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
20 August 2019

We investigate a nonlinear version of coevolving voter models, in which both node states and network structure update as a coupled stochastic dynamical process. Most prior work on coevolving voter models has focused on linear update rules with fixed rewiring and adopting probabilities. By contrast, in our nonlinear version, the probability that a node rewires or adopts is a function of how well it "fits in" within its neighborhood. To explore this idea, we incorporate a parameter σ that represents the fraction of neighbors of an updating node that share its opinion state. In an update, with probability σq (for some nonlinearity parameter q), the updating node rewires; with complementary probability 1−σq, the updating node adopts a new opinion state. We study this mechanism using three rewiring schemes: after an updating node deletes a discordant edge, it then either (1) "rewires-to-random" by choosing a new neighbor in a random process; (2) "rewires-to-same" by choosing a new neighbor in a random process from nodes that share its state; or (3) "rewires-to-none" by not rewiring at all (akin to "unfriending" on social media). We compare our nonlinear coevolving model to several existing linear models, and we find in our model that initial network topology can play a larger role in the dynamics, whereas the choice of rewiring mechanism plays a smaller role. A particularly interesting feature of our model is that, under certain conditions, the opinion state that is initially held by a minority of nodes can effectively spread to almost every node in a network if the minority nodes views themselves as the majority. In light of this observation, we relate our results to recent work on the majority illusion in social networks.



Kureh, Yacoub H., and Mason A. Porter. "Fitting In and Breaking Up: A Nonlinear Version of Coevolving Voter Models." arXiv preprint arXiv:1907.11608 (2019).

9 September 2019
11 September 2019
See below for speaker information

Further Information: 

The goal of the research workshop "Higher-order interaction networks: dynamics, structure, data" is to bring together researchers from these different communities with distinct perspectives on network dynamics —- from network science, dynamical systems, and data science/machine learning -- to develop novel approaches to understand networked systems. By cutting across different mathematical communities, this will allow to develop new tools, for example by exploring links between data driven methods (such as machine learning) and dynamics. A particular focus of this workshop will be on the role of non-dyadic dynamical interactions (joint interactions between more than two nodes) whose importance for the modeling, analysis, and control of such networked systems have recently been highlighted.

Expressions of interest are now open with an initial deadline of June 1, 2019, with notification of acceptance no later than June 15, 2019.

Participation in the workshop will cost a nominal fee of £50 which will be used to cover catering during the workshop. Participants will also have the chance to attend the workshop dinner on Wednesday 10th September at the nearby Somerville College, the cost of which will be £30.  

Thanks to generous funding from EU and the London Mathematical Society, there is limited travel support for UK-based early career researchers available. Please indicate whether you wish to apply for support during registration.

For further information including registration please click here.

Confirmed Speakers:

Lou Pecora (Naval Research Labs)
Tanya Berger-Wolf (Illinois)
Santiago Segarra (Rice)
Tiago Pereira (USP Sao Carlos)
Marta Sales-Pardo (Barcelona)
Jacopo Grilli (Santa Fe Institute/ICTP Trieste)
Marya Bazzi (ATI)
Rebecca Hoyle (Southampton)
Ana Paula Dias (Porto)
Laetitia Gauvin (ISI Torino)
Heather Harrington (Oxford)
Rodolphe Sepulchre (Cambridge)
Jess Enright (Stirling)
Peter Ashwin (Exeter)
Pawel Dlotko (Swansea)

10 September 2019
11 September 2019

Further Information: 

The Training School will bring together a multi-disciplinary group of clinicians, biomedical engineers, biologists and physical scientists to present recent advances in mathematical, computational, in-vitro, and in-vivo approaches to further our understanding of fluid mechanics within the stented ureter and to identify current challenges in urinary stent design. Moreover, leading speakers from the world of industry and regulatory affairs will share their experiences of commercialisation in the medtech industry, and how they have addressed industrial and regulatory challenges when taking their “next-generation” products from bench-to-bedside.

Here is a preliminary program.

We would like to encourage Early Career Researchers (Master students, PhD students, and PostDocs) to apply as trainees, by sending their CV and a short statement (of no more than 250 words) to  , explaining why they would like to attend the Training School. Participants are encouraged to present a poster about their work, and should send a title of their poster together with their application.

We will award 15 grants to fund accommodation, travel, and subsistence of trainees

Applications should be submitted by July 15th, and applicants will be notified by the end of July about the outcome of their application.

17 September 2019
Florian Klimm

In this seminar, I first discuss a paper by Aslak et al. on the detection of intermittent communities with the Infomap algorithm. Second, I present own work on the detection of intermittent communities with modularity-maximisation methods. 

Many real-world networks represent dynamic systems with interactions that change over time, often in uncoordinated ways and at irregular intervals. For example, university students connect in intermittent groups that repeatedly form and dissolve based on multiple factors, including their lectures, interests, and friends. Such dynamic systems can be represented as multilayer networks where each layer represents a snapshot of the temporal network. In this representation, it is crucial that the links between layers accurately capture real dependencies between those layers. Often, however, these dependencies are unknown. Therefore, current methods connect layers based on simplistic assumptions that do not capture node-level layer dependencies. For example, connecting every node to itself in other layers with the same weight can wipe out dependencies between intermittent groups, making it difficult or even impossible to identify them. In this paper, we present a principled approach to estimating node-level layer dependencies based on the network structure within each layer. We implement our node-level coupling method in the community detection framework Infomap and demonstrate its performance compared to current methods on synthetic and real temporal networks. We show that our approach more effectively constrains information inside multilayer communities so that Infomap can better recover planted groups in multilayer benchmark networks that represent multiple modes with different groups and better identify intermittent communities in real temporal contact networks. These results suggest that node-level layer coupling can improve the modeling of information spreading in temporal networks and better capture intermittent community structure.

