can be used to generate a figure without being concerned about copyright e.g. to generate a graph based on data provided by the author.

AI Authorship Guidance

Authors should familiarise themselves with the current known risks of using AI models before using them in their manuscript. AI models have been known to plagiarise content and to create false content. As such, authors should carry out due diligence to ensure that any AI-generated content in their book is correct, appropriately referenced, and follow the standards as laid out in our Book Authors’ Code of Conduct.

AI-generated Images Policy

The fast-moving area of generative AI image creation has resulted in novel legal copyright and research integrity issues. As publishers, we strictly follow existing copyright law and best practices regarding publication ethics. While legal issues relating to AI-generated images and videos remain broadly unresolved, Springer Nature journals and books are unable to permit its use for publication.

Exceptions:

  • Images/art obtained from agencies that we have contractual relationships with that have created images in a legally acceptable manner.
  • Images and videos that are directly referenced in a piece that is specifically about AI and such cases will be reviewed on a case-by-case basis.
  • The use of generative AI tools developed with specific sets of underlying scientific data that can be attributed, checked and verified for accuracy, provided that ethics, copyright and terms of use restrictions are adhered to.

* All exceptions must be labelled clearly as generated by AI within the image field.
As we expect things to develop rapidly in this field in the near future, we will review this policy regularly and adapt if necessary.Note: Examples of image types covered by this policy include: video and animation, including video stills; photography; illustration such as scientific diagrams, photo-illustrations and other collages, and editorial illustrations such as drawings, cartoons or other 2D or 3D visual representations. Not included in this policy are text-based and numerical display items, such as: tables, flow charts and other simple graphs that do not contain images. Please note that not all AI tools are generative. The use of non-generative machine learning tools to manipulate, combine or enhance existing images or figures should be disclosed in the relevant caption upon submission to allow a case-by-case review.

AI-generated Images Guidance

For more information on the inclusion of third party content (i.e. any work that you have not created yourself and which you have reproduced or adapted from other sources) please see Rights, Permissions, Third Party Distribution.

Papers must be formatted using the Springer LNICST Authors’ Kit.

Instructions and templates are available from Springer’s LNICST homepage:

Please make sure that your paper adheres to the format as specified in the instructions and templates.

When uploading the camera-ready copy of your paper, please be sure to upload both:

  • a PDF copy of your paper formatted according to the above templates, and
  • an archive file (e.g. zip, tar.gz) containing the both a PDF copy of your paper and LaTeX or Word source material prepared according to the above guidelines.
Workshop Organizers

Dr. Shakeela Sathish

Department of Computing  Technologies, School of Computing,  SRM Institute of Science and Technology, Kattankulathur, Tamilnadu,  India,

Professor Ovidiu Bagdasar 

School of Computing, University of Derby, UK

Workshop TPC members

Professor G. Niranjana
Department of Computing  Technologies, School of Computing,  SRM Institute of Science and Technology, Kattankulathur, Tamilnadu,  India,

Professor Ovidiu Bagdasar
School of Computing, University of Derby, UK

Dr Harry Yu
Data Science Research Centre, University of Derby, UK

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