Call for papers

Theory, algorithms, and systems for modern machine learning — from foundation models and agentic AI to graph learning and trustworthy deployment. This track welcomes both methodological advances and rigorous applied studies across engineering domains.

• Foundation models and LLM applications
• Agentic AI and autonomous workflows
• Graph neural networks and network data analytics
• Explainable and trustworthy AI
• Federated and edge machine learning
• Reinforcement learning and decision systems
• Generative AI for engineering design
• AI for scientific discovery
• ML systems, MLOps, and efficient inference
• Out-of-distribution generalization and robustness

Modeling, control, and analytics for the modern grid and electrified infrastructure. The track unifies classical power engineering with data-driven methods for resilient, decarbonized energy systems.

• Smart grid analytics and grid-edge intelligence
• Renewable integration and forecasting
• Grid resilience, fault and incident classification
• Microgrids and distributed energy resources
• Energy storage modeling and control
• Electric vehicle technologies and charging infrastructure
• Power electronics, converters, and inverters
• Photovoltaics, fuel cells, and energy harvesting
• High voltage, dielectrics, and electric machines
• Energy management and green computing

Architectures, signal processing, and protocols for the networks of the next decade — terrestrial and beyond. Contributions on the convergence of communication, sensing, and machine intelligence are particularly welcome.

• 5G-Advanced and 6G systems
• Integrated sensing and communication (ISAC)
• Semantic and AI-native communications
• IoT, IIoT, and wireless sensor networks
• Satellite and non-terrestrial networks
• MIMO, antenna technologies, and signal propagation
• Optical and fiber communication
• Network security and resilient protocols
• Advanced signal processing for transmission
• Radar and remote sensing applications

From transistors to qubits: devices, circuits, and architectures that power emerging computing paradigms, including hardware specialized for AI workloads.

• Hardware accelerators for AI and ML
• Neuromorphic and in-memory computing
• RISC-V and open hardware ecosystems
• System-on-chip, FPGA, and VLSI design
• Chiplets and advanced packaging
• Quantum computing, Josephson devices, and qubits
• Post-quantum and quantum-resistant cryptography
• Photonic and optical computing
• Embedded and real-time systems
• Nanotechnology and solid-state electronics

Security, privacy, and safety for software, hardware, and AI-enabled systems. The track spans offensive and defensive research, formal assurance, and the unique threat surface introduced by machine learning.

• AI-driven threat detection and response
• Adversarial machine learning and AI robustness
• Privacy-preserving computation and federated security
• Blockchain and distributed-ledger applications
• Cryptography and secure computing
• Digital forensics and cybercrime analysis
• Hardware security and side channels
• Safety and assurance of autonomous systems
• Critical infrastructure and OT/ICS security
• Identity, access management, and zero trust

Computational methods at the interface of engineering, the life sciences, and human systems — where interdisciplinary work has the most direct societal impact.

• AI in healthcare and clinical decision support
• Bioinformatics and computational biology
• Network biology and multi-omics integration
• Medical imaging and multidimensional signal processing
• Digital twins for health and infrastructure
• Brain–computer interfaces and neuroengineering
• Wearables, biosensors, and remote monitoring
• Computational social systems and network science
• Human-centered and accessible computing
• Ethics, fairness, and responsible AI deployment

A special industry-facing track on intelligent manufacturing and the digital transformation of small and mid-sized enterprises. Case studies, deployed systems, and academia–industry collaborations are welcome alongside traditional research papers.

• Smart manufacturing and Industry 4.0/5.0
• Predictive maintenance and prognostics
• Industrial automation, robotics, and control
• Computer vision for quality inspection
• Supply chain analytics and optimization
• Digital twins for production systems
• Agtech and precision agriculture
• AI adoption in small and mid-sized enterprises
• Workforce development and AI readiness
• Operations research and decision intelligence