Our laboratory develops novel computational methodologies for Robotics and Biomedicine.
In robotics we are interested in enabling robots to work with people and in support of people. Our research develops the underlying methodologies for achieving this goal: algorithms for motion planning for high-dimensional kinodynamic systems, integrated methods for reasoning with sensing and control uncertainty, frameworks for learning and using experiences, and ways to instruct robots at a high level and collaborate with them. We work across different applications, from robots that will assist people in their home, to robots that would build space habitats.
In biomedicine we develop computational tools on high-performance systems to model protein structure and function, understand biomolecular interactions, develop new medicinal drugs, and help analyze, in the long run, the molecular machinery of the cell. Our work has applications, among others, in personalized immunotherapy.
Both domains above involve real-world problems. In both domains we seek to develop physical algorithms: algorithms that are capable of solving complex high-dimensional problems arising in real-world applications (e.g., move a robot from A to B or predict if a drug can bind to a receptor). 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 that are subject to the independent and imperfectly modeled laws of nature. Through robotics and biomedicine, we study the fundamental issues arising when algorithms are designed for problems in the physical world and develop coherent solution frameworks that quantify, to the extent possible, the unavoidable tradeoff between accuracy and performance.
The Kavraki Lab is currently supported in part by Cancer Prevention and Research Institute of Texas (CPRIT) grant #RP170508, NIH grant #1R21CA209941, NSF IIS grant #1718478, NSF CCF grant #1514372, NSF NRI grant #1317849, and NSF ABI grant #1262491. Our research is also supported by Rice University Funds.
We use high-performance computer clusters supported by Rice Research Computing and XSEDE.
Two icons on the frontpage (“Robot” by Artem Kovyazin, “Computer Science” by Oliviu Stoian”) are from the Noun Project.