## Archived seminars in MathematicsSeminars 1 to 50 | Next 50 seminars |

### Gemma Mason

*University of Auckland*

Date: Tuesday 10 October 2017

### Honours and PGDip students

*Department of Mathematics and Statistics*

Date: Friday 6 October 2017

Jodie Buckby : ~~Model checking for hidden Markov models~~

Jie Kang : ~~Model averaging for renewal process~~

Yu Yang : ~~Robustness of temperature reconstruction for the past 500 years~~

MATHEMATICS

Sam Bremer : ~~An effective model for particle distribution in waterways~~

Joshua Mills : ~~Hyperbolic equations and finite difference schemes~~

### Ken Ono

*Emory University; 2017 NZMS/AMS Maclaurin Lecturer*

Date: Thursday 5 October 2017

Ramanujan’s work has had a truly transformative effect on modern mathematics, and continues to do so as we understand further lines from his letters and notebooks. In this lecture, some of the studies of Ramanujan that are most accessible to the general public will be presented and how Ramanujan’s findings fundamentally changed modern mathematics, and also influenced the lecturer’s work, will be discussed. The speaker is an Associate Producer of the film ~~The Man Who Knew Infinity~~ (starring Dev Patel and Jeremy Irons) about Ramanujan. He will share several clips from the film in the lecture.

Biography: Ken Ono is the Asa Griggs Candler Professor of Mathematics at Emory University. He is considered to be an expert in the theory of integer partitions and modular forms. He has been invited to speak to audiences all over North America, Asia and Europe. His contributions include several monographs and over 150 research and popular articles in number theory, combinatorics and algebra. He received his Ph.D. from UCLA and has received many awards for his research in number theory, including a Guggenheim Fellowship, a Packard Fellowship and a Sloan Fellowship. He was awarded a Presidential Early Career Award for Science and Engineering (PECASE) by Bill Clinton in 2000 and he was named the National Science Foundation’s Distinguished Teaching Scholar in 2005. In addition to being a thesis advisor and postdoctoral mentor, he has also mentored dozens of undergraduates and high school students. He serves as Editor-in-Chief for several journals and is an editor of The Ramanujan Journal. He is also a member of the US National Committee for Mathematics at the National Academy of Science.

### Ken Ono

*Emory University; 2017 NZMS/AMS Maclaurin Lecturer*

Date: Thursday 5 October 2017

In 1927 Pólya proved that the Riemann Hypotheses is equivalent to the hyperbolicity of Jensen polynomials for Riemann’s Xi-function. This hyperbolicity has been proved for degrees $d\leq 3$. We obtain an arbitrary precision asymptotic formula for the derivatives $\Xi^{(2n)}(0)$ which allows us to prove the hyperbolicity of 100% of the Jensen polynomials of each degree. We obtain a general theorem which models such polynomials by Hermite polynomials. This theorem also allows us to prove a conjecture of Chen, Jia, and Wang on the partition function.

This is joint work with Michael Griffin, Larry Rolen and Don Zagier.

### Jörg Frauendiener

*Department of Mathematics and Statistics*

Date: Tuesday 26 September 2017

In this talk the origin of the Penrose inequality and some attempts and special cases of its proof will be discussed in more detail.

### Philippe LeFloch

*Université Pierre et Marie Curie*

Date: Tuesday 19 September 2017

This lecture will present recent work on a class of partial differential equations arising in mathematical physics in the context of Einstein's theory of gravity. Specifically, I will consider the question of the nonlinear stability of Minkowski spacetime and I will review the global evolution problem for self-gravitating massive matter. The presentation will kept at an introductory level, accessible to students and non-experts.

