Forthcoming events in this series


Fri, 03 Feb 2023

15:30 - 16:30
Large Lecture Theatre, Department of Statistics, University of Oxford

Statistics' Florence Nightingale Lecture

Professor Marloes Matthuis
(ETH Zurich)
Further Information

Title: “Causal learning from observational data”

Please register in advance using the online form: https://www.stats.ox.ac.uk/events/florence-nightingale-lecture-2023

Marloes Henriette Maathuis is a Dutch statistician known for her work on causal inference using graphical models, particularly in high-dimensional data from applications in biology and epidemiology. She is a professor of statistics at ETH Zurich in Switzerland.

Abstract

I will discuss a line of work on estimating causal effects from observational data. In the first part of the talk, I will discuss identification and estimation of causal effects when the underlying causal graph is known, using adjustment. In the second part, I will discuss what one can do when the causal graph is unknown. Throughout, examples will be used to illustrate the concepts and no background in causality is assumed.

Fri, 27 Jan 2023
16:00
L1

How to give a talk

Abstract

In this session, we will hold a panel discussion on how to best give an academic talk. Among other topics, we will focus on techniques for engaging your audience, for determining the level and technical details of the talk, and for giving both blackboard and slide presentations. The discussion will begin with a directed panel discussion before opening up to questions from the audience.

Fri, 20 Jan 2023
16:00
L1

Departmental Colloquium

Professor James Maynard
(Mathematical Institute (University of Oxford))
Further Information

Title: “Prime numbers: Techniques, results and questions”

Abstract

The basic question in prime number theory is to try to understand the number of primes in some interesting set of integers. Unfortunately many of the most basic and natural examples are famous open problems which are over 100 years old!

We aim to give an accessible survey of (a selection of) the main results and techniques in prime number theory. In particular we highlight progress on some of these famous problems, as well as a selection of our favourite problems for future progress.

Fri, 25 Nov 2022

16:00 - 17:00
L1

Maths Meets Stats

Matthew Buckland (Statistics) and Ofir Gorodetsky (North Wing)
Abstract

Matthew Buckland 
Branching Interval Partition Diffusions

We construct an interval-partition-valued diffusion from a collection of excursions sampled from the excursion measure of a real-valued diffusion, and we use a spectrally positive Lévy process to order both these excursions and their start times. At any point in time, the interval partition generated is the concatenation of intervals where each excursion alive at that point contributes an interval of size given by its value. Previous work by Forman, Pal, Rizzolo and Winkel considers self-similar interval partition diffusions – and the key aim of this work is to generalise these results by dropping the self-similarity condition. The interval partition can be interpreted as an ordered collection of individuals (intervals) alive that have varying characteristics and generate new intervals during their finite lifetimes, and hence can be viewed as a class of Crump-Mode-Jagers-type processes.

 

 

Ofir Gorodetsky
Smooth and rough numbers


We all know and love prime numbers, but what about smooth and rough numbers?
We'll define y-smooth numbers -- numbers whose prime factors are all less than y. We'll explain their application in cryptography, specifically to factorization of integers.
We'll shed light on their density, which is modelled using a peculiar differential equation. This equation appears naturally in probability theory.
We'll also explain the dual notion to smooth numbers, that of y-rough numbers: numbers whose prime factors are all bigger than y, and in some sense generalize primes.
We'll explain their importance in sieve theory. Like smooth numbers, their density has interesting properties and will be surveyed.

 

Fri, 11 Nov 2022

16:00 - 17:00
L1

Managing your supervisor

Eva Antonopoulou
Abstract

Your supervisor is the person you will interact with on a scientific level most of all during your studies here. As a result, it is vital that you establish a good working relationship. But how should you do this? In this session we discuss tips and tricks for getting the most out of your supervisions to maximize your success as a researcher. Note that this session will have no faculty in the audience in order to allow people to speak openly about their experiences. 

Fri, 04 Nov 2022

16:00 - 17:00
L1

Illustrating Mathematics

Joshua Bull and Christoph Dorn
Abstract
What should we be thinking about when we're making a diagram for a paper? How do we help it to express the right things? Or make it engaging? What kind of colour palette is appropriate? What software should we use? And how do we make this process as painless as possible? Join Joshua Bull and Christoph Dorn for a lively Fridays@4 session on illustrating mathematics, as they share tips, tricks, and their own personal experiences in bringing mathematics to life via illustrations.
Fri, 28 Oct 2022

16:00 - 17:00
L1

North Meets South

Ilia Smilga and Charles Parker
Abstract

Ilia Smilga
Margulis spacetimes and crooked planes

We are interested in the following problem: which groups can act 
properly on R^n by affine transformations, or in other terms, can occur 
as a symmetry group of a "regular affine tiling"? If we additionally 
require that they preserve a Euclidean metric (i.e. act by affine 
isometries), then these groups are well-known: they all contain a 
finite-index abelian subgroup. If we remove this requirement, a 
surprising result due to Margulis is that the free group can act 
properly on R^3. I shall explain how to construct such an action.

