Lydia Kavraki - Biography#

Kavraki received her B.A. in Computer Science from the University of Crete in Greece and her Ph.D. in Computer Science from Stanford University. Her research interests span robotics, AI, and computational biomedicine. In robotics and AI, she is interested in enabling robots to work with people and in support of people. Her 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. Kavraki’s laboratory is inspired by a variety of applications: from robots that will assist people in their homes, to robots that would build space habitats. In biomedicine, she develops 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. Her work has applications, among others, in personalized immunotherapy and in the design of novel therapeutics for asthma. Kavraki’s research blends her extensive interdisciplinary background in computer science, artificial intelligence, machine learning, bioengineering and biomedical sciences promoting the convergence of these disciplines. Work in her group has produced the Open Motion Planning Library (OMPL), an open-source library of motion planning algorithms. The library is based on Kavraki’s extensive work on sampling-based motion planning algorithms. OMPL links directly with the Robot Operating System (ROS) and MoveIt, and it is heavily used in industry and in academia. Other widely used prototypes of the research conducted in her laboratory include HLA-Arena for peptide-HLA complexes, DINC and APE-Gen for molecular docking, and LabelHash for matching three dimensional structural motifs in proteins.

More information about Kavraki's research can be found at as well as and

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