Grethe Hystad

Grethe Hystad serves as Associate Professor of Statistics at Purdue University Northwest, where she teaches diverse statistics and mathematics courses while mentoring student research. Her active engagement spans classroom instruction, real-world data application, and cutting-edge mineral evolution research.

Education:

• Ph.D. in Mathematics, University of Arizona
• M.S. in Mathematics, University of Arizona
• Candidata Magisterii (B.S. equivalent) in Mathematics, University of Oslo

Her research specializes in statistical analysis of mineral evolution and ecology, focusing on deep-time geosphere-biosphere co-evolution. Using Bayesian methods and mineral abundance modeling, she develops frameworks for characterizing planetary mineralogy across evolutionary timescales. This interdisciplinary work bridges geoscience, data science, and statistical theory to address fundamental questions about Earth's mineralogical history and exoplanet characterization.

Her publication trend (2017-2019) reveals consistent innovation in applying statistical methodologies to mineral diversity quantification, with increasing emphasis on predictive modeling for undiscovered mineral species and Earth-like planet classification.

Scientific Awards:

• 2021 Felix Chayes Prize for Excellence in Research in Mathematical Petrology (International Association for Mathematical Geosciences)

Hystad actively secures external funding to support student research, including grants from the John Templeton Foundation and Robert and Margaret Hazen Foundation. Her mentoring philosophy emphasizes student autonomy within structured guidance, resulting in undergraduate researchers presenting at Geological Society of America conferences and PNW's Days of Discovery. She integrates research into teaching through industry-standard tools like R and Jupyter Notebook, connecting classroom theory to real-world applications.

As part of collaborative research teams investigating mineral evolution, she directs student projects analyzing deep-time geological datasets while developing statistical frameworks for planetary mineralogy characterization.