 

Charles Parker
Unexpected Behavior in Finite Elements for Linear Elasticity
One of the first problems that finite elements were designed to approximate is the small deformations of a linear elastic body; i.e. the 2D/3D version of Hooke's law for springs from elementary physics. However, for nearly incompressible materials, such as rubber, certain finite elements seemingly lose their approximation power. After briefly reviewing the equations of linear elasticity and the basics of finite element methods, we will spend most of the time looking at a few examples that highlight this unexpected behavior. We conclude with a theoretical result that (mostly) explains these findings.

 

 

Fri, 21 Oct 2022

16:00 - 17:00
L1

Maintaining your mental fitness as a graduate student or postdoc

Rebecca Reed and Ian Griffiths
Abstract

Academic research can be challenging and can bring with it difficulties in maintaining good mental health. This session will be led by Rebecca Reed, Mental Health First Aid (MHFA) Instructor, Meditation & Yoga Teacher and Personal Development Coach and owner of wellbeing company Siendo. Rebecca will talk about how we can maintain good mental fitness, recognizing good practices to ensure we avoid mental-health difficulties before they begin. We have deliberately set this session to be at the beginning of the academic year in this spirit. We will also talk about maintaining good mental health specifically in the academic community.   

Fri, 14 Oct 2022

16:00 - 17:00
L1

Meet and Greet Event

Amy Kent and Ellen Luckins
Abstract

Abstract: 

Welcome (back) to Fridays@4! To start the new academic year in this session we’ll introduce what Fridays@4 is for our new students and colleagues. This session will be a chance to meet current students and ECRs from across Maths and Stats who will share their hints and tips on conducting successful research in Oxford. There will be lots of time for questions, discussions and generally meeting more people across the two departments – everyone is welcome!

 

Fri, 10 Jun 2022

16:00 - 17:00
L2

Maths Meets Stats

Melanie Weber and Francesca Panero
Abstract

Melanie Weber 

Title: Geometric Methods for Machine Learning and Optimization

Abstract: A key challenge in machine learning and optimization is the identification of geometric structure in high-dimensional data. Such structural understanding is of great value for the design of efficient algorithms and for developing fundamental guarantees for their performance. Motivated by the observation that many applications involve non-Euclidean data, such as graphs, strings, or matrices, we discuss how Riemannian geometry can be exploited in Machine Learning and Optimization. First, we consider the task of learning a classifier in hyperbolic space. Such spaces have received a surge of interest for representing large-scale, hierarchical data, since they achieve better representation accuracy with fewer dimensions. Secondly, we consider the problem of optimizing a function on a Riemannian manifold. Specifically, we will consider classes of optimization problems where exploiting Riemannian geometry can deliver algorithms that are computationally superior to standard (Euclidean) approaches.

 

Francesca Panero

Title: A general overview of the different projects explored during my DPhil in Statistics.

Abstract: In the first half of the talk, I will present my work on statistical models for complex networks. I will propose a model to describe sparse spatial random graph underpinned by the Bayesian nonparametric theory and asymptotic properties of a more general class of these models, regarding sparsity, degree distribution and clustering coefficients.

The second half will be devoted to the statistical quantification of the risk of disclosure, a quantity used to evaluate the level of privacy that can be achieved by publishing a microdata file without modifications. I propose two ways to estimate the risk of disclosure, using both frequentist and Bayes nonparametric statistics.

 

Fri, 13 May 2022

16:00 - 17:00
L2

Mental health and wellbeing

Rebecca Reed (Siendo)
Abstract

*Note the different room location (L2) to usual Fridays@4 sessions*

This week is Mental Health Awareness Week. To mark this, Rebecca Reed from Siendo will deliver a session on mental health and wellbeing. The session will cover the following things: 

- The importance of finding a balance with achievement and managing stress and pressure.
- Coping mechanisms work with stresses at work in a positive way (not seeing all stress as bad).
- The difficulties faced in the HE environment, such as the uncertainty felt within jobs and research, combined with the high expectations and workload. 

