21 citations · 24 across the 11 of their papers we have counts for
14 papers
IBAD: Interpretable Behavioral Anomaly Detection on Human Mobility Data
Bita Azarijoo, John Krumm, Cyrus Shahabi
Human mobility appears highly diverse, yet much of a person's daily mobility can be explained by a small set of recurring behavioral templates, such as commuting, school-centered a…
TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
Maria Despoina Siampou, Gengchen Mai, Ni Lao +4
Multimodal self-supervised learning (MSSL) has emerged as a key paradigm for pretraining geospatial foundation models. However, existing geospatial MSSL methods are mainly designed…
TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond
Shang-Ling Hsu, Mark Tenzer, Cyrus Shahabi +1
Human mobility differs from text and from generic time series in three structural ways: visits are tuple-valued events whose meaning depends on the joint distribution over location…
Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement
Maria Despoina Siampou, Shushman Choudhury, Shang-Ling Hsu +2
Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POI…
WaveGNN: Integrating Graph Neural Networks and Transformers for Decay-Aware Classification of Irregular Clinical Time-Series
Arash Hajisafi, Maria Despoina Siampou, Bita Azarijoo +2
Clinical time series are often irregularly sampled, with varying sensor frequencies, missing observations, and misaligned timestamps. Prior approaches typically address these irreg…
Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors
Ziyao Li, Shang-Ling Hsu, Cyrus Shahabi
Understanding human mobility behavior is crucial for numerous applications, including crowd management, location-based recommendations, and the estimation of pandemic spread. Machi…