Spring 2023 Schedule

Monday
Swampland Seminar: 11:00am - 12:00pm ET, bi-weekly

Tuesday
Member Seminar: 12:00pm - 1:00pm ET
Quantum Matter in Mathematics and Physics Seminar: 1:15pm - 2:45pm ET

Wednesday
Colloquium: 12:30pm - 1:30pm ET
New Technologies in Mathematics Seminar: 2:00pm - 3:00pm ET
Probability Seminar: 3:30pm - 4:30pm ET

Thursday
General Relativity Seminar: 9:30am - 10:30am ET
Active Matter Seminar: 1:00pm - 2:00pm ET, bi-weekly

Friday
Algebraic Geometry in String Theory Seminar: 9:30am - 10:30am ET
Quantum Matter in Mathematics and Physics Seminar: 10:30am - 12:00pm ET


  • Tuesday, February 7, 2023
  • 12:00 PM
Category: Member Seminar
Speaker: Chuck Doran, CMSA
Title: Motivic Geometry of Two-Loop Feynman Integrals
Member Seminar Speaker: Chuck Doran Title: Motivic Geometry of Two-Loop Feynman Integrals Abstract: We study the geometry and Hodge theory of the cubic hypersurfaces attached to two-loop Feynman integrals for generic physical parameters. We show that the Hodge structure attached to planar two-loop Feynman graphs decomposes into a mixed Tate piece and a variation of Hodge structure from families of hyperelliptic curves, elliptic curves, or rational curves depending on the space-time dimension. We give more precise results for two-loop graphs with a small number of edges. In particular, we recover a result of Spencer Bloch that in the well-known double box example there is an underlying family of elliptic curves, and we give a concrete description of these elliptic curves. We show...
  • Wednesday, February 8, 2023
  • 12:30 PM
Category: Colloquia
Speaker: David Gamarnik, MIT
Title: From spin glasses to Boolean circuits lower bounds &#; Algorithmic barriers from the overlap gap property
Speaker: David Gamarnik (MIT) Title: From spin glasses to Boolean circuits lower bounds. Algorithmic barriers from the overlap gap property Abstract: Many decision and optimization problems over random structures exhibit an apparent gap between the existentially optimal values and algorithmically achievable values. Examples include the problem of finding a largest independent set in a random graph, the problem of finding a near ground state in a spin glass model, the problem of finding a satisfying assignment in a random constraint satisfaction problem, and many many more. Unfortunately, at the same time no formal computational hardness results exist which  explains this persistent algorithmic gap. In the talk we will describe a new approach for establishing an algorithmic intractability for these problems called...
  • Wednesday, February 8, 2023
  • 03:30 PM
Category: Probability Seminar
Speaker: Roland Bauerschmidt, Cambridge
Title: Title TBA
Probability Seminar Speaker: Roland Bauerschmidt (Cambridge)
  • Thursday, February 9, 2023
  • 01:30 PM
Category: General Relativity Seminar
Speaker: Maciej Zworski, UC Berkeley
Title: Quasinormal modes and Ruelle resonances: mathematician&#;s perspective
General Relativity Seminar Speaker: Maciej Zworski, UC Berkeley Title: Quasinormal modes and Ruelle resonances: mathematician's perspective Abstract: Quasinormal modes of gravitational waves and Ruelle resonances in hyperbolic classical dynamics share many general properties and can be considered "scattering resonances": they appear in expansions of correlations, as poles of Green functions and are associated to trapping of trajectories (and are both notoriously hard to observe in nature, unlike, say, quantum resonances in chemistry or scattering poles in acoustical scattering). I will present a mathematical perspective that also includes zeros of the Riemann zeta function (scattering resonances for the Hamiltonian given by the Laplacian on the modular surface) and stresses the importance of different kinds of trapping phenomena, resulting, for...
  • Wednesday, February 15, 2023
  • 03:30 PM
Category: Probability Seminar
Title: Title TBA
Probability Seminar Speaker: Zhigang Yao (Harvard CMSA/National University of Singapore)
  • Wednesday, February 22, 2023
  • 02:00 PM
Category: New Technologies in Mathematics Seminar
Speaker: Sean Welleck, University of Washington and AI2
Title: New Technologies in Mathematics Seminar &#; Title TBA
New Technologies in Mathematics Seminar Speaker: Sean Welleck,  University of Washington and AI2      
  • Wednesday, February 22, 2023
  • 03:30 PM
Category: Probability Seminar
Speaker: Vishesh Jain, UIC
Title: Title TBA
Probability Seminar Speaker: Vishesh Jain (UIC)
  • Wednesday, March 8, 2023
  • 02:00 PM
Category: New Technologies in Mathematics Seminar
Speaker: Jimmy Ba, University of Toronto
Title: New Technologies in Mathematics Seminar &#; Title TBA
New Technologies in Mathematics Seminar Speaker: Jimmy Ba, University of Toronto    
  • Wednesday, March 8, 2023
  • 03:30 PM
Category: Probability Seminar
Speaker: Jean-Christophe Mourrat, Lyon
Title: Title TBA
Probability Seminar Speaker: Jean-Christophe Mourrat (Lyon)
  • Monday, March 13, 2023
  • 11:00 AM
Category: Swampland Seminar
Speaker: David Andriot, Annecy, LAPTH
Title: Swampland Seminar &#; Title TBA
Swampland Seminar Speaker: David Andriot (Annecy, LAPTH)
  • Wednesday, March 22, 2023
  • 03:30 PM
Category: Probability Seminar
Speaker: Wei-Kuo Chen, Minnesota
Title: Title TBA
Probability Seminar Speaker: Wei-Kuo Chen (Minnesota)
  • Monday, March 27, 2023
  • 11:00 AM
Category: Swampland Seminar
Speaker: Valerio De Luca, UPenn
Title: Swampland Seminar &#; Title TBA
Swampland Seminar Speaker: Valerio De Luca (UPenn)
  • Wednesday, April 5, 2023
  • 03:30 PM
Category: Probability Seminar
Speaker: Ahmed El Alaoui, Cornell
Title: Title TBA
Probability Seminar Speaker: Ahmed El Alaoui (Cornell)
  • Monday, April 10, 2023
  • 11:00 AM
Category: Swampland Seminar
Speaker: Seung-Joo Lee (IBS Daejeon), IBS Daejeon
Title: Swampland Seminar &#; Title TBA
Swampland Seminar Speaker: Seung-Joo Lee (IBS Daejeon)
  • Wednesday, April 12, 2023
  • 12:30 PM
Category: Colloquia
Speaker: James Halverson, Northeastern University
Title: Unexpected Uses of Neural Networks: Field Theory and Metric Flows  
Speaker: James Halverson (Northeastern University)   Title: Unexpected Uses of Neural Networks: Field Theory and Metric Flows Abstract:  We are now quite used to the idea that deep neural networks may be trained in a variety of ways to tackle cutting-edge problems in physics and mathematics, sometimes leading to rigorous results. In this talk, however, I will argue that breakthroughs in deep learning theory are also useful for making progress, focusing on applications to field theory and metric flows. Specifically, I will introduce a neural network approach to field theory with a different statistical origin, that exhibits generalized free field behavior at infinite width and interactions at finite width, and that allows for the study of symmetries via the study of correlation functions...