Aslak, Ulf, Martin Rosvall, and Sune Lehmann. "Constrained information flows in temporal networks reveal intermittent communities." Physical Review E 97.6 (2018): 062312.


18 September 2019
19 September 2019
Various Speakers

Further Information: 

The cost for registration is £80. This includes lunch and coffee both days of the workshop, and drinks at a reception following the public lecture on Wednesday 18th September. Registration should be completed through the University of Oxford Online stores:

Deadline for registration: July 5th. Space is limited, so register early to avoid disappointment!



This meeting is being held in celebration of Prof Philip Maini's 60th birthday. Prof Maini has been an internationally leading researcher in mathematical biology for decades. He is currently the Director of the Wolfson Centre for Mathematical Biology, a position he has held since 1998. In the past 20 years he has grown the group significantly. He has established countless interdisciplinary collaborations, has over 400 publications in numerous areas of mathematical biology, with major contributions in mathematical modelling of tumours, wound healing and embryonic pattern formation. He has been elected Fellow of the Royal Society (FRS), Fellow of the Academy of Medical Sciences (FMedSci), and Foreign Fellow of the Indian National Science Academy (FNA). He has served or is serving on editorial board of a large number of journals, and was Editor-in-Chief of the Bulletin of Mathematical Biology [2002-15]. And yet his service to the community cannot be captured just by numbers and titles. Anyone who has met him and worked with him cannot but notice and be touched by his unfailing generosity and the many sacrifices he has made and continues to make day in and day out to help students, early career researchers, and fellow faculty alike.

This meeting provides an opportunity to celebrate Prof Maini's many accomplishments; to thank him for all of his sacrifices; and to bring together the large number of researchers – mathematicians, biologists, physiologists, and clinicians – that he has worked with and interacted with over the years. More broadly, the meeting provides a unique opportunity to reflect on mathematical biology, to provide perspectives on the trajectory of a field that was scarcely recognised and had very few dedicated researchers in the days of Prof Maini's own DPhil; yet a field that has grown tremendously since then. Much of this growth is attributable to the work of Prof Maini, so that today the value of mathematics in biology is increasingly recognized by biologists and clinicians, and with theoretical predictions of mathematical models having cemented a role in advancing biological understanding. 


David SumpterUppsala University (Public lecture), Derek MoultonUniversity of Oxford, Hans OthmerMinnesota University, Jen Flegg, University of Melbourne, Jim MurrayUniversity of Washington, Jonathan SherrattHeriot-Watt University, Kevin PainterHeriot-Watt University, Linus Schumacher, University of Edinburgh, Lucy HutchinsonRoche, Mark ChaplainUniversity of St Andrews, Mark LewisUniversity of Alberta, Mary MyerscoughUniversity of Sydney, Natasha MartinUniversity of Bristol, Noemi Picco, Swansea University, Paul Kulesa, Stowers Institute for Medical Research, Ruth Baker, University of Oxford, Santiago SchnellUniversity of Michigan, Tim Pedley, University of Cambridge


Organising committee

Ruth Baker (University of Oxford)

Derek Moulton (University of Oxford)

Helen Byrne (University of Oxford)

Santiago Schnell (University of Michigan)

Mark Chaplain (University of St Andrews)

18 September 2019
David Sumpter

Further Information: 

Former Barcelona, Bayern Munich and current Manchester City coach Pep Guardiola is considered by many to be a footballing genius. He has revolutionised the tactical approach to football and that revolution has come about through his careful study of the geometry of the game. But can abstract mathematics really help a team improve its performance?

David Sumpter thinks it can. Unlike the simple statistics applied to (lesser) sports, football is best understood through the patterns the players create together on the field. From the geometry of shooting, through the graph theory of passing, to the tessellations created by players as they find space to move in to, all of these patterns can be captured by mathematical models. As a result, football clubs are increasingly turning to mathematicians. 

David Sumpter is Professor of Applied Mathematics at the University of Uppsala, Sweden. His scientific research covers everything from the inner workings of fish schools and ant colonies, the analysis of the passing networks of football teams and segregation in society.

5.00pm-6.00pm, Mathematical Institute, Oxford

Please email to register

Watch live:

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

8 October 2019
Marya Bazzi

Multilayer networks are a way to represent dependent connectivity patterns — e.g., time-dependence, multiple types of interactions, or both — that arise in many applications and which are difficult to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as communities, to discover features that lie between the microscale and the macroscale. We introduce a framework for the construction of generative models for mesoscale structure in multilayer networks.  We model dependency at the level of partitions rather than with respect to edges, and treat the process of generating a multilayer partition separately from the process of generating edges for a given multilayer partition. Our framework can admit many features of empirical multilayer networks and explicitly incorporates a user-specified interlayer dependency structure. We discuss the parameters and some properties of our framework, and illustrate an example of its use with benchmark models for multilayer community-detection tools. 



Add to My Calendar