 

Fri, 29 Apr 2022

16:00 - 17:00
L1

North Meets South

Akshat Mugdal and Renee Hoekzema
Abstract
Speaker: Akshat Mugdal
 
Title: Fantastic arithmetic structures and where to find them
 
Abstract: This talk will be a gentle introduction to additive combinatorics, an area lying somewhat at the intersection of combinatorics, number theory and harmonic analysis, which concerns itself with identification and classification of sets with additive structure. In this talk, I will outline various notions of when a finite set of integers may be considered to be additively structured and how these different notions interconnect with each other, with various examples sprinkled throughout. I will provide some further applications and open problems surrounding this circle of ideas, including a quick study of sets that exhibit multiplicative structure and their interactions with the aforementioned notions of additivity.
 
 
Speaker: Renee Hoekzema 

Title: Exploring the space of genes in single cell transcriptomics datasets

Abstract: Single cell transcriptomics is a revolutionary technique in biology that allows for the measurement of gene expression levels across the genome for many individual cells simultaneously. Analysis of these vast datasets reveals variations in expression patterns between cells that were previously out of reach. On top of discrete clustering into cell types, continuous patterns of variation become visible, which are associated to differentiation pathways, cell cycle, response to treatment, adaptive heterogeneity or what just whatever the cells are doing at that moment. Current methods for assigning biological meaning to single cell experiments relies on predefining groups of cells and computing what genes are differentially expressed between them. The complexity found in modern single cell transcriptomics datasets calls for more intricate methods to biologically interpret both discrete clusters as well as continuous variations. We propose topologically-inspired data analysis methods that identify coherent gene expression patterns on multiple scales in the dataset. The multiscale methods consider discrete and continuous transcriptional patterns on equal footing based on the mathematics of spectral graph theory. As well as selecting important genes, the methodology allows one to visualise and explore the space of gene expression patterns in the dataset.

Fri, 01 Apr 2022

16:00 - 17:00
L3

What's it like working for Citadel Securities?

Oliver Sheriden-Methven (Citadel Securities)
Abstract

Dr Oliver Sheridan-Methven from Citadel Securities, (an InFoMM and MScMCF alumni), will be talking about his experiences from studying at the Mathematical Institute, interviewing for jobs, to working in finance. Now in Zurich, Oliver is a quantitative developer in the advanced scientific computing team at Citadel Securities, a world leading market maker. Citadel Securities specialises in ultra high frequency trading, low latency execution, and their researchers tackle cutting edge machine learning and data science problems on colossal data sets with humongous computational resources. Oliver will be talking about his own experiences, and also how mathematicians are naturally great fits for a huge number of roles at Citadel Securities.

Fri, 25 Feb 2022

16:00 - 17:00
L1

North Meets South

Pascal Heid and Ilyas Khan
Abstract

This event will be hybrid and will take place in L1 and on Teams. A link will be available 30 minutes before the session begins.

Pascal Heid
Title: Adaptive iterative linearised Galerkin methods for nonlinear PDEs

Abstract: A wide variety of iterative methods for the solution of nonlinear equations exist. In many cases, such schemes can be interpreted as iterative local linearisation methods, which can be obtained by applying a suitable linear preconditioning operator to the original nonlinear equation. Based on this observation, we will derive an abstract linearisation framework which recovers some prominent iteration schemes. Subsequently, in order to cast this unified iteration procedure into a computational scheme, we will consider the discretisation by means of finite dimensional subspaces. We may then obtain an effective numerical algorithm by an instantaneous interplay of the iterative linearisation and an (optimally convergent) adaptive discretisation method. This will be demonstrated by a numerical experiment for a quasilinear elliptic PDE in divergence form.   

 

Ilyas Khan
Title: Geometric Analysis: Curvature and Applications

Abstract: Often, one will want to find a geometric structure on some given manifold satisfying certain properties. For example, one might want to find a minimal embedding of one manifold into another, or a metric on a manifold with constant scalar curvature, to name some well known examples of this sort of problem. In general, these problems can be seen as equivalent to solving a system of PDEs: differential relations on coordinate patches that can be assembled compatibly over the whole manifold to give a globally defined geometric equation.

In this talk, we will present the theories of minimal surfaces and mean curvature flow as representative examples of the techniques and philosophy that geometric analysis employs to solve problems in geometry of the aforementioned type. The description of the theory will be accompanied by a number of examples and applications to other fields, including physics, topology, and dynamics. 

Fri, 18 Feb 2022

16:00 - 17:00
L1

Conferences and collaboration

Abstract

This event will be hybrid and will take place in L1 and on Teams. A link will be available 30 minutes before the session begins.

