We briefly overview how historically string theory led theoretical physics first to algebraic/differential geometry, and then to computational geometry, and now to data science. Using the Calabi-Yau landscape - accumulated by the collaboration of physicists, mathematicians and computer scientists over the last 4 decades - as a starting-point and concrete playground, we then launch to review our recent programme in machine-learning mathematical structures and address the tantalizing question of how AI helps doing mathematics, ranging from geometry, to representation theory, to combinatorics, to number theory.

Yang-Hui He, Professor of Mathematics & Senior Tutor for Research, City, University of London (Major appointment); Quondam Fellow & Tutor by Invitation in Mathematics, Merton College, University of Oxford, UK (Joint appointment); Chang Jiang Chair Professor of Physics NanKai University, Tian Jin, P.R. China (Joint appointment).
Prof. He is mathematician working on the interface between geometry (computational algebraic geometry), data science (machine-learning applied to mathematical structures) and theoretical physics (string/gauge theory). He has 185 publications, 6223 total citations (16 papers are 100+) and h-index = 42 (google scholar). He has given over 100 colloquia, seminars, conference talks and public lectures.