I study the long-term evolution of solar magnetism across multiple cycles, focusing on cycle-to-cycle variability, global field diagnostics, and historical reconstructions that connect observations and modeling for space-weather relevant insights.
Solar Cycle Periodicity
This includes historical reconstruction, polar-field diagnostics, and links between surface magnetism and predictive space-weather indicators.
Typical questions
- How do cycles differ in amplitude and timing?
- What do polar fields tell us about the next cycle?
- How far back can reconstructions be trusted?
Related sections
SFT / AFT Modeling
I use surface flux transport frameworks—including AFT in data-assimilation mode—to evolve photospheric magnetic fields under realistic surface flows. This supports consistent full-Sun maps, validation against synoptic products, and sensitivity studies.
Workflow focus
- Assimilation-ready active region inputs
- Validation against synoptic and full-Sun products
- Sensitivity testing under realistic flow choices
Why it matters
These models connect observations and prediction by turning surface magnetism into interpretable evolution scenarios rather than static snapshots.
Magnetic Reconnection
I investigate magnetic reconnection as a key mechanism for rapid energy release in the solar atmosphere, combining observational signatures and magnetic context to interpret transient events and connectivity changes.
Interpretive lens
- Transient events in magnetic context
- Connectivity changes across complex regions
- Links between topology and energy release
Current role in site
This topic is part of the research map now, with room for a later standalone project page.
Spectro-Polarimetry
I work with polarized spectral diagnostics to infer magnetic and thermodynamic properties of solar plasma, emphasizing robust inversions, careful uncertainty handling, and consistent interpretation across instruments and observing conditions.
Method priorities
- Careful inversions and uncertainty handling
- Cross-instrument interpretability
- Magnetic diagnostics tied back to physical context
How it fits
It complements the larger magnetic-field story by connecting detailed diagnostics to global evolution.
Machine Learning
I develop ML-ready datasets and models for tasks like geometry-aware cross-calibration, systematic error characterization, and feature detection/tracking—aiming for reproducible evaluation and physically meaningful outputs.
Applied directions
- Cross-calibration for heterogeneous observations
- Feature detection and tracking
- Forecast-friendly data products
Design principle
The goal is not ML for its own sake, but ML that stays physically interpretable and reusable.
Tool Development
I build reusable tools for data retrieval, archiving, conversion, and analysis—designed for large solar datasets. This includes automated updates, metadata-preserving storage, and visualization/diagnostic utilities that support end-to-end pipelines.
Tooling emphasis
- Automated updates and archiving
- Metadata-preserving conversion
- Visualization and diagnostic utilities
Next layer
This section is ready for future software and project pages without introducing broken links today.