About me

I’m a paleoceanography Ph.D. candidate in the School of Earth and Atmospheric Sciences at Georgia Tech. My research uses planktonic foraminiferal geochemistry to reconstruct ocean density and surface winds on glacial timescales. Understanding how the ocean-atmosphere system has changed in the geologic past is vital for projecting how it will respond to anthropogenic forcing.

I tackle this problem with statistical modeling, data science techniques, and machine learning algorithms. I both compile isotope data from existing sources and generate new data from coretop and Last Glacial Maximum sediments.

In my free time, I volunteer with Community Consulting Teams (CCT) Atlanta, a nonprofit focused on providing free consulting to local nonprofits. I have completed two projects with CCT Atlanta, working with Keep North Fulton Beautiful and Community Farmer’s Market.

Projects (past and present)

Constraining Foraminifera Calcification Depths

Using foraminferal oxygen isotope data from the global tropics, I (with the help of my co-authors) investigated the calcification habitat for planktonic foraminifera. These freely floating microorganisms are very useful for paleooceanographic study, as long as we understand the depths at which they live and calcify. This work builds on previous global studies of surface-dwelling foraminifera (MARGO project and Malevich et al. 2019) and extends it to subsurface-dwelling foraminifera (G. tumida, N. dutertrei, and P. obliquiloculata). We present this new dataset and attempt to characterize trends in the calcification depth under various assumptions. Using a novel "TP" metric, we show that G. tumida has a calcification habitat independent of the thermocline depth, while N. dutertrei and P. obliquiloculata have calcification habitats that depend on the thermocline depth. This visualization of the data summarizes the main findings.

Thermocline Regression

With the species depth data, we can attempt to determine the depth of the thermocline with just these foraminiferal data. I (with the help of my co-authors) do this by assuming a form of the thermocline based on oceanographic principles and fitting parameters to the oxygen isotope data. This is done with enough flexibility to accommodate diffences in the thermocline profile regionally in the Pacific Ocean (allowing the surface mixed layer to be shallow or deep, etc.). With this method, I analyze previously published data as well as generate new data for the Western, Central and Eastern Pacific to investigate thermocline changes during the Last Glacial Maximum. I am currently validating this method under different oceanographic conditions and with different combinations of species.

Statistical model of LGM mean state

With the species depth data, there are other methods to estimate the depth of the thermocline more holistically. With this approach, we attempt to characterize the entire tropical Pacific density structure using dimensionality reduction. Using empirical orthogonal functions, I (with the help of Emanuele di Lorenzo and co-authors) model the Pacific density structure and wind structure and how these foraminferal oxygen isotope data can be sufficient to reconstruct the climate state in the Last Glacial Maximum. I am currently estimating the size of the errors from these steps and investigating other linear and non-linear dimensionality reduction techniques for this work.