Welcome to the Kavraki Lab webpage
Openings: • Postdoctoral research associate in computational structural biology and bioinformatics. • Undergraduates in robotics and bioinformatics. • Prospective graduate students, please contact Dr. Kavraki directly.
Our group does research in physical algorithms and has two main applications areas: Robotics and Bioinformatics.
In robotics we are interested in motion planning with emphasis on high-dimensional systems and kinodynamic planning, assembly planning, reasoning with sensing and control uncertainty, flexible object manipulation, physical modeling, probabilistic methods in robotics, the geometry of motion and the use of new enabling technologies such as MicroElectroMechanical Systems.
In computational structural biology and bioinformatics we develop computational tools on high-performance systems to model protein structure and function, understand biomolecular interactions and help analyze, in the long run, the molecular machinery of the cell. We integrate sequence information with three-dimensional structural information to capture, represent and exploit relevant molecular motion.
Both areas above involve real-world problems and fall into the broader category of physical computing. In both areas we seek to develop physical algorithms: algorithms that are capable of solving complex high-dimensional geometric problems arising in real-world applications (e.g., move a robot from A to B, predict a biomolecular complex). We believe that as computers become ubiquitous, we need to use computers to represent, simulate, and interact with the physical world. This is not an easy task, however. Algorithms for physical problems differ in significant ways from those for traditional (artificial world) problems. The latter algorithms have full control over and perfect access to the required data. In contrast, physical algorithms apply to objects in the real world which are subject to the independent and imperfectly modeled laws of nature. Our long term goal is to study the fundamental issues arising when algorithms are designed for problems in the physical world and to develop coherent solution frameworks which quantify, to the extent possible, the tradeoff between accuracy and performance present in solutions developed for realistic settings.
Our research is currently supported by NSF, NIH, and a Sloan Fellowship. For our work, we use high-performance computer-clusters supported by NSF in partnership with Rice University, AMD and Cray. Part of our work is supported by the National Science Foundation through TeraGrid resources.