transkinesis
Research
Established research on spatio-temporal machine learning for mobility systems — forecasting, origin-destination modeling, and learning under structure — with emerging work on language models for research and decision workflows.
Themes
- 01
Spatio-temporal forecasting
Forecasting models that treat time, geography, and mobility demand as connected structure.
- 02
Origin-destination modeling
Methods for representing and predicting multi-modal movement patterns across urban systems.
- 03
Learning under structure
Lipschitz-aware learning, graph structure, and transport constraints for stable modeling.
- 04
Language models for research
Emerging work applying and evaluating language models for research workflows and structured decision support — the direction behind Local Research.
Publications
- 2026
Coming soon
Selected publications will be added here.