Liz Bradley
Location: (Boulder, CO)
Personal Research Web Page: http://www.cs.colorado.edu/~lizb
Keywords: nonlinear dynamics & chaos, data assimilation, dynamics of human movement, automating scientific reasoning, computational topology, fluid dynamics.
Posted on: Wednesday, May 12th, 2010
Broad Research Area: AI / Machine Learning / Robotics / Vision, Numerical/Scientific Computing / HPC / Data-Intensive Scalable Computing, Other
Research Interests:
My research interests fall at the intersection of nonlinear dynamics, artificial intelligence, and control theory. I am deeply intrigued by the process of modelling: what you can learn — and accomplish — by describing something in the language of mathematics. My students and I have worked on a variety of analysis and synthesis problems whose solutions, at their core, are rooted in the modelling process. The applications involved have ranged from computer architecture to internet attacks, and meltwater ponds on the arctic ice sheets, and the mathematics involved has ranged from topology to time-series analysis, but the underlying questions are the same: what is the right abstraction to use for a given problem, when does it work, how does it fail, and what does all of that enable? Using the right mathematical representations, it becomes obvious, for instance, that computers are often chaotic, that fluids can be mixed by exploiting sensitive dependence on initial conditions, and that denial-of-service attacks can be handled gracefully if one works with their nonlinear nature.
Dynamical systems theory gives us a great way to think about human movement. In collaboration with Jessica Hodgins, we are using this mathematics to model and simulate dance. Along with scientific papers, this collaboration has produced a performance piece entitled “Con/cantation: chaotic variations,” which involves a human dancer and computer animations projected on three 10? by 10? screens. One potential postdoc project would be to explore whether movement — dance and other types — “lives” on some lower-dimensional manifold, and what techniques might be useful in finding that manifold. Another potential postdoc project would focus on the cognitive neuroscience angle of this problem, working with an international group of scientists that is just forming to explore how movement, dynamics, and the perception of beauty interact.
The focus of our AI work is on automating scientific reasoning. In collaboration with software engineers and climate scientists, we have developed computer tools that help geoscientists deduce the ages of landforms from cosmogenic isotope data. This tool is not simply a number cruncher; rather, it uses AI techniques to capture the expert’s knowledge and help him or her make sense of complex relationships in sparse, noisy data. We have just initiated a similar project involving another forensic scientific reasoning problem: the deduction of age models from ice and sediment cores. This would be a particularly appropriate postdoc project for someone whose interests fall at the intersection of computer science and climate science, as well as for someone who is interested in the techniques and ontologies involved in the automation of scientific reasoning.
