Formation and thermal evolution
My early work modeled heat and mass transport in comet nuclei and other icy bodies: when volatiles move or survive, how interior structure changes, and what present-day activity may remember about earlier conditions.
Research
I tend to work the same way in very different fields.
Start with a physical model, ask what is actually observable, and let the data push back. That approach has followed me from comet interiors and asteroid dynamics to calibration of Earth-observing instruments.
Research framework linking formation, evolution and observation through physical and measurement models.
How I think about the problem
Models and inferences.
The left side is forward modeling: start from physical assumptions and calculate what should happen. The right side is inverse modeling, retrieval, or inference: start from measurements and work back toward the physical state that could have produced them.
The interesting part is the connection: when a model doesn't match the data, we need to understand why, but measurements alone are limited without the physics interpretation.
Planetary science
Small bodies carry a long memory.
Comets, Centaurs, trans-Neptunian objects, and asteroids preserve clues about formation, later processing, and movement through the Solar System. My planetary work has mostly asked how those pieces fit together.
Planetesimal formation schematic showing rocky and icy populations separated by the snow line.
Illustration of the Jupiter-Family comets Gateway with associated small bodies (aphelion-perihelion distribution).
Dynamical pathways
Later work coupled physical questions to orbital history, including the Centaur-to-Jupiter-family-comet transition and the Gateway region exterior to Jupiter.
Linked processes
Following the chain from orbit to interior.
A small body does not have separate dynamical and physical histories. Its orbit shapes the thermal environment, which influences the evolution of its interior, surface, and composition. Activity, impacts, and binary interactions can further modify the body and what we observe.
The figure provides a visual overview of these connections, bringing together examples of the physical processes and research questions I have investigated. Its purpose is to illustrate the broader relationship and context of these distinct, but scientifically related, modeling efforts, rather than present a single evolutionary sequence or the results of one integrated model.
Selected small-body modeling summary.
Earth observation
Calibration is not housekeeping. It is part of the measurement.
For a satellite record that runs for many years, the instrument changes, the orbit changes, and the geometry changes. If those effects are not properly monitored and understood, they can be mistaken for changes in the Earth scene and nature measurmenet itself.
True-color observation of Saharan dust over the Mediterranean Sea.
(Image courtesy Jeff Schmaltz, MODIS Rapid Response Team at NASA GSFC)
Day-night-band observation of aurora over Canada and Hudson Bay.
(Image courtesy Joshua Stevens, NASA Earth Observatory at NASA GSFC)
Reflective solar bands
I support calibration of the reflective solar bands on Terra and Aqua MODIS as a member of the MODIS Characterization Support Team. Recent work has focused on what happens as orbital drift changes the solar-diffuser viewing geometry late in the missions.
Day–night band
I also support calibration of the VIIRS day–night band on NOAA-20 and Suomi NPP. The DNB spans a very large radiometric range, so gain behavior and long-term calibration need to be tracked carefully.
Collaborative work includes NOAA-21 calibration/performance analysis, and I presented the DNB calibration update at the 2026 MODIS/VIIRS Calibration Workshop.
Scientific computing
The code is part of the scientific argument.
I use computation to make physical assumptions explicit, compare them with data, and repeat the analysis without relying on a one-off calculation.
Numerical modeling
Heat and mass transfer, orbital dynamics, ejecta evolution, and related physical systems.
Data analysis
Time series, trends, uncertainty checks, cross-comparisons, and diagnostics for large scientific datasets.
Research software
Python, C/C++, Fortran, shell scripting, Linux, and Git. I care about code that can be checked, reproduced, and maintained.