Mayar Ariss is a Research Fellow at the Senseable City
Lab, where he leads interdisciplinary research on
how complex systems can remain stable when resources are
scarce and risks are high.
He received an MEng degree in Civil and Environmental Engineering from
Imperial College London in 2024.
Publications
Ergodicity-Informed Adaptive Sensing for Energy-Constrained Urban IoT Networks
IEEE Internet of Things. 2025. DOI: 10.1109/JIOT.2025.3621047.Seismic assessment of unreinforced masonry façades from images using macroelement-based modeling
Communications Engineering. 2025. DOI: 10.1038/s44172-025-00487-2.Drive-by environmental sensing strategy to reach optimal and continuous spatio-temporal coverage using local transit network
Transportation Research Record. 2024. DOI: 10.1177/0361198124124705.
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Featured
Generative AI for Smarter Wildfire Water Response.
As wildfires intensify, urban water systems must adapt in real time to rapidly changing conditions.
Mayar Ariss has received the MIT Generative AI Impact Consortium (MGAIC) Award, for a
project sponsored by OpenAI that applies generative AI to strengthen urban water systems during wildfire emergencies.
The research integrates generative AI with real-time wildfire prediction data to dynamically adjust pump and valve operations,
such that the system prioritizes firefighting demand while protecting critical infrastructure.
Designed as a scalable decision-support tool, the platform aims to equip municipalities and emergency teams with
actionable insights to strengthen preparedness in the face of escalating wildfire risk.
Earthquakes can destroy buildings. AI may help predict which ones.
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.
Driving cleaner cities: Using public transport to measure pollution in real-time
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.
Imperial student develops flat-pack homes for earthquake-stricken regions
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.
Clocking Emissions
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.
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