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Khurram Shafique

Khurram Shafique contributes to research discovery and scholarly infrastructure.

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Machine Learning Artificial Intelligence Neural and Evolutionary Computing

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preprint2026arXiv

NARA: Anchor-Conditioned Relation-Aware Contextualization of Heterogeneous Geoentities

Geospatial foundation models have primarily focused on raster data such as satellite imagery, where self-supervised learning has been widely studied. Vector geospatial data instead represent the world as discrete geoentities with explicit geometry, semantics, and structured spatial relations, including metric proximity and topological relationships. These relations jointly determine how entities interact within space, yet existing representation learning methods remain fragmented, often restricted to specific geometry types or partial spatial relations, limiting their ability to capture unified spatial context across heterogeneous geoentities. We propose NARA (Neural Anchor-conditioned Relation-Aware representation learning), a self-supervised framework for vector geoentities. NARA learns context-dependent representations by jointly modeling semantics, geometry, and spatial relations within a unified framework and captures relational spatial structure beyond proximity alone, enabling rich contextualized representations across heterogeneous geoentities of points, polylines, and polygons. Evaluation on building function classification, traffic speed prediction, and next point-of-interest recommendation shows consistent improvements over prior methods, highlighting the benefit of unified relational modeling for vector geospatial data.

preprint2026arXiv

TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond

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, time, and activity; users carry persistent signatures across trajectories; and visits are not independent across users, since co-location at shared places is a primary signal. Existing pre-training recipes for mobility import objectives from language modeling, treating trajectories as sentences and visits as tokens, an analogy that fails against each of the three properties above. These properties define a broader class, multi-entity spatiotemporal event streams (MESES), spanning enterprise authentication logs, electronic health records, and other event-stream domains where entities share infrastructure, schedules, or contexts. We make the properties precise as three axioms that any pre-training framework for MESES should satisfy, and introduce TraXion, whose objectives and architecture are jointly designed to meet them. A single TraXion checkpoint per dataset beats task-specific baselines on every task across six public mobility datasets covering anomaly detection, next-POI recommendation, next-visit prediction, and social-link prediction. The same recipe, applied unchanged to enterprise authentication logs and ICU mortality prediction, matches or exceeds prior work on both, showing that event streams from domains as different as mobility, security, and healthcare can be modeled under a single framework.

preprint2022arXiv

Learning Citywide Patterns of Life from Trajectory Monitoring

The recent proliferation of real-world human mobility datasets has catalyzed geospatial and transportation research in trajectory prediction, demand forecasting, travel time estimation, and anomaly detection. However, these datasets also enable, more broadly, a descriptive analysis of intricate systems of human mobility. We formally define patterns of life analysis as a natural, explainable extension of online unsupervised anomaly detection, where we not only monitor a data stream for anomalies but also explicitly extract normal patterns over time. To learn patterns of life, we adapt Grow When Required (GWR) episodic memory from research in computational biology and neurorobotics to a new domain of geospatial analysis. This biologically-inspired neural network, related to self-organizing maps (SOM), constructs a set of "memories" or prototype traffic patterns incrementally as it iterates over the GPS stream. It then compares each new observation to its prior experiences, inducing an online, unsupervised clustering and anomaly detection on the data. We mine patterns-of-interest from the Porto taxi dataset, including both major public holidays and newly-discovered transportation anomalies, such as festivals and concerts which, to our knowledge, have not been previously acknowledged or reported in prior work. We anticipate that the capability to incrementally learn normal and abnormal road transportation behavior will be useful in many domains, including smart cities, autonomous vehicles, and urban planning and management.

preprint2022arXiv

Meta-Learning over Time for Destination Prediction Tasks

A need to understand and predict vehicles' behavior underlies both public and private goals in the transportation domain, including urban planning and management, ride-sharing services, and intelligent transportation systems. Individuals' preferences and intended destinations vary throughout the day, week, and year: for example, bars are most popular in the evenings, and beaches are most popular in the summer. Despite this principle, we note that recent studies on a popular benchmark dataset from Porto, Portugal have found, at best, only marginal improvements in predictive performance from incorporating temporal information. We propose an approach based on hypernetworks, a variant of meta-learning ("learning to learn") in which a neural network learns to change its own weights in response to an input. In our case, the weights responsible for destination prediction vary with the metadata, in particular the time, of the input trajectory. The time-conditioned weights notably improve the model's error relative to ablation studies and comparable prior work, and we confirm our hypothesis that knowledge of time should improve prediction of a vehicle's intended destination.

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