This Week

Here is a selection of seminars that might be of interest to string theorists in Oxford:

On Monday, May 25, at 12:45 in Virtual, a seminar in the String Theory series:
Andrew Dancer (University of Oxford)
Symplectic duality and implosion -- ZOOM SEMINAR
Further information: We discuss hyperkahler implosion spaces, their relevance to group actions and why they should fit into the symplectic duality picture. For certain groups we present candidates for the symplectic duals of the associated implosion spaces and provide computational evidence. This is joint work with Amihay Hanany and Frances Kirwan.  
On Monday, May 25, at 14:15 in Virtual, a seminar in the Geometry and Analysis series:
Cyril Closset (Oxford)
Quantum K-theory and 3d A-model
Further information: I will discuss some ongoing work on three-dimensional supersymmetric gauge theories and their relationship to (equivariant) quantum K-theory. I will emphasise the interplay between the physical and mathematical motivations and approaches, and attempt to build a dictionary between the two.  As an interesting example, I will discuss the quantum K-theory of flag manifolds. The QK ring will be related to the vacuum structure of a gauge theory with Chern-Simons interactions, and the (genus-0) K-theoretic invariants will be computed in terms of explicit residue formulas that can be derived from the relevant supersymmetric path integrals.
On Tuesday, May 26, at 14:00 in zoom, a seminar in the Cosmology series:
Dan Scolnic (Duke University)
New Advances with Type Ia Supernovae To Measure The Expansion of the Universe
On Thursday at 13:00 in virtual, a seminar in the Dalitz Seminar in Fundamental Physics series:
Lucian Harland-Lang (Oxford)
Physics from Photons at the LHC
On Thursday at 15:30 in via Zoom, a seminar in the Machine Learning and Physics series:
Thomas Kipf (Google Brain)
Interaction network inference with graph neural networks
On Thursday at 16:00 in, a seminar in the Theoretical Particle Physics series:
Vishnu Jejjala (Mandelstam Institute for Theoretical physics, Johannesburg)
Machine learning as a discovery tool in hep-th