### Philippe LeFloch

*Université Pierre et Marie Curie, visiting William Evans fellow*

Date: Wednesday 13 September 2017

Waves surround us, and many technological advances were made possible only because engineers, physicists, and applied mathematicians worked together in order to understand these phenomena. Understanding shock waves was essential to design the modern airliners, which we use to travel. Understanding electro-magnetic waves propagating in space (and time!) was essential to design the GPS global navigation system and allow us to use cell phones. In this lecture, from the perspective of an applied mathematician, I will illustrate with examples the role of mathematics in overcoming practical problems using pioneering works by Leonhard Euler, James Maxwell, and Albert Einstein. Blog: https://philippelefloch.org/

### Melissa Tacy

*Department of Mathematics and Statistics*

Date: Tuesday 12 September 2017

### Richard Norton

*Department of Mathematics and Statistics*

Date: Tuesday 5 September 2017

Markov chain Monte Carlo (MCMC) methods compute a sequence of correlated samples of the random variable, and estimate the expectation by an average over the samples. The error of computing finitely many samples is estimated by computing estimates of the integrated autocorrelation time and variance. Generally, to be accurate, MCMC requires cheap evaluations of the density function.

When the density function is costly (in CPU time) to evaluate then we can try replacing it with a 'proxy' that is cheap to evaluate and perform MCMC on the proxy. But what error does this introduce?

I will present a computable upper bound for this error and apply it to a couple of simple examples.

### Alice Harang

*Department of Marine Science*

Date: Tuesday 22 August 2017

### Markus Antoni

*Department of Mathematics and Statistics*

Date: Tuesday 15 August 2017

\begin{equation*}

d X(t) + A X(t) d t = F(t,X(t)) d t + B(t,X(t)) d β(t)

\end{equation*}

for random fields $X \colon \Omega \times [0,T] \times U \to \mathbb{R}$, where $[0,T]$ is a time interval, $(\Omega,\mathcal{F},\mathbb{P})$ a measure space representing the randomness of the system, and $U$ is typically a domain in $\mathbb{R}^d$ (or again a measure space). We reduce the existence and uniqueness of solutions to a fixed point equation in certain fixed point spaces. To be more precise, we look for mild solutions so that $X(\omega,\cdot,\cdot)$ has values in $L^p(U;L^q[0,T])$ for almost all $\omega \in \Omega$ under appropriate Lipschitz and linear growth conditions on the nonlinearities $F$ and $B$. In contrast to the classical semigroup approach, which gives $X(\omega,\cdot,\cdot) \in L^q([0,T];L^p(U))$, the order of integration is reversed. In combination with concrete examples of stochastic partial differential equations we show that this new approach leads to strong regularity results in particular for the time variable of the random field $X(\omega,t,u)$, e.g.

*pointwise*Hölder estimates for the paths $t \mapsto X(\omega,t,u), \mathbb{P}$-almost surely.

### Johannes Mosig

*Department of Mathematics and Statistics*

Date: Tuesday 8 August 2017

I will present my Mathematica package which makes it easy to work with gPC methods on any physical model, and demonstrate a few applications in the area of ocean wave/sea ice interactions.

### Boris Baeumer

*Department of Mathematics and Statistics*

Date: Tuesday 1 August 2017

### Florian Beyer

*Department of Mathematics and Statistics*

Date: Tuesday 23 May 2017

### Chris Horvat

*Harvard University*

Date: Tuesday 2 May 2017

I'll discuss how melt ponding on sea ice floes has dramatically shifted the ecological status quo in the Arctic. Using a combination of simple modeling techniques, observations, and reanalysis products I'll demonstrate that the thinning of sea ice in the past several decades allows for extensive and frequent under-ice phytoplankton blooms, which can have a significant effect on the ecological and carbon cycle in the high latitudes.

I'll also discuss how the thermodynamic evolution of sea ice is determined through the interaction of sea ice floes and ocean eddies, and how these are determined by the floe size distribution. I'll then present a predictive model for the joint statistical distribution of floe sizes and thicknesses (FSTD) which is tested under different forcing scenarios to establish its conservation properties and demonstrate its usability in future climate studies.

Suggested reading:

Horvat, Rees Jones, Iams, Schroeder, Flocco, & Feltham (2017). The frequency and extent of sub-ice phytoplankton blooms in the Arctic Ocean.

*Science Advances*.

