ACM Paris Kanellakis Theory and Practice Award
University of Maryland United States – 2011
CITATION

For fundamental contributions to the development of multidimensional spatial data structures and indexing.


Dealing with multidimensional numerical data naturally arises in spatial, optimization, and learning problems. Accessing such information rapidly requires efficient and preferably elegantly simple data structures. If a distance metric can be associated to data, then spatial data structures may be used to index and to navigate data quickly and hierarchically. Such structures are ubiquitous in myriad areas, including databases, biomedical imaging, computer graphics and vision, geographic information systems, geometry, games, computational physics, and scientific computation.

In his pioneering research since the 1980s on quadtrees and other data structures, as well as his well-received books, Prof. Hanan Samet has profoundly influenced the theory and application of multidimensional spatial data structures. His contributions to, and application of, incremental nearest neighbor search, metric navigation of spatial structures, and spatial data mining exemplify the breadth of his work, the impact of which can be seen in a wide array of practical applications.

In addition to this body of work, his 1975 Ph.D. thesis on formal proofs of correctness of compilers, and the symbolic execution of compiled execution sequences, was among the earliest contributions to the field that twenty years later became known as translation validation for compilers.

Fellow
United States – 1996
CITATION

For research and contributions in the area of hierarchical data structures for applications in spatial data bases for computer graphics, image processing, geographic information systems, and robotics.