Forthcoming Seminars

Please note that the list below only shows forthcoming events, which may not include regular events that have not yet been entered for the forthcoming term. Please see the past events page for a list of all seminar series that the department has on offer.

Past events in this series
21 August 2020
15:00
Abstract

The TMD algorithm (Kanari et al. 2018) computes the barcode of a neuron (tree) with respect to the radial or path distance from the soma (root). We are interested in the inverse problem: how to understand the space of trees that are represented by the same barcode. Our tool to study this spaces is the stochastic TNS algorithm (Kanari et al. 2020) which generates trees from a given barcode in a biologically meaningful way. 

I will present some theoretical results on the space of trees that have the same barcode, as well as the effect of adding noise to the barcode. In addition, I will provide a more combinatorial perspective on the space of barcodes, expressed in terms of the symmetric group. I will illustrate these results with experiments based on the TNS.

This is joint work with L. Kanari and K. Hess. 

  • Applied Topology Seminar
3 September 2020
16:00
Michael Moor
Abstract

Topological features as computed via persistent homology offer a non-parametric approach to robustly capture multi-scale connectivity information of complex datasets. This has started to gain attention in various machine learning applications. Conventionally, in topological data analysis, this method has been employed as an immutable feature descriptor in order to characterize topological properties of datasets. In this talk, however, I will explore how topological features can be directly integrated into deep learning architectures. This allows us to impose differentiable topological constraints for preserving the global structure of the data space when learning low-dimensional representations.

The join button will be published on the right (Above the view all button) 30 minutes before the seminar starts (login required).

8 October 2020
17:00

Further Information: 

When was the last time you read a grand statement, accompanied by a large number, and wondered whether it could really be true?

Statistics are vital in helping us tell stories – we see them in the papers, on social media, and we hear them used in everyday conversation – and yet we doubt them more than ever. But numbers, in the right hands, have the power to change the world for the better. Contrary to popular belief, good statistics are not a trick, although they are a kind of magic. Good statistics are like a telescope for an astronomer, or a microscope for a bacteriologist. If we are willing to let them, good statistics help us see things about the world around us and about ourselves.

Tim Harford is a senior columnist for the Financial Times, the presenter of Radio 4’s More or Less and is a visiting fellow at Nuffield College, Oxford. His books include The Fifty Things that Made the Modern Economy, Messy, and The Undercover Economist.

Watch live (no need to register):
https://twitter.com/OxUniMaths
https://www.facebook.com/OxfordMathematics/
https://livestream.com/oxuni/Harford

The Oxford Mathematics Public Lectures are generously supported by XTX Markets.

  • Oxford Mathematics Public Lectures
16 October 2020
14:00
Abstract

 Inherent fluctuations may play an important role in biological and chemical systems when the copy number of some chemical species is small. This talk will present the recent work on the stochastic modeling of reaction-diffusion processes in biochemical systems. First, I will introduce several stochastic models, which describe system features at different scales of interest. Then, model reduction and coarse-graining methods will be discussed to reduce model complexity. Next, I will show multiscale algorithms for stochastic simulation of reaction-diffusion processes that couple different modeling schemes for better efficiency of the simulation. The algorithms apply to the systems whose domain is partitioned into two regions with a few molecules and a large number of molecules.

  • Mathematical Biology and Ecology Seminar

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