Alon Efrat

University of Arizona, Stanford University, Laboratoire d'Informatique de Paris-Nord

Papers

Total Citations

147

H-Index

About

Alon Efrat is a computational geometer whose research bridges theoretical algorithm design with high-impact practical applications across robotics, bioinformatics, and sensor systems. His most celebrated contribution lies at the intersection of geometry and proteomics: his work on geometric algorithms for analyzing two-dimensional gel electrophoresis gels (2002, 55 citations) introduced rigorous computational methods for automating protein spot identification and gel cross-comparison, a notoriously difficult image analysis problem that had long resisted algorithmic treatment. This work helped modernize proteomics workflows by reducing reliance on time-consuming manual inspection. Efrat has also made significant contributions to robot motion planning, particularly his 2000 paper (50 citations) on strategies for efficient 3D model construction, demonstrating his ability to translate geometric insights into actionable robotic behavior. His 2004 research on optimal target-tracking strategies within polygonal environments further showcases his talent for solving visibility and pursuit problems with provably efficient algorithms. Additional contributions to geometric pattern matching for segment collections and sensor system algorithms round out a research portfolio that consistently combines mathematical elegance with real-world relevance, making Efrat a valuable figure for students working at the frontier of applied computational geometry.