Interpretable Spatio-Temporal Point Processes
Event models for complex temporal and spatial dynamics, with interpretability kept in view.
Summary
I work on point process models for event data where the goal is not only forecasting, but also understanding dynamics, stress-testing models, and supporting downstream scientific reasoning.
Research Question
What kinds of point process models remain interpretable while still being expressive enough for realistic spatio-temporal event data?
Methodology
- Neural and physics-informed point process models
- Benchmark design for spatio-temporal pattern complexity
- Counterfactual analysis for event-generating processes
Key Contributions
- Surveys and organizes the design space of neural spatio-temporal point processes
- Builds evaluation frameworks for comparing event-model complexity
- Studies counterfactual behavior and physically structured event models
Outputs
- Publication: (Mukherjee et al., 2025)
- Publication: (Aalaila et al., 2025)
- Publication: (Mukherjee et al., 2025)
- Publication: (Großmann et al., 2024)