Past Events

Abstract: Although there is an explicit isomorphism that relates the Hilbert scheme of points on an abelian surface to compactified Jacobians of curves in a linear system on the dual abelian surface, computing its action directly is challenging...

Abstract: Dynamical modeling for the spread of an infectious disease has 100 years of history. Traditional compartmental models such as SIR and SEIR, empirical models and data-driven models were all used during the COVID-19 epidemic. They played an...

Abstract of the talk: In his 1938 thesis, C. Godbersen conjectured that, among convex bodies of fixed positive volume, the mixed volume of a body and its reflection about the origin is maximized by simplices. Although not stated explicitly in...

Students of grades 5 to 7 are invited to challenge and develop their thinking and creativity skills at the Math Circle Workshops. This is an enrichment program for students organized by the faculty members of the Department of Mathematics at the...

We construct the 2D Yang-Mills-Higgs (YMH) measure via stochastic quantization. To this end, we show global well-posedness and uniform-in-time bounds for the associated Langevin dynamics, which is given by the 2D stochastic YMH equations. A key...

The work deals with establishing the solvability of a system of integro-differential equations in the situation of the double scale anomalous diffusion. Each equation of such system involves the sum of the two negative Laplace operators raised to two...

Abstract: This presentation introduces a multi-frequency approach to reconstructing width defects in elastic waveguides. Unlike conventional inverse methods, our approach exploits resonant frequencies, at which the governing propagation equations...

Abstract: We develop an ensemble inference framework for valid post-fusion inference in the context of supervised homogeneity pursuit (SHoP). This methodology allows to fuse numerous micro-level weak signals into a few macro-level strong predictors...

We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polynomials. This gives the first sample-efficient LDP learners for several...