Lydia E. Kavraki

About

Lydia E. Kavraki is a University Professor and the Kenneth and Audrey Kennedy Professor of Computing at Rice University, where she also serves as a professor of Computer Science, Electrical & Computer Engineering, Mechanical Engineering, and Bioengineering. She is the director of the Ken Kennedy Institute at Rice University. Kavraki earned her B.A. in Computer Science from the University of Crete in Greece and her Ph.D. in Computer Science from Stanford University, working with Professor Jean-Claude Latombe. Her research broadly spans robotics, computational biomedicine, and physical AI, with a focus on developing methodologies for motion planning, reasoning under uncertainty, learning, and high-level robot instruction to enable robots to work seamlessly with humans. Her seminal work on sampling-based motion planning algorithms, including the Probabilistic Roadmap Planner, has significantly advanced the field, reducing planning times from minutes to seconds and achieving microsecond-level planning for complex manipulations. Her group has produced widely used open-source tools such as the Open Motion Planning Library (OMPL) and maintains several web servers for biomedical applications. Kavraki's research has been funded by numerous agencies including NSF, NIH, DOD, NASA, industry, and CPRIT. She has contributed to the development of robotic systems for space operations, such as NASA’s Robonaut2, and biomedical tools for protein structure modeling, drug discovery, and…

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Engineering drawing
• Computational biology
• Engineering
• Mathematics
• Pharmacology
• Chemistry
• Biochemistry

Selected publications

• Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds

  Tissue Engineering Part A · 2020 · 111 citations

  Senior authorCorresponding

  Various material compositions have been successfully used in 3D printing with promising applications as scaffolds in tissue engineering. However, identifying suitable printing conditions for new materials requires extensive experimentation in a time and resource-demanding process. This study investigates the use of Machine Learning (ML) for distinguishing between printing configurations that are likely to result in low-quality prints and printing configurations that are more promising as a first…

  DOI

• Prediction of drug metabolites using neural machine translation

  Chemical Science · 2020 · 54 citations

  Senior authorCorresponding

  Metabolic processes in the human body can alter the structure of a drug affecting its efficacy and safety. As a result, the investigation of the metabolic fate of a candidate drug is an essential part of drug design studies. Computational approaches have been developed for the prediction of possible drug metabolites in an effort to assist the traditional and resource-demanding experimental route. Current methodologies are based upon metabolic transformation rules, which are tied to specific enzy…

  DOI

• MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

  IEEE Robotics and Automation Letters · 2021 · 51 citations

  Senior authorCorresponding

  Recently, there has been a wealth of development in motion planning for robotic manipulation—new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="…

  DOI

• TCR-pMHC Binding Specificity Prediction From Structure Using Graph Neural Networks

  IEEE Transactions on Computational Biology and Bioinformatics · 2025-01-01 · 10 citations

  articleOpen accessSenior author

  The mapping of T-cell-receptors (TCRs) to their cognate peptides is crucial to improving cancer immunotherapy. Numerous computational methods and machine learning tools have been proposed to aid in the task. Yet, accurately constructing this map computationally remains a difficult problem. Most prior work has sought to predict TCR-peptide-MHC (TCR-pMHC) binding specificity by analyzing the amino acid sequences of the TCRs and peptides. However, recent advancements in crystallography, cryo-EM, an…

  PublisherOA PDFDOI

• DINC-ensemble: A web server for docking large ligands incrementally to an ensemble of receptor conformations

  Journal of Molecular Biology · 2025-04-21 · 5 citations

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

Recent grants

• RI: Small: A Novel Framework for Informed Manipulation Planning

  NSF · $441k · 2020–2024

• SHF: Medium: Automating robot programming through constraint solving and motion planning

  NSF · $816k · 2015–2021

• CSR/EHS: A Robotics-Inspired Approach for the Verification of Hybrid Systems

  NSF · $420k · 2006–2011

Frequent coauthors

• Mark Moll

  97 shared

• Didier Devaurs

  49 shared

• Nicanor Silva

  Norwegian University of Science and Technology

  49 shared

• Jorge I. Poveda

  49 shared

• Dinler A. Antunes

  43 shared

• Cecilia Clementi

  41 shared

• Zachary Kingston

  35 shared

• Moshe Y. Vardi

  31 shared

Labs

• Kavraki LabPI

  Physical AI, Robotics & Biomedicine Lab

Awards & honors

• IEEE Frances E. Allen medal (2023)
• ACM Grace Murray Hopper Award (2000)
• ACM Athena Lecturer Award (2017)
• ACM/AAAI Allen Newell Award (2020)
• Early Academic Career Award from the IEEE Society on Robotic…

Similar researchers at Rice University

• Richard G. Baraniukh-97
• Stephen Wongh-90