IEEE Punecon-2025 Tracks:

• Track 1: Emerging Computing Paradigm
• Track 2: Systems Engineering for AI-Driven Applications
• Track 3: Security, Ethics & Governance
• Track 4: Disruptive Technologies & Future Directions
• Track 5: Generative Social Engineering
• Track 6: Platforms, Frameworks, and Developer Ecosystems
• Track 7: Bioinformatics and Computational Biology
• Track 8: Toolkits, Frameworks, and Platforms for Generative Engineering
• Track 9: Foundations of Intelligent Systems

Track Details:

Track 1: Emerging Computing Paradigm

Explores cutting-edge computational models driving the future—from quantum and neuromorphic computing to edge intelligence—enabling scalable and energy-efficient generative technologies. Exploring the next frontier in computation. Pioneering the computational backbone of generative intelligence.

• Quantum and Neuromorphic Computing
• Edge, Fog, and Swarm Intelligence
• Distributed Computing and Collaborative Platforms
• AI for High-Performance and Cloud-Native Systems
• Quantum Computing and Quantum AI
• Neuromorphic and Brain-Inspired Computing
• Edge AI and Energy-Efficient Architectures
• Hyperdimensional & Holographic Computing
• Synthetic Neural Systems

 Track 2: Systems Engineering for AI-Driven Applications

Focuses on integrating generative AI into cyber-physical systems, digital twins, smart factories, and autonomous infrastructures across sectors like manufacturing, healthcare, and transportation. Real-world systems that integrate AI at the edge, cloud, and device layers. Engineering the infrastructure and intelligence behind smart systems.

• Computer Vision and Sensor Fusion
• AI in Industrial, Automotive, and Embedded Systems
• Smart Cities, IoT, and Adaptive Environments
• Robotics, Actuation, and Real-Time Decisioning
• Digital Twins and Simulation-Driven AI
• Smart Factories and Industrial IoT
• Autonomous Systems: Robotics, Drones, Vehicles
• Cyber-Physical Systems and Human-Machine Interaction
• Generative AI for Healthcare, Manufacturing, and Logistics

 Track 3: Security, Ethics & Governance

Addresses the ethical, legal, and social challenges of generative AI—including misinformation, privacy, bias, and responsible innovation—while exploring frameworks for global governance and regulation. Policy, security, and human alignment in an AI-driven world. Ensuring trustworthy, transparent, and secure generative technologies.

• Ethics in Generative AI
• Trust, Bias, and Accountability
• Cybersecurity in Intelligent Systems
• Governance, Regulation, and Public Policy
• Deepfake Detection and Synthetic Media Validation
• AI Policy, Regulation, and Global Governance
• Privacy-Preserving Generative AI
• Fairness, Bias Mitigation, and Ethical Frameworks
• Responsible Deployment and Compliance Models

 Track 4: Disruptive Technologies & Future Directions

Highlights interdisciplinary breakthroughs shaping the next decade, such as metaverse ecosystems, holographic interfaces, and AI-augmented reality for education, defense, and space. The convergence of tech frontiers that will define the next decade. Exploring revolutionary ideas and cross-disciplinary breakthroughs.

• Distributed Ledger Technologies
• Human-AI Augmentation
• Cross-domain Intelligence Systems
• Convergence of AI, AR/VR, and Metaverse
• Tech Foresight and Futures Thinking
• AI in Metaverse and Virtual/Augmented Reality
• Holography, Mixed Reality, and Immersive Interfaces
• AI for Space, Defense, and Climate Engineering
• Cross-Domain Intelligence and Autonomous Discovery

 Track 5: Generative Social Engineering

Investigates the impact of generative AI on human interaction, behaviour manipulation, and communication—covering deepfakes, persuasion algorithms, synthetic media, and digital influence strategies. Understanding the societal dynamics influenced by generative content.

• Generative AI: Models and Techniques
• Generative Social Engineering and Behavioral AI
• Generative Sustainable Systems
• Large Language Models & Diffusion Systems
• Prompt Engineering and Human-AI Collaboration
• Behavioral Influence and Digital Persuasion
• NLP for Narrative Shaping and Sentiment Control
• Social Media Bots and Propaganda Detection
• Generative Memes, Avatars, and Virtual Personas
• Synthetic Journalism and AI-Driven Storytelling

 Track 6: Platforms Frameworks & Developer Ecosystems

Covers tools, SDKs, APIs, and cloud-native platforms for building, training, and deploying generative systems—including AutoML, open-source stacks, scalable MLOps, and ethical prompt engineering. Engineering, building, and scaling with the right tools and platforms.

Tools and systems empowering the creators of generative intelligence.

• Toolkits and Frameworks for Generative AI
• Scalable AI/ML Pipelines
• Open Source and Low-Code Platforms
• Deployment, Monitoring, and MLOps
• LLM Toolkits and Prompt Engineering Pipelines
• AutoML and No-Code/Low-Code GenAI Platforms
• Open Source Libraries and API Frameworks
• Scalable MLOps and Cloud-Native Architectures
• GPU/TPU Optimization and Model Compression

Track 7: Bioinformatics and Computational Biology

With the exponential growth of biological data and the increasing convergence of computer science and life sciences, there is a critical need for platforms that bring together researchers, practitioners, and academicians working in Bioinformatics and Computational Biology. This track will focus on the latest advancements in computational methods, data-driven insights, and modelling techniques that are transforming the life sciences, biotechnology, and healthcare sectors. The track will invite high-quality research papers in, but not limited to, the following areas:

• AI/ML/Deep Learning applications in Bioinformatics data
• Genomics, Proteomics, and Multi-omics data integration
• Next-Generation Sequencing (NGS) data analytics
• Biomedical Informatics and Precision Medicine
• Computational Drug Discovery and Repurposing
• Medical Image Analysis
• Network Pharmacology
• Biodiversity and Conservation Informatics
• Bio-Computing, Bio-AI Integration and Genetic Algorithm Applications
• General Computational Biology

Track 8: Toolkits, Frameworks, and Platforms for Generative Engineering:

• Ethics in Generative AI
• Trust, Bias, and Accountability
• Cybersecurity in Intelligent Systems
• Governance, Regulation, and Public Policy
• Deepfake Detection and Synthetic Media Validation
• AI Policy, Regulation, and Global Governance
• Privacy-Preserving Generative AI
• Fairness, Bias Mitigation, and Ethical Frameworks
• Responsible Deployment and Compliance Models

Track 9: Foundations of Intelligent Systems       

Delves into the core models powering generative AI—foundation models, LLMs, GANs, diffusion models, and multimodal learning—with a focus on theory, architecture, and optimization. Exploring the theoretical and architectural bedrock of generative AI.

• Deep Learning: Architectures and Advances
• Foundations of Generative AI
• Data, Models, and Optimization
• Computational Neuroscience and Cognitive Models
• Large Language Models and Foundation Models
• GANs, Diffusion Models, and Variants
• Multimodal AI and Cross-Modal Learning
• Continual, Few-Shot, and Zero-Shot Learning
• Knowledge Graphs and Neuro-symbolic Reasoning