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# Events for 11/18/2019 from all calendars

## Working Seminar on Quantum Computation and Quantum Information

## Geometry Seminar

## Industrial and Applied Math

## AMUSE

**Time:** 12:30PM - 1:45PM

**Location:** BOC 624

**Speaker:** Xiaoyu Su, TAMU

**Title:** *Brascamp-Lieb duality for quantum relative entropies*

**Time:** 3:00PM - 4:00PM

**Location:** BLOC 628

**Speaker:** Elise Walker, Texas A&M University

**Title:** *Toric degenerations and optimal homotopies from finite Khovanskii bases*

**Abstract:** Homotopies are useful numerical methods for solving systems of polynomial equations. I will present such a homotopy method using Khovanskii bases. Finite Khovanskii bases provide a flat degeneration to a toric variety, which consequentially gives a homotopy. The polyhedral homotopy, which is implemented in PHCPack, can be used to solve for points on a general linear section of this toric variety. These points can then be traced via the Khovanskii homotopy to points on a general linear slice of the original variety. This is joint work with Michael Burr and Frank Sottile.

**Time:** 4:00PM - 5:00PM

**Location:** Bloc220

**Speaker:** Dr. Nikki Meshkat, Santa Clara University

**Title:** *Structural Identifiability of Biological Models*

**Abstract:** Parameter identifiability analysis addresses the problem of which unknown parameters of a model can be determined from given input/output data. If all of the parameters of a model can be determined from data, the parameters and the model are called identifiable. However, if some subset of the parameters can not be determined from data, the model is called unidentifiable. We examine this problem for the case of perfect input/output data, i.e. absent of any experimental noise. This is called the structural identifiability problem. We show that, even in the ideal case of perfect input/output data, many biological models are structurally unidentifiable, meaning some subset of the parameters can take on an infinite number of values, yet yield the same input/output data. In this case, one attempts to reparametrize the model in terms of new parameters that can be determined from data. In this talk, we discuss the problem of finding an identifiable reparametrization and give necessary and sufficient conditions for a certain class of linear compartmental models to have an identifiable reparametrization. We also discuss finding classes of identifiable models and finding identifiable submodels of identifiable models. Our work uses graph theory and tools from computational algebra. This is joint work with Elizabeth Gross and Anne Shiu.

**Time:** 6:00PM - 7:00PM

**Location:** BLOC 220

**Speaker:** Irina Holmes, Dept of Mathematics, Texas A&M University

**Title:** *The Limitations of the Riemann Integral*

**Abstract:** As an analyst, I found myself paralyzed by the task of speaking about my research in any meaningful way without the knowledge of the Lebesgue integral and measure theory. So, I decided instead to motivate the need for yet another integral: we already have the Riemann integral, why do we need another one? The talk is meant to be accessible to undergraduate students, and the purpose is to reveal the limitations of the Riemann integral, and the ways that the Lebesgue integral comes to the rescue.

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