CONSTRAINT-2023 welcomes theoretical and practical paper submissions on various scopes to contribute in developing ML systems under a variety of resource constraints and to address resource constraints. We will particularly encourage studies that address either practical applications or improve upon resource constraints for a variety of ML systems in the field. We invite submissions on topics that include, but are not limited to, the following:


Creating new resources such as data, hardware, and protocols for ML systems, Algorithms and Applications
Optimizing data science systems, embedded platforms, and test beds for ML systems, Algorithms and Applications
Data privacy, hardware privacy, and new ML system and Algorithms design
Algorithms for Urban computing and ML analytics under resource constraints.
Energy-efficient computing and inferencing for ML systems, Algorithms and Applications
Optimized VLSI and architecture design for ML and data science applications
Algorithms and systems for increasing database efficiency using Machine Learning
Optimizing Machine Learning Algorithms for environmental sustainability and Green Machine Learning.
Cheaper surrogate ML systems, Algorithms and Applications
Tools and methods for “green Machine Learning systems” hardware-software system design and evaluation
Frameworks and methods to improve equity of Machine Learning systems especially under constraint of data and infrastructure.
Machine Learning Algorithms catering to applications in resource constraint third world applications.
Empirical study of resource constraints in areas of healthcare, supply chain, enterprise mobility solution, mobile systems, edge computing, education, smart campus, smart city and buildings, manufacturing, energy, demand forecasting, finance, retail, social computation, crowd sensing, wireless communication and networking, smart mobility, cyber security, environmental policy, climate change, and control, internet of personalized things, etc.
Applications of AI and ML under resource constraints.