Claire
Gorman
Hanly



Claire Gorman Hanly is an American computer scientist and environmental designer. Her research and practice focus on the implementation of deep learning-based computer vision methods in built and natural environments, with applications in regenerative agriculture, remote sensing of hydrology, and cultural landscape preservation. 

Claire’s work ranges geographically across glaciers and caves, river deltas and grain supply-sheds, Arctic wilderness and subtropical cities. It ranges methodologically across scientific research, creative curation, and speculative tool-building. She has collaborated with technology companies, research labs, and the US National Park Service as well as her most recent role in co-curating the 19th International Architecture Exhibition at La Biennale di Venezia.

Claire is currently pursuing two Master’s degrees at MIT: an MCP in Environmental Policy and Planning, and an MS in Computer Science. Her Bachelor’s degree is in Computer Science and Architecture, from Yale University.


clairego@mit.edu
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Bog Studies


Dual Environmental Planning and Computer Science thesis project investigating the remote sensing opportunities and cost-benefit factors influencing adoption of emerging UK peatland conservation policy in Northern Ireland. Combines deep learning experiments on bog feature detection with policy analysis of private carbon markets as incentives for regenerative land management.

Field work in Northern Ireland supported by Morningside Academy for Design and MIT Loyd and Nadine Rodwin Grant.

Skills:
Deep learning
Geospatial data processing
Policy analysis
Archival research
Stakeholder engagement September 2025 - May 2026


“ The Stick ” 

Prototype sensor development of a modular, lightweight accessory that can transform any “stick” or branch into a landscape sensor using gesture detection. Device captures GPS coordinates, elevation, and timestamp each time the walking stick is tapped on the ground; captures images on double-tap; and detects soil moisture using capacitive sensing when rotated.

Documentation for course 6.9020 How To Make Almost Anything taught by Prof. Neil Gershenfeld (MIT Media Lab).

Skills:
Electronics assembly
3D printing
Digital design and fabrication
Embedded programming
Technical versioning and documentation Fall 2025


Biennale Architettura 2025


Assistant Curator to Dr. Carlo Ratti for the 2025 Venice Architecture Biennale, leading the integration of 300 participations in the world’s largest architecture exhibition in Venice, Italy. Led theme and concept development, oversight of all communications with participants, management of curatorial process from selection to construction, and followthrough of research and public events post-opening. Projects represented over 1M EUR in public funding and over 10M EUR in private fundraising.

curatorial statement

images: Catalogues and entrance line for Biennale Architettura (via Bänziger Hug), Presenting projects to Curator Carlo Ratti and journalists, Floor plan of Corderie + Artiglierie in La Biennale offices.


Skills:
Project Management
Press and Professional Communication
Curation
Exhibition Design
Research January 2024 - November 2025


MAD Fellowship


Elaborating research on peatland ecologies, carbon economy, and the integration of traditional and sensing-based land management paradigms into a curated exhibition through MIT’s Morningside Academy for Design. Research and exhibition will trace the outlines of the “computational landscape” as represented in historical, scientific, and cartographic image making.

fellow profile

image: Reading room at the National Library of Ireland, during a visit to review 19th-century original Bog Commission documents.


Skills:
Machine Learning / Computer Vision
Planning research
Historical research
Drawing
Curation
Exhibition Design
Fall 2025


CIBO Technologies


Generalist data science contributions to the Computer Vision team at venture-backed regenerative agriculture startup CIBO Technologies. Projects included scaling nationwide road data acquisition and rasterization for integration in a cropland prediction workflow, training a multi-billion-parameter crop detection model using augmented geospatial data, and scoping a research project to build a generative model to synthesize missing Sentinel vegetation index images.

images: screen captures of the USDA’s Crop Data Layer, agricultural land use raster data for the entire United States. This data is used as training labels for deep learning to predict crop type.

Skills:
Deep Learning 
Computer Vision
Data Science
2022 - 2024

Andrea Chegut Fellowship


Inaugural awardee of MIT’s Andrea Chegut Fellowship, a grant that funds independent research and prototyping projects for women in the School of Architecture and Planning. Project focused on publishing essays and experimental results that illuminate use cases of Large Language Models for environmental planning.

essays in the MIT Public Interest Technologist

image: combination of figures from a scientific paper on millipedes that was found to have been generated with AI in 2023.

Skills:
Writing (public audience)
Experiment design
Research Fall 2023


Art & Agriculture


Art research project focused on the traditions and technologies of agriculture in Palestine. Reimagining the ancient “zeer pot,” which uses a double-layered unglazed wall structure, packed with wet sand between the layers, to create a temperature differential for refrigeration. 

Here implemented for seed starting to conserve water: chia seed polyculture attaches seeds of typical Palestinian crops (molokhia/spinach and kusa/squash) to the water source.

Skills:
Ceramic fabrication
Research
[not actually a green thumb]

For MIT 4.s34 Art, Technology & Agriculture taught by Prof. Nida Sinnokrot Spring 2023


Detecting Arctic Wetlands


Testing alternate pre-trained “backbone” models for image segmentation to determine if performance on an Arctic wetland mapping task is responsive to backbone network architecture. Based on original ArcticNet paper by Jiang et al. (2019).

Skills:
Deep Learning
Computer Vision
Scientific Writing

For MIT 6.8300 Advances in Computer Vision taught by Prof. Bill Freeman Spring 2023


Soil Carbon and the Future of Computing


Essay awarded an Honorable Mention in the inaugural Future of Computing Essay Contest by MIT’s Schwartzman College of Computing. Advocates a greener future for the field of AI, in which Large Language Models are intertwined with carbon models and Earth observation data to foster ease and inclusion in sustainable land management.

essay link

image: Google Satellite image of agricultural land in California

Skills:
Writing (public audience)
Research Winter 2023


Deep Learning on Ice


Implementation of an Attention U-Net for segmentation of glaciers in the Hindu-Kush region of the Himalayas, framed by discussion of image-based methods for documenting glacial extent over time. Based on original glacial segmentation paper by Baraka et al. (2020) and proposal of Attention U-Net by Oktay et al. (2018).

project link

image: Glaciated area of the Hindu Kush Himalayas in the disputed Jammu and Kashmir area of India and Pakistan, with clean-ice glaciers (training labels, from ICIMOD) shown in blue over Google Satellite data (2021).

Skills:
Deep Learning
Computer Vision
Remote Sensing

For MIT 6.S898 Deep Learning taught by Profs. Philip Isola and Stephanie Jegelka

 2020-2022