Uncertainty in climate model projections, sea level rise in particular, can lead to suboptimal, ineffective, and - at worst - outright dangerous policy decisions. To avoid this, we must use the information we have available make the best possible policy decisions. This requires accounting for not only varying forms of uncertainty in model parameters and projections, but deep uncertainty - uncertainty in the uncertainty in model structure and parameters. Statistical calibration approaches allow us to constrain these models and characterize the uncertainties inherent in both the model and data, and are a critical part of any modeling effort. In particular, I am interested in future projections of sea-level rise and storm surges, and their impacts on coastal defense decision-making.
Bayesian statistics, storm surge, sea-level rise, coastal adaptation, model calibration and simulation, deep uncertainty, climate risk management
APPM 2350 - Calculus 3 for Engineers
Covers multivariable calculus, vector analysis, and theorems of Gauss, Green, and Stokes. Degree credit not granted for this course and MATH 2400.
CSCI 1300 - Computer Science 1: Starting Computing
Teaches techniques for writing computer programs in higher level programming languages to solve problems of interest in a range of application domains. Appropriate for students with little to no experience in computing or programming. Degree credit not granted for this course and CSCI 1310 or CSCI 1320 or ECEN 1310. Same as CSPB 1300.
CSCI 2824 - Discrete Structures
Spring 2018 / Fall 2018
Covers foundational materials for computer science that is often assumed in advanced courses. Topics include set theory, Boolean algebra, functions and relations, graphs, propositional and predicate calculus, proofs, mathematical induction, recurrence relations, combinatorics, discrete probability. Focuses on examples based on diverse applications of computer science. Same as CSPB 2824.
CSCI 2830 - Special Topics in Computer Science
Covers topics of interest in computer science at the sophomore level. Content varies from semester to semester. Does not count as Computer Science credit for Computer Science majors or minors. May be repeated up to 9 total credit hours.
CSCI 3022 - Introduction to Data Science with Probability and Statistics
Summer 2018 / Fall 2018 / Spring 2019
Introduces students to the tools methods and theory behind extracting insights from data. Covers algorithms of cleaning and munging data, probability theory and common distributions, statistical simulation, drawing inferences from data, and basic statistical modeling. Same as CSPB 3022.