Research Overview

My work develops new tools to turn geophysical observations into model-ready constraints, advancing predictions of ice sheet behavior and reducing uncertainty in poorly understood processes. This often involves borrowing and adapting methods from other fields to answer glaciological questions. For example, I applied a spectral analysis technique from seismology to harness the full radar frequency spectrum for estimating ice temperature, marking its first application in radar sounding.

Bridging communities and ideas across disciplines is central to my work. I am currently leading a synthesis that brings together Earth and planetary perspectives on radar attenuation into a shared physical framework. Although these fields face similar technical challenges, the communities rarely interact. This work draws on both glaciological and planetary radar expertise to identify common needs and opportunities across icy worlds.

I take a systems-level view of ice sheet change, linking surface, basal, and margin processes with external climate drivers. Collaborations with planetary radar scientists and oceanographers connect radar signal behavior and ice–ocean interactions, while my atmospheric science background informs my climate perspective from Arctic energy budgets to Antarctic ice sheet mass loss.

I use geophysical observations, mainly radar sounding, to reveal the physical state of ice sheets from surface to bed. Radar data can capture variations in ice properties and basal thermal and hydrologic conditions, yet quantitative analyses remain uncommon. My work advances these capabilities by applying statistical and machine learning approaches to extract robust physical insight from radar echoes.

For example, I developed a statistical framework to classify frozen and thawed bed conditions beneath the outflow of the Wilkes Subglacial Basin in East Antarctica, along with confidence estimates for each classification. This analysis not only revealed variable basal conditions in a region critical to East Antarctica's stability but also marked the first radar-based assessment of subglacial thermal state in this region. These findings were covered in Stanford Engineering News. By turning radar observations into physically interpretable classifications, this work opens the door to using thermal state maps as direct constraints in ice sheet models.

I specialize in integrating geophysical observations into ice sheet models, using methods such as inverse modeling, data assimilation, and targeted sensitivity analysis to translate measurements into physically meaningful model constraints. This two-way connection is essential: observations are only as powerful as the insight they provide to models, and models are what allow us to project future mass loss, instability, and contributions to sea-level rise. For instance, the basal thermal state has a large influence on ice flow, yet leading ice sheet models give entirely different maps of basal temperature. By coupling radar and other field data with process-based simulations, my work anchors model behavior in observations while also guiding new data collection to the locations that will most improve predictions.

My research in Nature Communications is an example of how targeted modeling experiments can identify which observations will be most valuable for ice flow projections. By testing the sensitivity of Antarctic mass loss to basal thermal state in the Ice-Sheet and Sea-Level System Model (ISSM), I found that in regions where the bed is frozen but close to melting, even a small temperature increase can drastically reduce basal friction, accelerating ice discharge. This study, featured in Stanford Sustainability News, shows how better constraints on basal thermal conditions are key to improving future sea-level rise projections.