`Conferences and collaboration’ is a Fridays@4 group discussion. The goal is to have an open and honest conversion about the hurdles posed by these things, led by a panel of graduate students and postdocs. Conferences can be both exciting and stressful - they involve meeting new people and learning new mathematics, but can be intimidating new professional experiences. Many of us also will either have never been to one in person, or at least not been to one in the past two years. Optimistically looking towards the world opening up again, we thought it would be a good time to ask questions such as:
-Which talks should I go to?
-How to cope with incomprehensible talks. Is it imposter syndrome or is the speaker just bad?
-Should I/how should I go about introducing myself to more senior people in the field?
-How do you start collaborations? Does it happen at conferences or elsewhere?
-How do you approach workload in collaborations?
-What happens if a collaboration isn’t working out?
-FOMO if you like working by yourself. Over the hour we’ll have a conversation about these hurdles and most importantly, talk about how we can make conferences and collaborations better for everyone early in their careers.

Fri, 04 Feb 2022

16:00 - 17:00
L1

Careers outside of academia

Kim Moore (Faculty AI) and Sébastien Racanière (Google DeepMind)
Abstract

This event will take place on Teams. A link will be available 30 minutes before the session begins.

Sebastien Racaniere is a Staff Research Engineer at DeepMind. His current main interest is in the use of symmetries in Machine Learning. This offers diverse applications, for example in Neuroscience or Theoretical Physics (in particular Lattice Quantum Chromodynamics). Past interests, still in Machine Learning, include Reinforcement Learning (i.e. learning from rewards), generative models (i.e. learn to sample from probability distributions), and optimisation (i.e. how to find 'good' minima of functions)

 

Kim Moore is a senior data scientist at faculty, which is a data science consultancy based in London. As a data scientist, her role is to help our clients across sectors such as healthcare, government and consumer business solve their problems using data science and AI. This involves applying a variety of techniques, ranging from simple data analysis to designing and implementing bespoke machine learning algorithms. Kim will talk about day to day life at faculty, some interesting projects that I have worked on and why her mathematical background makes her a great data scientist.
Fri, 28 Jan 2022

16:00 - 17:00
L1

North Meets South

Kaibo Hu and Davide Spriano
Abstract

This event will be hybrid and will take place in L1 and on Teams. A link will be available 30 minutes before the session begins.

Kaibo Hu
Title: Complexes from complexes
Abstract:
Continuous and discrete (finite element) de Rham complexes have inspired key progress in the mathematical and numerical analysis of the Maxwell equations. In this talk, we derive new differential complexes from the de Rham complexes. These complexes have applications in, e.g., general relativity and continuum mechanics. Examples include the elasticity (Kröner or Calabi) complex, which encodes fundamental structures in Riemannian geometry and elasticity. This homological algebraic construction is inspired by the Bernstein-​Gelfand-Gelfand (BGG) machinery from representation theory. Analytic results, e.g., various generalisations of the Korn inequality, follow from the algebraic structures. We briefly discuss applications in numerical PDEs and other fields.

Davide Spriano

Title: Growth of groups.

Abstract:
Given a transitive graph, it is natural to consider how many vertices are contained in a ball of radius n, and to study how this quantity changes as n increases. We call such a function the growth of the graph.

In this talk, we will see some examples of growth of Cayley graph of groups, and survey some classical results. Then we will see a dichotomy in the growth behaviour of groups acting on CAT(0) cube complexes.  

Fri, 21 Jan 2022

16:00 - 17:00
L1

Thriving in, or perhaps simply surviving, academia: insights gained after nearly 40 years in STEM

Margot Gerritsen
(Stanford)
Abstract

This event will take place in L1 and on Teams. A link will be available 30 minutes before the session begins. 

 

It's hard to believe: I've spent nearly 40 years in STEM. In that time, much changed: we changed from typewriters to PCs, from low performance to high  performance computing, from data-supported research to data-driven research, from traditional languages such as Fortran to a plethora of programming environments. And the rate of change seems to increase constantly. Some things have stayed more or less the same, such as the (lack of) diversity of the STEM community, the level of stress and the struggles we all experience (and the joys!). In this talk, I will reflect on those years, on lessons learned and not learned or unlearned, on things I wish I understood 40 years ago, and on things I still don't understand.

Margot is a professor at Stanford University in the Department of Energy Resources Engineering (ERE) and the Institute of Computational & Mathematical Engineering (ICME). Margot was born and raised in the Netherlands. Her STEM education started in 1982. In 1990 she received a MSc in applied mathematics at Delft University and then left her home country to search for sunnier and hillier places. She moved to Colorado and a year later to California to join the PhD program in Scientific Computing and Computational Mathematics at Stanford. During her PhD, Margot spent several quarters at Oxford University (with very good memories). Before returning to Stanford as faculty member in ERE, Margot spent 5 years as lecturer at the University of Auckland, New Zealand. From 2010-2018, Margot was the director of ICME. During this directorship, she founded the Women in Data Science initiative, which is now a global organization in over 70 countries. From 2015-2020, Margot was also the Senior Associate Dean of Educational Affairs at Stanford's school of Earth, Energy & Environmental Sciences. Currently, Margot still co-directs WiDS and is the Chair of the Board of SIAM. She has since moved back to the mountains (still sunny too) and now lives in Bend, Oregon.

