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