Speaker: Vitaliy Kurlin (http://kurlin.org/)

Title: Mathematical Data Science for solid crystalline materials

Abstract: Most real data is ambiguous in the sense that the same real object has too many (often infinitely many) data representations. For
example, any periodic lattice can be represented by infinitely many different linear bases. Data Science aims to define equivalence
relations on real objects to make their classifications meaningful. The most natural equivalence of solid crystalline materials is rigid
motion or isometry, because periodic crystal structures are determined in a rigid form. This talk will describe recent advances in isometry
classifications of finite and periodic point sets. The implemented isometry invariants completely distinguished all (hundreds of
thousands) real periodic crystals in the world's largest Cambridge Structural Database. The work is joint with colleagues from the Data
Science Theory and Applications group, http://kurlin.org/index.php#group

Bio: Vitaliy Kurlin is a Data Scientist at the Materials Innovation Factory at Liverpool and the Royal Academy Engineering Fellow at the Cambridge Crystallographic Data Centre, who leads the group developing the new area of Periodic Geometry for applications in crystallography and materials science.