Call for Participation

AIMLSystems is a new conference targeting research at the intersection of AI/ML techniques and systems engineering. Through this conference we plan to bring out and highlight the natural connections between these two fields and their application to socio-economic systems. Specifically we explore how immense strides in AI/ML techniques are made possible through computational systems research (e.g., improvements in CPU/GPU architectures, data-intensive infrastructure, and communications ), how the use of AI/ML can help in the continuous and workload-driven design space exploration of computational systems (e.g., self-tuning databases, learning compiler optimisers, and learnable network systems ), and the use of AI/ML in the design of socio-economic systems such as public healthcare, and security. The goal is to bring together these diverse communities and elicit connections between them.

Topics of Interest

The areas of interest are broadly categorized into the following three streams:

  1. Systems for AI/ML, including but not limited to:
    • 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”
    • Efficient model training, optimization and inference
    • Hardware efficient ML methods
    • Resource-constrained ML
    • Tiny Machine Learning
    • Embedded and Edge Artificial Intelligence
    • Distributed and parallel learning algorithms
    • MLOps (data collection, monitoring and re-training)
  2. AI/ML for Systems, including but not limited to:
    • 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
  3. AI/ML for Socio-Economic Systems Design, which includes, but not limited to:
    • Computational design and analysis of socio-economic systems
    • Fair and bias-free systems for social welfare, business platforms
    • Applications of AI/ML in the design, short-/long-term analysis of cyber-physical systems
    • Mechanism design for socio-economic systems
    • Fairness, interpretability and explainability for ML applications
    • Privacy and security in AI/ML systems
    • Sustainability in AI/ML systems
    • Ethics in AI/ML systems
    • Applications of AI/ML in financial systems

Conference Highlights

  • Keynote/Invited Talks
  • Technical Paper Sessions
  • Industry Track Session
  • Panel Discussions
  • Demos & Exhibits
  • Doctoral Symposium
  • Tutorials

Keynote Speakers

Carlos Guestrin

Stanford University, USA

Cynthia Rudin

Duke University, USA

Max Welling

University of Amsterdam & Microsoft Research, Netherlands

Nikko Strom

Amazon, USA

Partha Pratim Talukdar

IISc Bangalore & Google Research, India

Sunita Sarawagi

IIT Bombay, India

Thorsten Joachims

Cornell University, USA