Welcome to BIGML 2026

7th International conference on Big Data, Machine learning and Applications (BIGML 2026)

November 21 ~ 22, 2025, London, United Kingdom

Hybrid--Registered authors can present their work online or face to face New

Program Committee

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Accepted Papers

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Toronto, Canada

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Scope

7th International conference on Big Data, Machine learning and Applications (BIGML 2026) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning.

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Call for Papers


7th International conference on Big Data, Machine learning and Applications (BIGML 2026) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science, Engineering and Applications.

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


  • Big Data
  • Big Data Techniques, models and algorithms
  • Big Data Infrastructure and platform
  • Big Data Search and Mining
  • Big Data Security, Privacy and Trust
  • Big Data Applications, Bioinformatics, Multimedia etc
  • Big Data Tools and systems
  • Big Data Mining
  • Big Data Management
  • Cloud and grid computing for Big Data
  • Machine Learning and AI for Big Data
  • Big Data Analytics and Social Media
  • 5G and Networks for Big Data
  • Machine Learning
  • Machine Learning Applications
  • Learning in knowledge-intensive systems
  • Learning Methods and analysis
  • Learning Problems
  • Deep Learning

Paper Submission

Authors are invited to submit papers through the conference Submission System by . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).

Proceedings

Hard copy of the proceedings will be distributed during the Conference. The softcopy will be available on AIRCC Digital Library

Sponsors