Join us on 27 Nov 2020 | 11:30 AM (London), 5:00 PM IST, 5:15 PM NPT
Register at:-
OR
We are extremely delighted to welcome Prof Yang Hui-He, from Oxford, UK to talk about how the problem of string phenomenology led physics first to algebraic and differential geometry, then to computational geometry, and now to data science and AI. This talk is a taster of his book The Calabi-Yau Landscape: from Geometry to Physics, to Machine-Learning. He is a Professor of Mathematics at City, University of London, Chang-Jiang Chair Professor at NanKai University, and Lecturer at Merton College, Oxford. He studied at Princeton, Cambridge, and MIT and works at the interface of geometry, string theory, and machine-learning.
"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." - - Prof Yang Hui-he
This event is scheduled for 27 November 2020 at 11:30 AM (London) and in collaboration with QUB- School of Mathematics and Physics, QUB-PAMSoc, Physics Initiative Nepal. It's an online talk followed by a Q/A session and will be conducted through Zoom.
String theory—with its cornerstones of supersymmetry and extra space-time dimensions—has been a muse to pure mathematics. The discipline has provided novel methods of attack for a myriad of mathematical problems: from enumerative geometry to Moonshine, from quantum invariants to mock modular forms. Central to string theory is the study of Calabi-Yau manifolds, which serve as a beacon to such important investigations as compactification, mirror symmetry, moduli space, and duality. In this talk, Yang-Hui reviews how the problem of string phenomenology led physics first to algebraic and differential geometry, then to computational geometry, and now to data science and AI. How did this pencil-and-paper driven field become increasingly suited to computational techniques? Within the playground of the Calabi-Yau landscape, accumulated over four decades by physicists, mathematicians, and computer scientists, He shows how the latest techniques in machine-learning can help explore problems of physical and mathematical interest. This talk is a taster of his book The Calabi-Yau Landscape: from Geometry to Physics, to Machine-Learning.
For more information:
Email address: madhav@bmuco.org
Phone no: +44-7746895174