ACM has named Lydia E. Kavraki of Rice University and Daphne Koller of Stanford University and Insitro recipients of the ACM - AAAI Allen Newell Award.
Lydia Kavraki is recognized for pioneering contributions to robotic motion planning, including the invention of randomized motion planning algorithms and probabilistic roadmaps, with applications to bioinformatics and biomedicine.
Kavraki conducted foundational work on physical algorithms and developed efficient high-dimensional search frameworks that impacted robotics (motion planning, hybrid systems, formal methods in robotics, assembly planning, and micro- and flexible manipulation), as well as computational structural biology, translational bioinformatics, and biomedical informatics.
Kavraki has authored more than 240 peer-reviewed publications and is a co-author of the widely used robotics textbook, Principles of Robot Motion. Her seminal paper, “Probabilistic Roadmaps for Path Planning in High Dimensional Configuration Spaces,” (with Svestka, Latombe and Overmars) was the first to establish a probabilistic approach to developing roadmaps for high-dimensional spaces, which has become one of the key techniques for motion planning for complex physical systems.
Kavraki’s contributions go beyond robotics to address problems underlying the functional annotation of proteins, the understanding of metabolic networks, and the investigation of molecular conformations and protein flexibility. She has contributed to problems that involve reasoning about the three-dimensional structure of biomolecules and their ability to interact with other biomolecules primarily for drug design and, more recently, for personalized cancer immunotherapy.
Daphne Koller is recognized for seminal contributions to machine learning and probabilistic models, the application of these techniques to biology and human health, and for contributions to democratizing education.
Koller was a leader in the development and use of graphical models, including learning the model structure as well as its parameters, and pioneered the unification of statistical learning and relational modelling languages. She also developed foundational methods for inference and learning in temporal models. Her textbook (with Nir Friedman), Probabilistic Graphical Models, is the definitive text in this area.
As an early leader in bringing machine learning methods to the life sciences, she developed Module Networks, wherein she and her colleagues harnessed modularity in gene regulatory programs to build an effective model of gene activity. She has developed groundbreaking applications of machine learning to pathology, work that not only demonstrated the ability of machine learning to outperform human pathologists, but also was one of the first to highlight the importance of the stromal tissue in cancer prognosis (now well-recognized).
Koller is also the co-founder and former co-CEO of Coursera, a platform offering free education from top universities to people worldwide. Coursera, now in its eighth year, has touched the lives of over 50 million learners in every country in the world. Koller is currently the founder and CEO of Insitro, a biotech startup that works to discover better medicines through the integration of machine learning and biology at scale.
