Mayar Ariss

A new study led by Mayar Ariss, research fellow at the MIT Senseable City Lab, introduces an AI-based method to assess how buildings might respond to earthquakes. The approach extracts visual and geometric features from street-level imagery and translates them into simplified structural models. These models are then used to simulate seismic performance, offering a scalable alternative to conventional, resource-intensive surveys. “Extensive validation against experimental data shows that the predictions closely match observed outcomes,” says Ariss. The work highlights how widely available imagery, combined with artificial intelligence, can support rapid and low-cost seismic risk assessment in cities around the world.

Can city buses and trams serve as moving environmental watchdogs, tracking pollution as they travel through urban streets? A study led by Mayar Ariss at the MIT Senseable City Lab explores this question through a case study in Amsterdam. The project equips transit vehicles with pollution sensors, creating a low-cost and wide-reaching monitoring network. “Although we take Amsterdam as a case study, the power of this research lies in its applicability to other cities,” says Ariss. The findings point to new ways of integrating mobility systems with environmental sensing to better understand and manage urban air quality.

In the final issue of Felix this academic year, attention is given to the GAMMA relief project, an initiative founded by Mayar Ariss at Imperial College London, in collaboration with MIT. Ariss launched the project in 2023 in response to the devastating earthquakes in Morocco, Syria, and Turkey, which caused tens of thousands of deaths and left many without homes. The project explores how new technologies and data-driven methods can support communities in the aftermath of large-scale seismic events. By combining engineering research with humanitarian aims, Ariss’ work highlights the role of students and researchers in developing practical tools for disaster relief.

Motorized transport accounts for around 9% of Amsterdam’s emissions, producing 360 kilotons of CO₂ annually. Yet emissions fluctuate significantly across time and space, and most cities lack tools to capture data at this level of detail. A study led by Mayar Ariss at the MIT Senseable City Lab shows that by equipping just a fraction of Amsterdam’s buses and trams with environmental sensors, the city could build the world’s first real-time emissions monitoring system. This “drive-by sensing” approach builds on earlier projects: Urban Sensing, which showed that 30 taxis could cover half the streets in Manhattan in a single day, and City Veins, which demonstrated how sensing potential depends on street networks, fleet size, and mobility patterns. In Clocking Emissions, Ariss and colleagues propose a model that determines the minimum number of vehicles required to track air pollution, noise, and temperature. The framework leverages existing transit fleets and could be adapted to cities worldwide.