Our laboratory develops novel computational methodologies for Robotics & AI and Biomedicine.
In Robotics and AI 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 systems with kinematic and dynamic constraints, integrated frameworks for reasoning under sensing and control uncertainty, novel methods for learning and for using experiences, and ways to instruct robots at a high level and collaborate with them. Our work is inspired by a variety of applications: from robots that will assist people in their home, to robots that would build space habitats.
In Biomedicine we develop computational methods and tools to model protein structure and function, understand biomolecular interactions, aid the process of medicinal drug discovery, analyze the molecular machinery of the cell, and help integrate biological and biomedical data for improving human health. We are also interested broadly in metabolic engineering and metabolism prediction. Our work has applications, among others, in personalized immunotherapy and in the design of novel therapeutics.
Through the confluence of algorithms, statistical reasoning, formal methods, machine learning, data science and, importantly, physics modeling, our lab and our associates seek to understand how computers can reason effectively and robustly about problems in the real world.
The Kavraki Lab is currently supported in part by Cancer Prevention and Research Institute of Texas (CPRIT) grant #RP170508, NIH grant #1R21CA209941, NSF NRI #1830549, NSF IIS grant #1718478, NSF CCF grant #1514372 and NSF NRI grant #1317849. 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.