Despite the stereotype of the lone genius working by themselves, most professional mathematicians collaborate with others.  But when you're learning maths as a student, is it OK to work with other people, or is that cheating?  And if you're not used to collaborating with others, then you might feel shy about discussing your ideas when you're not confident about them.  In this session, we'll explore ways in which you can get the most out of collaborations with your fellow students, whilst avoiding inadvertently passing off other people's work as your own.  This session will be suitable for undergraduate and MSc students at any stage of their degree who would like to increase their confidence in collaboration.  Please bring a pen or pencil!

In this interactive workshop, we'll discuss what mathematicians are looking for in written solutions.  How can you set out your ideas clearly, and what are the standard mathematical conventions?  Please bring a pen or pencil! 

This session is likely to be most relevant for first-year undergraduates, but all are welcome.

What should you expect in intercollegiate classes?  What can you do to get the most out of them?  In this session, experienced class tutors will share their thoughts, and a current student will offer tips and advice based on their experience.  

All undergraduate and masters students welcome, especially Part B and MSc students attending intercollegiate classes. (Students who attended the Part C/OMMS induction event will find significant overlap between the advice offered there and this session!)

Decision problems – those in which the goal is to return an answer of “YES" or “NO" – are at the heart of the theory of computational complexity, and remain the most studied problems in the theoretical algorithms community. However, in many real-world applications it is not enough just to decide whether the problem under consideration admits a solution: we often want to find all solutions, or at least count (either exactly or approximately) their  total number. It is clear that finding or counting all solutions is at least as computationally difficult as deciding whether there exists a single solution, and  indeed in many cases it is strictly harder (assuming P is not equal NP) even to count approximately the number of solutions than it is to decide whether there exists at least one.

In this talk I will discuss a restricted family of problems, in which we are interested in solutions of a given size: for example, solutions could be copies of a specific k-vertex graph H in a large host graph G, or more generally k-vertex subgraphs of G that have some specified property (e.g. k-vertex subgraphs that are connected). In this setting, although exact counting is strictly harder than decision (assuming standard assumptions in parameterised complexity), the methods typically used to separate approximate counting from decision break down. Indeed, I will demonstrate a method that, subject to certain additional assumptions, allows us to transform an efficient decision algorithm for a problem of this form into an approximate counting algorithm with essentially the same running time.

This is joint work with John Lapinskas (Bristol) and Holger Dell (ITU Copenhagen).

Measurement outcomes in quantum theory are randomly distributed, and local measurements of the energy density of a QFT exhibit nontrivial fluctuations even in a vacuum state. This talk will present recent progress in determining the probability distribution for such measurements. In the specific case of 1+1 dimensional CFT, there are two methods (one based on Ward identities, the other on "conformal welding") which can lead to explicit closed-form results in some cases. The analogous problem for the free field in 1+3 dimensions will also be discussed.