Smart cities are emerging as a priority for Cyber-Physical Systems (CPS) research and development across the world. Artificial Intelligence and Machine Learning algorithms have played a large part in automating and advancing city operations and aiding the development of CPS in cities. Increasingly, data-driven modeling and intelligent decision-making under uncertainty are forming the basis for advancing transportation, safety, connectivity, and health services. For example, advanced traffic solutions, improved public transportation systems, smart emergency response, energy modeling, and autonomous driving are some of the applications that have benefited from approaches to principled decision-making.

There are many challenges pertaining to decision-making for CPS in smart cities. With the advent of IoT, sensor data is being generated at a pace and volume that is difficult to process and make inferences from. Further, the needs of the cities dictate that much of the processing happens on the edge, making it imperative that fast and tractable approaches to decision-making are designed. At the same time, there is a growing need for automated applications to be fair, secure, and resilient. Participants in the workshop will exchange ideas on these and allied topics, including data science and open-source data sets for smart cities, decision making for smart cities, design of intelligent systems in smart cities, and challenges in deployment, equity and fairness in smart cities, and security and privacy in AI for cities. Authors can choose to include their papers in the ACM CPS Week proceedings or opt out. See the entire call for papers here.

Important Dates

March 10: Submission Deadline (Extended)
March 18: Author Notification
March 25: Camera-ready Submission Deadline
May 18: Workshop

Workshop Organizers

1. Ayan Mukhopadhyay, Vanderbilt University (Workshop Chair)
   
2. Aron Laszka, University of Houston (Workshop Chair)
   
3. Abhishek Dubey, Vanderbilt University
   
4. Ram Rajagopal, Stanford University
   
5. Danny Huang, New York University