Noam J. Gal

Ph.D. candidate in geoinformatics at the Hebrew University of Jerusalem. I work on GeoAI, urban simulation, and data-driven planning tools.

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Posts

portfolio

Gaza Envelope Community Engagement Platform

Community engagement interface for memorial sites and tourist infrastructure planning.

Be’er Sheva Innovation District Planning Platform

Full-stack urban analytics platform built on MIT Cityscope for the Be’er Sheva Innovation District.

NYC Workforce Development Fiscal Planning

Fiscal planning and survey analysis for federal workforce spending compliance.

Optimal Transport Neural Network

Input Convex Neural Network for probability density mapping, contributing to NeurIPS 2023 research.

The Right to Shelter — Bedouin Communities Planning Support System

Web application to locate optimal bomb shelter locations in unrecognized Bedouin villages.

Urban Vitality Laboratory — Digital-Physical Fragmentation Research

ETL pipelines and machine learning for geospatial, physiological, and digital environment data.

publications

A generative flow model for conditional sampling via optimal transport

Published in arXiv preprint, 2023

Preprint on generative flow models for conditional sampling via optimal transport.

Recommended citation: Alfonso, J., Baptista, R., Bhakta, A., Gal, N. J., Hou, A., Lyubimova, V., Pocklington, D., Sajonz, J., Trigila, G., & Tsai, R. (2023). A generative flow model for conditional sampling via optimal transport.
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Mental health in a digitally fragmented world: Exploring negative and positive associations across spatial contexts and gender

Published in Social Science & Medicine, 2026

Published research on associations between digital-physical fragmentation and mental health across spatial contexts.

Recommended citation: Gal, N. J., Wang, A. L. M., Franco, A., Chaix, B., & Birenboim, A. (2026). Mental health in a digitally fragmented world: Exploring negative and positive associations across spatial contexts and gender. Social Science & Medicine, 401, 119337.
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