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Critical Minerals

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Our program focuses on technological innovation needed to create a resilient mineral supply chain to achieve clean renewable energy. Our program will also develop new pathways in the Mineral-Energy nexus, such as geothermal energy and renewable energy resources that enable a decarbonized mineral supply chain. Our work focuses on decision making under uncertainty at all levels of the Mineral-Energy nexus.


Critical Minerals Faculty

Jef Caers

Professor of Earth and Planetary Sciences and, by courtesy, of Geophysics

Classes offered in Critical Minerals:

EARTHSYS 100A: Introduction to Data Science for Geoscience (EPS 6) (Formerly GEOLSCI 6) This course provides an overview of the most relevant areas of data science to address geoscientific challenges and questions as they pertain to the environment, earth resources & hazards. The focus lies on the methods that treat common characters of geoscientific data: multivariate, multi-scale, compositional, geospatial and space-time. In addition, the course will treat those statistical method that allow a quantification of the human dimension by looking at quantifying impact on humans (e.g. hazards, contamination) and how humans impact the environment (e.g. contamination, land use). The course focuses on developing skills that are not covered in traditional statistics and machine learning courses. Change of Department Name: Earth and Planetary Science (Formerly Geologic Sciences).

Terms: Win | Units: 3 | UG Reqs: WAY-AQR | Repeatable 3 times (up to 9 units total)
Instructors: Caers, J. (PI) ; Mantilla Salas, S. (TA) ; Sharma, R. (TA)

EPS 140: Data Science for Geoscience (EARTHSYS 140, EARTHSYS 240, ENERGY 240, EPS 240, ESS 239) (Formerly GEOLSCI 140 and 240) Overview of some of the most important data science methods (statistics, machine learning & computer vision) relevant for geological sciences, as well as other fields in the Earth Sciences. Areas covered are: extreme value statistics for predicting rare events; compositional data analysis for geochemistry; multivariate analysis for designing data & computer experiments; probabilistic aggregation of evidence for spatial mapping; functional data analysis for multivariate environmental datasets, spatial regression and modeling spatial uncertainty with covariate information (geostatistics). Identification & learning of geo-objects with computer vision. Focus on practicality rather than theory. Matlab exercises on realistic data problems. Change of Department Name: Earth and Planetary Science (Formerly Geologic Sciences).

Terms: Win | Units: 3
Instructors: Caers, J. (PI)
Schedule for EPS 140