In general, my lab's research focuses on intelligent systems in computational science and engineering. In the area of computational biology, we are pursuing a mixed computational-experimental approach to the structural and functional understanding of and control over the molecular machinery of the cell. We are developing algorithms and systems to automatically plan experiments, predict outcomes, interpret data, revise models, and so forth. In the area of qualitative reasoning about physical systems, we are focusing on analysis of spatially distributed data, for example, in phase portrait representations and for decentralized control design. We are developing and applying a general framework that navigates a hierarchy from input data to abstract description and back, using a mixture of numeric, symbolic, and geometric reasoning.

The figures below (and the menu on the left) point to pages with high-level descriptions of some of our current projects.

This work is funded in part by the following:

• An Alfred P. Sloan Research Fellowship, to C. Bailey-Kellogg.
• A grant from the NSF SEIII program: "Integration of multimodal experiments for protein structure", to C. Bailey-Kellogg, B. Craig (Statistics, Purdue) and A. Friedman (Biological Sciences, Purdue).
• An NSF CAREER award: "Sparse Spatial Reasoning for High-Throughput Protein Structure Determination", to C. Bailey-Kellogg.

We gratefully acknowledge previous support from the following:

• An award from the Showalter Foundation: "Distribution Functions of Standard Free Energies of Protein-Protein Association", to C. Bailey-Kellogg and M. Laskowski (Chemistry, Purdue).
• A grant from Purdue's Computing Research Institute, through the PRF-SIRG program: "Multimodal Discrimination of Protein Fold", to C. Bailey-Kellogg and A. Friedman (Biological Sciences, Purdue).