Fri, 27 Feb 2026

14:00 - 15:00
L1

Where can a mathematics degree take you?

Abstract

In this week's Fridays@2, a panel of representatives from a range of companies who employ mathematics graduates will be here to answer your questions.A degree in mathematics opens doors far beyond academia, but what do those paths really look like? Join us for a panel event bringing together mathematicians working across Finance, Digital Services, Technology, Consulting, Data Analytics, and Teaching.

Our speakers will share their career journeys, how they moved from studying mathematics into industry roles, and what their day to day work involves. This is your opportunity to gain insight into the skills employers value, the challenges and opportunities in different sectors, and the many ways mathematical thinking shapes real world impact.

Whether you already have a clear goal or are still exploring your options, come along with your questions and curiosity and discover where maths could take you.

Fri, 20 Feb 2026

14:00 - 15:00
L1

AI and programming

Dominik Lukeš
Abstract

Dominik Lukeš from the AI Competency Centre will give an introductory survey of AI in relation to programming.

Fri, 06 Mar 2026
16:00
L1

We are all different: Modeling key individual differences in physiological systems

Anita Layton
Abstract
Mathematical models of whole-body dynamics have advanced our understanding of human integrative systems that regulate physiological processes such as metabolism, temperature, and blood pressure. For most of these whole-body models, baseline parameters describe a 35-year-old young adult man who weighs 70 kg. As such, even among adults those models may not accurately represent half of the population (women), the older population, and those who weigh significantly more than 70 kg. Indeed, sex, age, and weight are known modulators of physiological function. To more accurately simulate a person who does not look like that “baseline person,” or to explain the mechanisms that yield the observed sex or age differences, these factors should be incorporated into mathematical models of physiological systems. Another key modulator is the time of day, because most physiological processes are regulated by the circadian clocks. Thus, ideally, mathematical models of integrative physiological systems should be specific to either a man or woman, of a certain age and weight, and a given time of day. A major goal of our research program is to build models specific to different subpopulations, and conduct model simulations to unravel the functional impacts of individual differences.


 

Fri, 08 May 2026
16:00
L1

TBA

Prof. Zaher Hani
(University of Michigan)
Abstract

TBA

Mon, 26 Jan 2026

17:00 - 18:00
L1

Enhancing Wind Energy Using Unsteady Fluid Mechanics

Prof. John Dabiri
(California Institute of Technology, USA)
Abstract

This talk will describe recent studies of how time-dependent, unsteady flow physics can be exploited to improve the performance of energy harvesting systems such as wind turbines. A theoretical analysis will revisit the seminal Betz derivation to identify the role of unsteady flow from first principles. Following will be a discussion of an experimental campaign to test the predictions of the theoretical model. Finally, a new line of research related to turbulence transition and inspired by the work of T. Brooke Benjamin will be introduced.

 

Further Information

For more information, please visit: The Brooke Benjamin Lecture in Fluid Mechanics | Mathematical Institute 

Fri, 20 Feb 2026
16:00
L1

Where do you draw the (dividing) line?

Julia Wolf
(Cambridge)
Abstract
A longstanding classification programme in model theory aims to determine when a mathematical structure exhibits tame, structurally simple—as opposed to wild, intractable—behaviour. A key role is played by so-called dividing lines, i.e. properties of logical formulas (or theories) that separate these regimes. In this talk, we demonstrate how the lens of combinatorics has allowed us to gain new insight into higher-order dividing lines, drawing on examples in graphs and groups. We also explain how this perspective has led to advances in higher-order Fourier analysis and statistical learning.
 
This talk intends to be accessible to beginning graduate students in all areas of mathematics.


 

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