Fri, 03 Dec 2021

16:00 - 17:00
L1

North Meets South

Candida Bowtell and Joshua Bull
(Mathematical Institute)
Abstract

This session will take place live in L1 and online. A Teams link will be shared 30 minutes before the session begins.

 

Candida Bowtell

Title: Chess puzzles: from recreational maths to fundamental mathematical structures

Abstract:
Back in 1848, in a German chess magazine, Max Bezzel asked how many ways there are to place 8 queens on a chessboard so that no two queens can attack one another. This question caught the attention of many, including Gauss, and was subsequently generalised. What if we want to place n non-attacking queens on an n by n chessboard? What if we embed the chessboard on the surface of a torus? How many ways are there to do this? It turns out these questions are hard, but mathematically interesting, and many different strategies have been used to attack them. We'll survey some results, old and new, including progress from this year.


 

Joshua Bull

Title: From Cancer to Covid: topological and spatial descriptions of immune cells in disease

Abstract:
Advances in medical imaging techniques mean that we have increasingly detailed knowledge of the specific cells that are present in different diseases. The locations of certain cells, like immune cells, gives clinicians clues about which treatments might be effective against cancer, or about how the immune system reacts to a Covid infection - but the more detailed this spatial data becomes, the harder it is for medics to analyse or interpret. Instead, we can turn to tools from topological data analysis, mathematical modelling, and spatial statistics to describe and quantify the relationships between different cell types in a wide range of medical images. This talk will demonstrate how mathematics can be used as a tool to advance our understanding of medicine, with a focus on immune cells in both cancer and covid-19.

Fri, 26 Nov 2021

16:00 - 17:00
L1

Sharing the joy of Maths: Creating a workshop for school students

Mareli Grady (Outreach Events Coordinator) and Vicky Neale (Whitehead Lecturer)
(University of Oxford)
Abstract

This session will take place live in L1 only and not online on Teams. 

Are you interested in sharing your love of Maths with the next generation of mathematicians, but you don’t know where to start? In this session we will discuss some basic principles and top tips for creating a workshop for students aged 14–16, and get you started on developing your own. There will also be the opportunity to work on this further afterwards and potentially deliver your session as part of the Oxfordshire Maths Masterclasses (for local school students) in Hilary Term. Bring along your favourite bit of maths and a willingness to have a go.

 

Fri, 19 Nov 2021

16:00 - 17:00
L1

Mathematigals

(Mathematical Institute)
Abstract

This session will take place live in L1 and online. A Teams link will be shared 30 minutes before the session begins.

How can we make maths more accessible, promote its many applications, and encourage more women to enter the field? These are the questions we aim to address with Mathematigals.

Caoimhe Rooney and Jessica Williams met in 2015 at the start of their PhDs in mathematics in Oxford, and in 2020, they co-founded Mathematigals. Mathematigals is an online platform producing content to demonstrate fun mathematical curiosities, showcase ways maths can be used in real life, and promote female mathematicians. Mathematigals primarily produces animated videos that present maths in a way that is engaging to the general public.

In this session, Jess and Caoimhe will talk about their initial motivation to begin Mathematigals, demonstrate the process behind their content creation, and describe their future visions for the platform. The session will end with an opportunity for the audience to provide feedback or ideas to help Mathematigals on their journey to encourage future mathematicians.

 

Fri, 12 Nov 2021

16:00 - 17:00
L1

North Meets South

Anna Parlak and Gill Grindstaff
(Mathematical Institute)
Abstract

This session will take place live in L1 and online. A Teams link will be shared 30 minutes before the session begins.

Fri, 29 Oct 2021

16:00 - 17:00
L1

Applying for academic jobs

Edwina Yeo and Jay Swar
(Mathematical Institute)
Abstract

This session will take place live in L1 and online. A Teams link will be shared 30 minutes before the session begins.

Fri, 22 Oct 2021

16:00 - 17:00
L1

What does a DPhil in Oxford look like?

Brian Tyrrell, Naya Yerolemou and Alice Kerr
(Mathematical Institute)
Abstract

This session will take place live in L1 and online. A Teams link will be shared 30 minutes before the session begins.