Greenery Scanner

Gaetano Saurio is a PhD candidate in Data Science at Sapienza University of Rome, and a former visiting researcher at MIT's Senseable City Lab. His research combines deep learning, computer vision and Earth observation to study plant health, urban vegetation and surface heat. He works with satellite, drone, street-level and thermal imagery, focusing on models that remain reliable across environments. At MIT, he contributed to research on urban tree health and to study trees and surface temperatures in Amsterdam, Boston, Dubai and Los Angeles. His current work examines how foundation models generalise to thermal mapping in cities unseen during training.

Parikshit is an applied artificial intelligence researcher specializing in intelligent transportation systems, smart urban infrastructure, and edge-based IoT environmental monitoring. He focuses on translating deep learning theory into practical solutions that enhance urban safety and efficiency. As a Senior Research Fellow at the Indian Institute of Science and a visiting researcher at MIT’s SENSEable City Lab, Parikshit develops advanced AI models for complex urban systems running on low-powered devices. His research tackles key challenges in urban sensing, traffic management, and edge computing under resource constraints. His interests include AI for Impact, Artificial Intelligence of Things, and World Models as future directions.

Andrei Speridião is a Brazilian designer and technologist. At the MIT Senseable City Lab, he researched and designed urban sensing devices that capture data to reveal hidden dynamics of natural and built environments. With experience spanning makerspaces, academia, and industry, he blends envisioning and making to invent new forms of connection across what’s around, between, and within ourselves.

Song Guo is a Postdoctoral Fellow at the MIT Senseable City Lab. Her research applies artificial intelligence to study the built environment, human responses to it, and the relationships between the two. Her methodological interests include computer vision, vision-language models, and graph-based machine learning. Song received her Bachelor’s, Master’s, and Ph.D. from Tsinghua University. At MIT, she develops interdisciplinary research at the intersection of artificial intelligence, urban studies, and human behavior.

Giancarlo Papitto is a Forestry and Environmental Consultant, Brigadier General (Reserve) of the Carabinieri and Full Member of the Italian Academy of Forestry Sciences. He works with leading research institutions, including CNR, INGV and MIT, to turn scientific advances into operational forest monitoring, often through EU-funded projects. Member of the US–Italy Joint Commission's Earth Science working group and National Focal Point for ICP Forests under the UNECE Convention, he has co-authored publications in Nature and Scientific Data. His expertise spans remote sensing, hyperspectral satellite data and GIS, applied to forest and peri-urban green monitoring.

Umberto Fugiglando is Head of Research Strategy & Partnerships at the Senseable City Lab at the Massachusetts Institute of Technology (MIT), an 80-person multidisciplinary research group exploring the intersection of cities, people, and technology. He forges the Lab’s research agenda by developing multinational strategic collaborations with industry, governments, and philanthropy, ensuring alignment between scientific interests and societal impact. He also leads multi-stakeholder research projects applying data science to urban challenges. Umberto serves as an External Expert for the European Commission, advising policymakers on the future of mobility, and is co-founder and board member of ReFuse, a social enterprise focused on waste recycling in Lebanon, Uganda, and Rwanda. His background is in applied mathematics and engineering, and he has studied in Italy, Sweden, Canada, and the United States.

Leonardo Iacussi is a Postdoctoral Research Fellow at Politecnico di Milano, where he earned his PhD in Mechanical Engineering, specialising in Measurement and Experimental Techniques. His research focuses on monitoring the built environment through intelligent, distributed sensor networks. During his industrial PhD collaboration with STMicroelectronics, he worked on embedded sensing and edge AI, deploying data-processing and machine-learning models directly on resource-constrained sensor nodes. As a visiting researcher at the MIT Senseable City Lab, he applied this expertise to urban sensing, contributing to the design and prototyping of Greenery Scanner, a vehicle-based platform for mapping urban greenery.