The areas of interest are broadly categorized into the following three streams: (including but not limited to)

Systems for AI/ML

CPU/GPU architectures for AI/ML
Specialized/Embedded hardware for AI/ML workloads
Data intensive systems for efficient and distributed training
Challenges in production deployment of ML systems
ML programming models, languages, and abstractions
ML compilers and runtime
Efficient systems for data preparation and processing
Systems for visualization of data, models, and predictions
Testing, debugging, and monitoring of ML applications
Cloud-computing for machine and deep learning
Machine and deep learning “as-a-service”
Tiny Machine Learning
Embedded and Edge Artificial Intelligence
Distributed and parallel learning algorithms
MLOps (data collection, monitoring and re-training)

AI/ML for Systems

AI/ML for VLSI and architecture design
AI/ML in compiler optimization
AI/ML in data management – including database optimizations, virtualization, etc.
AI/ML for networks – design of networks, load modeling, etc.
AI/ML for power management – green computing, power models, etc.
AI/ML for Cloud Computing
AI/ML for IOT networks

AI/ML for Socio-Economic Systems Design

Deep Learning Architecture and applications
Computer Vision and Image processing
Natural language processing and understanding
Speech signal processing and Socio-Economic Systems
Fairness and biases of models
Explainability, Transparency, and Interpretability of AI systems
Privacy, Security, and Ethics in AI
AI/ML for cyber-physical systems
Fairness, Interpretability and Explainability for ML applications
Privacy and Security in AI/ML systems
Sustainability in AI/ML systems
Applications of AI/ML in financial systems