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Department of Mathematical Sciences

Post Exam Activities

Below is a list of activities taking place after the May/June 2017 exam period, including induction sessions for those students progressing to the next level of study.

Induction Sessions

Monday 19 June

10am - 12pm


For students progressing to Level 2.

1pm - 3pm


For students progressing to Level 3.

3pm - 5pm


For students progressing to Level 4.

Academic Advisor Meetings

Thursday 22 & Friday 23 June

Your advisor will be in touch with you to arrange a specific time for meeting with you at the end of this term. The purpose of this advisor meeting is to give you advice on selecting modules for next year, to discuss how the year has gone for you, and to discuss any other questions you may have (carreer, references, etc.).

Lectures & Workshops

Please note, this list is not complete and details will be updated in the coming weeks.

Tuesday 6 June

10am - 12pm, CM101

Writing Your Mathematics Project

Philip Nathan


This session is aimed at supporting you in the writing up of your mathematics project. The session will focus on the persuasive and rhetorical nature of maths project writing, including rhetorical aspects of introduction and conclusion writing in the Maths project, as well as focusing on other structural elements of the project. The characteristics of effective maths project writing will be addressed. The session will be supported and ideas illustrated using writing from previous successful maths projects. More general tips and advice will also be provided about mathematics project writing and writing in general.

Tuesday 6 June

2pm - 4pm, CM107

Mathematics and Card Tricks

Dr David Cushing

"I will present a one hour lecture on a couple of card tricks and the mathematical ideas behind them. First we will investigate the De Brujin sequences (which are used in coding theory) and then we will look at what happens mathematically when a deck is shuffled and how we can use this structure to our advantage in a trick.

For the second hour it will be more of a workshop for those who want to stay. There will be lots of packs of cards for people to learn some maths based tricks."

Thursday 8 June

5pm - 6pm, CG93

3D Shadows: Casting light on the fourth dimension (public lecture)

Henry Segerman (Oklahoma State University)


Our brains have evolved in a three-dimensional environment, and so we are very good at visualising two- and three-dimensional objects. But what about four-dimensional objects? The best we can really do is to look at three-dimensional "shadows". Just as a shadow of a three-dimensional object squishes it into the two-dimensional plane, we can squish a four-dimensional shape into three-dimensional space, where we can then make a sculpture of it. If the four-dimensional object isn't too complicated and we choose a good way to squish it, then we can get a very good sense of what it is like. We will explore the sphere in four-dimensional space, the four-dimensional polytopes (which are the four-dimensional versions of the three-dimensional polyhedra), and various 3D printed sculptures, puzzles, and virtual reality experiences that have come from thinking about these things. I talk about these topics and much more in my new book, "Visualizing Mathematics with 3D Printing".

Thursday 8 - Monday 13 June

Various locations

3D Printing Workshop

Dr Norbert Peyerimhoff & Dr Herbert Gangl

This event is a training opportunity for our PhD students, undergraduate students are also welcome to attend the talks. Confirmed presenters at present are Bekrokh Eskandari (Peacocks Orthotics Limited, Newcastle), Sabetta Matsumoto (Georgia Tech), Saul Schleimer (Warwick), and Henry Segerman (Oklahoma State).

For further details see

Thursday 22 June

9.30am - 12.30pm, CM101

Foundation of Statistics Training Day: Subjectivity, expectation, and exchangeability

Professor Michael Goldstein


We will start by discussing the meaning of a statistical analysis and explore the role of subjectivity and objectivity in the interpretation of statistical methods.

This leads to the issue as to what is the best way to quantify uncertainty. We will explain the fundamental role of expectation as a primitive expression of uncertainty. The statistical approach based around expectation is termed Bayes linear analysis (because of the linearity properties of expectation). An overview of some of the basic features of this approach, and their interpretation, will follow.

Finally, we will discuss the fundamental role of exchangeability in statistical inference, derive de Finetti's representation for an exchangeable sequence of events and give the Bayes linear equivalent form.

Thursday 22 June

1.30pm - 4.30pm, CM101

Foundation of Statistics Training Day: Uncertainty and decision making via desirability

Dr Matthias Troffaes


Since Abraham Wald, decisions have been at the center of classical statistical inference. As we will see in this talk, they also play a central role in the interpretation of probability, and in the practice of inference under severe uncertainty.

We will see how sets of desirable gambles and lower previsions are useful as models for severe uncertainty, applicable for instance when only partial probability specifications are available, and when we are worried about the consequences of implicit assumptions not reflected by data or expert opinion. We explore how such lower and upper expectations naturally arise in decision theory, simply by allowing for indecision, or incomparability between options.

We will investigate the decision theoretic foundation to imprecise probability and its link to standard Bayesian decision theory through the concept of desirability, which provides a more fundamental and geometrically appealing way of modelling uncertainty compared to standard probability. We critically review some fundamental problems encountered when applying imprecise probability theory to decision making. We will then continue to discuss various decision criteria, and why you might use them or not, and if time permits we will also demonstrate some algorithms that can be used to solve decision problems with imprecise probability in practice.