Minds Workshop
Workshop on Machine Intelligence in Networked Data and Systems (MINDS)
Detailed Schedule
Yogesh Simmhan
IISc Bangalore, IndiaVisit Homepage
6th January 2026, 9:40 - 10:40 AM (IST)
Sustainable Federated Intelligence for Networked SystemsAs networked systems increasingly host data at the edge, machine learning must balance privacy, scalability and sustainability. This talk explores advances in Federated Learning (FL) across multiple dimensions: (i) Programmability and scaling through Flotilla, a modular and resilient FL platform enabling synchronous and asynchronous strategies with fault tolerance and scale-out to 1,000+ clients; (ii) Energy-aware FL through FedJoule, which optimizes client selection and power modes for edge accelerators under global energy budgets, and (iii) A privacy-preserving FL pipeline for demand forecasting in microgrids, combining consumer clustering and custom loss functions to generalize thousands of unseen buildings. These illustrate design patterns that apply broadly to networked machine intelligence, paving the way for research into and translation of sustainable, secure and scalable AI on the edge.
Yogesh Simmhan is an Associate Professor in the Department of Computational and Data Sciences, Indian Institute of Science, Bangalore. His research explores scalable software platforms, algorithms and applications on distributed systems, which span Cloud and Edge Computing, Temporal Graph Processing, and Scalable Machine Learning to support emerging applications. He has published over 100 peer-reviewed papers, and won recognitions such as the Best Paper at IEEE CLOUD 2019, IEEE TCSC SCALE Challenge Award in 2019, the Swarna Jayanti Fellowship in 2019 and IEEE TCSC Award for Excellence in Scalable Computing (Mid Career Researcher) in 2020. He is an Associate Editor of Future Generation Computing System (FGCS), and earlier served on the editorial board of Journal of Parallel and Distributed Systems (JPDC) and IEEE Transactions on Cloud Computing. Yogesh has a Ph.D. in Computer Science from Indiana University, Bloomington, and was previously a Research Assistant Professor at the University of Southern California (USC), Los Angeles, and a Postdoc at Microsoft Research, San Francisco. He is a Distinguished Member of ACM, a Distinguished Contributor of the IEEE Computer Society and serves on the ACM India Executive Council.
Vijay Gabale
Infilect Inc.Visit Homepage
6th January 2026, 2:00 - 3:00 PM (IST)
The software development, as we know, has fundamentally changed. Every part of the software system, be it databases, backend, frontend, QA is undergoing systemic change where AI agents are making it simpler, faster, and better to produce software. The AI models themselves are being trained and deployed by AI agents. In this talk, I will take a real-life software product, and break it apart to talk about its journey, writing and rewriting software, with humans and with Agentic-led workflows. I will specifically touch upon Image Recognition or Computer Vision systems and how these systems have evolved from training classifiers to making use of embeddings to now RAGs, and how AI agents can prepare almost perfect ground truth to do auto-evaluation.
Vijay Gabale serves as the Co-founder & Chief Technology and Product Officer at Infilect Inc., a pioneering force in global retail visual intelligence. Leveraging cutting-edge Image Recognition and AI technologies, Infilect aims to revolutionize the retail landscape by addressing the complex challenges encountered by CPG brands in real time. Vijay has been the brain behind Infilect's flagship product InfiViz. He also brought forward a fistful of the biggest innovations in the field of Image Recognition namely Generative Image Recognition, an Automatic product catalog creation tool called InfiBrain, pricing, and promotion detection using AI-powered Image Recognition to name a few. He also led one of the most coveted solutions in the field of CPG Retail Execution which is Photos to PSA Files. Vijay Gabale earned his PhD from IIT Bombay, specializing in wireless platforms and AI systems. He has held key roles in prominent global technology firms such as IBM Research, distinguishing himself as both a respected technocrat and a forward-thinking leader. Among his notable accomplishments are pioneering demonstrations of the world's inaugural voice network on Zigbee in the USA, groundbreaking contributions to Deep Neural Network architecture for multi-object detection, and his keynote address at the Heidelberg Laureate Forum in Germany, advocating for the societal advantages of AI. With over 6 patents granted and 15 research publications, he continues to drive innovation at the forefront of technology.
Accepted Papers
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An Explainable Attention-Driven CNN Architecture for Reliable Infectious Disease Diagnosis
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A Hybrid Reinforcement Learning Based Intrusion Detection And Mitigation System for Encrypted Network Traffic
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SWiSS: Self-supervised Vision Transformer for Wideband Spectrum Segmentation
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Intent2Vec: Contrastive Learning for Predictive and Interpretable Intent Violation Detection in SDN
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Meta-agentic Framework for Software bug detection using Large Language Models
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Enhanced Channel Estimation in Near-Field XL-MIMO Using Deep Denoising Networks
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How Far is too Far? Fixing Vision of Autonomous Vehicles using Selective Super-Resolution
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Differentially Private Federated Autoencoder Framework for Securing Internet of Medical Things
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.
Call for Papers
Camera Ready Guidelines
Important Dates
| Paper Submission deadline: |
| Notification of Acceptance: |
| Camera-ready Submission: |
| Workshop Date: 6th January 2026 |
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: https://edas.info/N34100
For any queries, please contact the workshop chairs at comsnets.workshop@gmail.com
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
Vrije Universiteit Amsterdam
Netherlands
Shashikant Ilager
University of Amsterdam
Netherlands
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