James Doss-Gollin

Independent research and investigation, including a course toward directed research and/or a research project. Study of selected topics including individual investigations special lectures, and seminars. Student works independently with only minimal faculty direction. Offered upon mutual agreement of faculty and student. May earn varying amount of credit hours depending on the amount of time devoted and the amount of academic work associated with the course. Repeatable for Credit.

This course explores the integration of physics-based and statistical (and machine learning) methods used to assess and model hydroclimate extremes and catastrophes, such as floods, droughts, and extreme rainfall. A central theme is quantifying uncertainty in extreme event probabilities, given the challenges posed by sparse observations and model biases. Through hands-on programming assignments, students will gain experience applying these methods to real-world problems. Topics include extreme value theory, optimal sampling, synthetic weather generation, downscaling and bias correction, surrogate models, and data assimilation. Recommended Prerequisite(s): A background in Bayesian statistics (e.g., 525) and/or machine learning (e.g., ELEC 478/578) is strongly recommended, along with familiarity with programming (e.g., Python, Julia, or R).

Professional master Project course involves the following (1) a project of practical relevance to the practice of Civil and Environmental Engineering, and (2) detailed project report. Students need to work with a faculty advisor. Repeatable for Credit.

Repeatable for Credit.