Workshop on Machine Intelligence in Networked Data and Systems (MINDS)

Call for Papers

Important Dates

Workshop Overview

As connectivity, computing, and storage are getting cheaper, we are seeing more opportunities for data-driven approaches for networked data and systems. The adaptation of machine learning, artificial intelligence, and data analytics techniques in these networked systems is set to transform and disrupt many areas of business and everyday human life. The Workshop on Machine Intelligence in Networked Data and Systems (MINDS) is co-located with COMSNET 2026 and aims to bring together researchers and practitioners to explore and investigate this interworking of machine learning, big data analytics, and networked systems for various application domains.

MINDS welcomes original research submissions that define challenges, report experiences, or discuss progress toward design and solutions that integrate machine learning, artificial intelligence, data analytics, deep learning, mobile systems, and networked systems in various application areas. These application areas include but are not limited to healthcare, environment, retail, transportation, life sciences, e-commerce and cloud services. Contributions describing techniques applied to real-world problems and interdisciplinary research involving novel networking architectures, system designs, IoT systems, big data systems that use techniques from machine learning, artificial intelligence, deep learning, and data analytics as the core component are especially encouraged.

The topics of interest include, but are not limited to:

• Networks
  • ML for network management, orchestration, and control
  • ML for network security and privacy
  • Mining of large-scale network measurements
  • Networks for AI through programmability and hardware acceleration on the data plane (in-network computing and in-network ML)
  • LLMs for intent-based networking (IBN) and network configuration
  • AI-based assurance for self-managed networks (self-healing, self-configuration, self-optimization, self-protection)
  • ML for transport and transport for ML applications
  • ML for IoT, smart cities, and vehicular networks
  • AI/ML for JSAC and digital twinning
  • LLMs for goal/task oriented and semantic communications (SemCom)
• Systems
  • ML for systems management, performance modelling, and optimizations
  • ML-driven resource provisioning, scheduling, and scaling techniques
  • ML for sustainable resource management in cloud and edge systems
  • ML-driven systems using mobile phones, embedded devices, and sensor networks
  • Workflow management in edge-cloud compute continuum
• Applications
  • Design and implementation of intelligent systems for applications such as home automation, self-driving vehicles, driver assistance systems, supply chain, and logistics
  • Cloud-based machine and deep learning applications in retail and e-commerce
  • Machine learning systems for healthcare, weather modeling, financial services, life sciences, and environmental monitoring
  • Applications of machine learning in IoT, IIoT, manufacturing, and supply chain optimisation
  • Experiences in managing wearable devices, smart-home systems and mobile sensor networks
  • Anomaly and outlier detection in social networks

Submission Guidelines

• MINDS invites submission of original work not previously published, or under review at another conference or journal.
• Submissions (including title, abstract, all figures, tables, and references) must be no greater than 6 pages in length.
• Reviews will be double-blind: Information about the authors will not be shared with the reviewers during the review process. The submitted paper should be anonymous and not have any reference to the authors' names or institutions.
• Submissions must follow the formatting guidelines as given on the IEEE Website; and those that do not meet the size and formatting requirements will not be reviewed.
• All papers must be in Adobe Portable Document Format (PDF) and submitted through the MINDS Workshop submission site on EDAS.
• All workshop papers will appear in the conference proceedings and be submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases.

Papers can be submitted through EDAS : <TBA>

Technical Program Committee

• Dr. Rohit Kumar, Assistant Professor, DTU India
• Dr. Prem Singh, Assistant Professor, IIITB
• Dr. Manuj Mukherjee, Assistant Professor, IIIT Delhi
• Dr. Dimitrios Dechouniotis, Postdoctoral Fellow, National Technical University of Athens (NTUA), Greece
• Dr. Ram Prasad Padhy, IIT Bhubaneswar
• Dr. Pranay Saha, IIT ISM
• Dr. John Violos, Postdoctoral Research Fellow, Information Technologies Institute (ITI)
• Ramy Mohamed, PhD Candidate and Research Assistant, Carleton University
• Dr. Rahul Singh, Indian Institute of Science
• Dr. Subhasish Dhal, Assistant Professor, Indian Institute of Information Technology Guwahati
• Dr. Maria Diamanti, Postdoctoral Fellow, National Technical University of Athens (NTUA), Greece
• Dr. Md Yusuf Uddin, Assistant Professor, University of Missouri Kansas City (UMKC)
• Grigorios Kakkavas, PhD Candidate, National Technical University of Athens
• Dr. Anurag Satpathy, Postdoctoral Fellow, Missouri University of Science and Technology
• Dr. Andrey Chechulin, St. Petersburg Federal Research Center of Russian Academy of Sciences (SPC RAS)
• Dr. Ioannis Dimolitsas, Postdoctoral Fellow, National Technical University of Athens (NTUA), Greece
• Dr. Bishakh Ghosh, Indian Institute of Technology Kharagpur
• Dr. Aroosa Hameed, Postdoctoral Fellow, Carleton University
• Dr. Shubha Nath, Assistant Professor, Indian Institute of Information Technology Guwahati
• Dr. Dimitrios Spatharakis, Postdoctoral Fellow, National Technical University of Athens (NTUA), Greece
• Dr. Konstantinos Tsitseklis, Postdoctoral Fellow, National Technical University of Athens (NTUA), Greece
• Dr. Mridula Verma, Assistant Professor, IDRBT
• Dr. Debarati B. Chakraborty
• Dr. Dibyasundar Das
• Dr. Subhrendu Chattopadhyay, Assistant Professor, IDRBT

Workshop Co-Chairs

Marios Avgeris

University of Amsterdam
Netherlands

Balakrishnan Chandrasekaran

University of Amsterdam
Netherlands

Shashikant Ilager

University of Amsterdam
Netherlands