PhD Geology, in progress
Graduate Student Research Assistant
Richard M. Palin, advisor (now at University of Oxford, UK;
Colorado School of Mines
Lunar mantle evolution and volcanism

MS Geology, high honors
Graduate Student Teaching Assistant

Richard F. Wendlandt, advisor (now Emeritus Faculty)
Colorado School of Mines, 2018
Crystal size distributions and crystallization kinetics of the Laki fissure eruptions, Iceland

AS Mathematics, highest honors
Northern Virginia Community College, 2012
Student Achievement Award in Geology
Two-time Student Achievement Award in Mathematics

BS Geology, highest honors, honors in Geology
George Mason University, 2010
Department of Earth Science Adjunct faculty 
Geology Club president
Phi Beta Kappa
Crème de la Crème Award in French

BA Psychology, minor in Women’s Studies
State University of New York at Albany, 1998
Teaching Facilitator in the WSS Teaching Collective
Albany State’s first LGBTQ+ Student Association representative and panel member
Co-manager of Albany State’s Food Cooperative
President of Albany State’s University Cinemas

Academic ContributioNs and Publications


Hernández-Uribe, D., Palin, R.M., Cone, K.A., Cao, W. 2020.
Petrological implications of seafloor hydrothermal alteration on subducted mid-ocean ridge basalt.  Journal of Petrology.

Cone, K.A., Palin, R.M., Singha, K. 2020.
Unsupervised Machine Learning with petrological database ApolloBasaltDB reveals complexity in lunar basalt major element oxide and mineral distribution patterns. Icarus.

Cone, K.A., Wendlandt, R.F., Pfaff, K., Orlandini, O. 2020.
Texture constraints on crystal size distributions: An application to the Laki fissure eruption. American Mineralogist.

Hernández-Uribe, D., Hernández-Montenegro, J., Cone, K.A., Palin, R.M. 2020. 
Oceanic slab-top melting during subduction: Implications for trace-element recycling and adakite petrogenesis. 

Abstracts, Presentations, Invited talks

Cone, K.A., Palin, R.M., Singha, K. Machine Learning Approaches in Lunar Mantle Heterogeneity Investigations. AGU Fall Meeting, San Francisco, USA, 2020.

Cone, K.A., Palin, R.M., Singha, K. Revealing the Hidden Structure of the Lunar Interior: Insights from Machine Learning. GSA Annual Meeting, Montréal, Canada, 2020. Invited speaker.

Cone, K.A., Palin, R.M., Singha, K. Lunar Mantle Heterogeneity and the Apollo Mare Basalts: Examples from ApolloBasaltDB. GSA Annual Meeting, Phoenix, USA, 2019. Presentation.

Cone, K.A., Wendlandt, R.F., Pfaff, K., Orlandini, O. Textural Constraints and Imaging Techniques: Bias in Extracting Crystal Size Distributions. GSA Annual Meeting, Phoenix, USA, 2019. Requested presentation.

Cone, K.A., Diecchio, R.J. Geological Development of North America. GSA Annual Meeting, Phoenix, USA, 2019.

Cone, K., Krekeler, M.P.S., Diecchio, R.J., Kearns, L.E. Investigation of phosphatic sediment diagenesis of the Reedsville Formation, W. Virginia. GSA Annual Meeting, Philadelphia, USA, 2006.


The initial reason for creating this database was centered around investigations of mineral modes and major element oxides of the Apollo lunar basalts for unraveling clues to the evolution of the lunar mantle. In the process of gathering this data, I realized that multiple lunar basalt characteristics were poorly documented or entirely ignored. Determined to make the database more than just a simple table of modes and geochemical percentages for stable phase assemblage modeling (particularly since this involved reading through 300+ journal articles and the like), I included textural aspects and ages. I then applied machine learning within a spatial context to interpret data structure (complex data patterns are not immediately observable when using more traditional bivariate analyses (e.g. Harker diagrams)). 


— High P-T experiments are scheduled for Fall 2020 using a piston-cylinder apparatus.  These experiments will examine lunar mantle phase stabilities and titanium/ilmenite behavior. Phase equilibrium modeling will establish theoretical, early stable-phase assemblage boundaries considering a range of bulk silicate moon compositions.

I use Matlab to explore numerical modeling of best-fit aspect ratios for anhedral plagioclase microlites. Aspect ratios are one part of determining crystal size distributions (CSDs) and are known to reflect growth rates and associated magmatic environments.

— The lunar nearside contains geochemical and mineralogical signatures reflecting different types of mare basalts, sourced from different upper mantle protoliths. I examine correlations among these signatures to interpret potential interior processes. To accomplish this, I use unsupervised machine learning (common in the social sciences) as an investigative tool for re-evaluating lunar mantle heterogeneity.

—  I maintain a database of lunar basalt characteristics (ApolloBasaltDB, freely available for download in the top menu) used for both machine learning and thermodynamic modelling. Version 2.0 is the current release. Version 3.0 is scheduled for completion by the end of Summer 2021 and will include trace element and remote sensing data.



Below: A patch model created in Matlab. Plagioclase swallowtail forms are common in the groundmass of basalts and are related to cooling rates. Random cuts through these forms (and others) are modeled in order to explore how crystal aspect ratios (essentially length-to-width dimensions) measured in polished sections affect the interpretation of crystal size distributions.