Data-Driven Urban Tech: How Machine Learning and Optimization Addresses Today’s Urban Challenges

Technological innovations are creating opportunities for cities to rethink the way in which they address pressing and complex challenges. These include: mitigating and adapting to climate change, which has prompted researchers to design innovative prevention tools and disaster relief measures with the goal of making cities more robust; responding to the algorithmic economy (e.g., ride-hailing, bike-sharing systems, delivery systems), which has revolutionized our interactions with each other in the city; innovations in real-time information to help users’ decision-making, including smart infrastructure that aims to create a more sustainable ecosystem (e.g., energy efficient buildings, robust communication networks, smart grids); and other developments across  mobility, healthcare and, in general, public and private services, including  the adoption of autonomous vehicles, computer-assisted healthcare, analytics for water management, robust supply chains, among others. These developments bring myriad new questions around security, privacy, efficiency, equity, and complexity, alongside old challenges in urban planning. This workshop will consider how machine learning and data-driven approaches can be used to improve quality of life in urban environments.

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