An introduction to computation for contemporary science
From climate change to COVID, computation is an essential element of modern science. It allows us to find insights in a sea of data, ask principled questions about the future, and perform experiments without a laboratory. In this class, we’ll learn the practice of python programming, and quantitative questioning, through both data-driven and model-oriented case studies focused on the earth, the universe, and living systems. Taught with Jeremy Bloxham.
Natural disasters, including earthquakes, hurricanes, and pandemics, claim thousands of lives and cause tens of billions of dollars in damage each year. In this course, we develop an understanding of these natural hazards from an earth science perspective and examine several case studies to assess their catastrophic impacts.
Science in the age of artificial intelligence
Science is focused on discovering and explaining the world around and within us. The central goal of this class is to question the classical role of the scientist as a creator of theories and consider how scientists may become interpreters of theories developed with machine learning assistance.
Mathematical models are ubiquitous, providing a quantitative framework for understanding, prediction and decision making in nearly every aspect of life, ranging from timing traffic lights, to controlling the spread of disease, to climate, and increasingly in the social sciences such as economics, scheduling, renewal theory. Taught with Zhiming Kuang and Lakshminarayanan Mahadevan.
The missing Matlab course
Being able to write a working program is not just about syntax of the programming language but involves other skills such as debugging and being able to convert concepts and algebra to a sequence of commands. This is focused on developing Matlab programming techniques for a diverse set of applied scientific problems. Taught with Miaki Ishii.