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About

Matias Korman is a computational geometer and algorithm theorist whose research spans combinatorial optimization, modular robotics reconfiguration, and discrete geometry. His work tackles fundamental questions about how systems — whether abstract tokens on graphs or physical robotic modules — can be efficiently rearranged and transformed. Among his most notable contributions is his investigation into **token swapping**, exploring the computational complexity of executing permutations on graphs, including proving hardness results on tree structures. Equally significant is his sustained work on **modular robot reconfiguration**: Korman has developed universal reconfiguration algorithms for pivoting and sliding modular robots, establishing both efficient algorithms and theoretical hardness boundaries for hexagonal and square-grid configurations. His "O(1) Musketeers" result elegantly demonstrated that just five helper modules suffice to universally reconfigure arbitrary square-grid robot configurations — a landmark theoretical achievement. He has also applied algorithmic thinking to real-world challenges, contributing to UV disinfection robot path planning during the COVID-19 pandemic. While his papers are recent and citation counts are still growing (collectively accumulating over a dozen citations across recent publications), Korman's work bridges theoretical computer science and practical robotics, making meaningful contributions to both communities and establishing him as an emerging voice in computational reconfiguration research.