Teaching & education
Undergraduate and graduate courses in astronomy and statistics, along with workshops and short courses on Bayesian inference and scientific computing.
Students should gain a deep understanding of the underlying themes and concepts of the course. This enables students to build intuition as well as transfer skills and knowledge gained from the course to other areas throughout their careers.
The most effective ways to learn always involve “hands-on” experience. Students should have numerous opportunities to directly apply the knowledge and skills they are learning to relevant problems, discuss these experiences with their peers, and pursue long-term projects with substantial autonomy over their focus and execution.
Students should have the ability to access, pursue, and learn from these opportunities regardless of their background. Courses should be explicitly designed to reward diverse backgrounds so students feel their unique identities strengthen their connection to the material and sense of belonging in the scientific community.
I regularly conduct workshops on Bayesian methods, Python for astronomy, and statistical computing for various audiences including graduate students, postdocs, and faculty.
No courses or workshops match your search or filters.