COURSES

Goals

The goal of the course is to familiarize the student with the field of computational scientific discovery, i.e., with computational approaches to automating or supporting crucial aspects of scientific discovery. Basic concepts will be covered first, including the scientific method and the elements of scientific behavior, which include scientific knowledge structures, as well as scientific activities that generate and manipulate these structures. The history of the development of the field will be presented and its relation to recent developments such as mining scientific data will be discussed. A range of techniques will be covered, as well as their applications in environmental sciences (ecology) and life sciences (bioinformatics). The students will acquire a basic understanding of scientific knowledge structures and activities, as well as computer methods to support their automation.

Curriculum

1) The scientific method Scientific knowledge structures, scientific activities/processes. 2) Computational scientific discovery Introduction, history of development of the area, basic methods, e.g., equation discovery, discovering networks, discovering pathways, inductive process modelling. 3) Mining scientific data Specific requirements for mining scientific data vs. data mining in business, finance, retail. 4) Applications in Environmental Sciences Habitat modeling, modeling population dynamics. 5) Applications in Life Sciences Applications in bioinformatics, biomedicine, and systems biology, e.g., predicting gene function, discovering metabolic and regulation pathways. 6) Introduction to e-Science The Grid, work-flows, semantic web/Grid, scientific ontologies.