Horvat, Tziperman, & Campin (2016) Interaction of sea ice floe size, ocean eddies, and sea ice melting.

*Geophys. Res. Lett.*

### Jörg Hennig

*Department of Mathematics and Statistics*

Date: Tuesday 11 April 2017

### John Clark

*Department of Mathematics and Statisitcs*

Date: Tuesday 4 April 2017

$$r(sm) = (rs)m, \; (r+s)m = rm + sm,\; \text{ and }\; r(m+n) = rm + rn.$$

Note that this definition generalises that of a vector space $M$ over a field $R$.

In the special vector space setting, linear algebra introduces the important concepts of linearly (in)dependent subsets and generating subsets of a vector space $M$. The same definitions carry over to modules. A pivotal result in linear algebra says that every vector space $M$ has a ${\bf basis}$, i.e., $M$ has a linearly independent subset $B$ which also generates $M$. Moreover any two bases of $M$ have the same size. This unique size, say $n$, is called the ${\bf dimension}$ of $M$ and we write $\dim(M) = n$.

In this talk we’ll look at how the transfer of these and some related results fail for some rings.

### Maciej Floryan

*University of Western Ontario*

Date: Thursday 30 March 2017

**Note venue for this seminar**

A joint seminar by the Departments of Mathematics and Statistics, and Physics

A joint seminar by the Departments of Mathematics and Statistics, and Physics

It has been recognized since the pioneering experiments of Reynolds in 1883 that surface roughness plays a significant role in the dynamics of shear layers. This is a classical problem in fluid dynamics but, nevertheless, its resolution is still lacking. Most of the efforts have been focused on experimental approaches that have resulted in a number of correlations but have failed to uncover the mechanisms responsible for the flow response. Theoretical analyses have also failed to provide a consistent explanation of the flow dynamics. As there are an uncountable number of possible geometrical roughness forms, the problem formulation represents a logical contradiction as it might not be possible to find a general answer to a problem that has an uncountable number of variations. The recent progress towards the theoretical resolution of this apparent contradiction will be discussed and recent results dealing with the problem of distributed surface roughness will be presented. The progress has hinged on the development of the immersed boundary conditions method and the reduced geometry concept. It will be shown that it is possible to propose a rational definition of a hydraulically smooth surface by invoking flow bifurcations associated with the presence of roughness. Successful resolution of roughness problems gives access to the design of surface roughness for passive flow control where drag reduction can be achieved either directly, through re-arrangement of the form of the flow that results in the reduction of the shear stress, or indirectly, through delay of the laminar-turbulent transition.

### David Bryant

*Department of Mathematics and Statisitcs*

Date: Tuesday 21 March 2017

### Vee-Liem Saw

*Department of Mathematics and Statisitcs*

Date: Tuesday 28 February 2017

### Rajeev Rajaram

*Kent State University, Ohio*

Date: Tuesday 7 February 2017

**NOTE venue is not our usual**

In this talk, I will develop an entropy-based measure called case-based entropy which can be used to compare the diversity of distributions. The measure is based on computing the support of a

*Shannon-equivalent equi-probable*distribution. It also has the capacity to compare whole or parts of distribution in a scale-free manner. I will develop the main idea from scratch and will keep the talk accessible to graduate students and researchers alike. The utility of the measure is still being explored, but one of the latest uses that I found is its use in economics as a better method to compare income or wealth inequality than the Gini index, for example. I have also used the measure to compare the diversity of complexity in a variety of distributions from the velocities of galaxies to the energy distribution of Maxwell Boltzmann, Bose-Einstein and Fermi-Dirac distributions.

### Olaf Knapp

*University of Education Weingarten, Germany*

Date: Wednesday 25 January 2017

**Note day, time and venue of this special seminar**

3D-dynamic geometry systems (3D-DGS) with graphical user interfaces can open up a wide range of creative activities for mathematics education. Examples will be given from a research project into their use for didactic presentations, visualization, synthetic geometry, 3D modelling, morphing and mapping, design, and analogization. They allow interactive exploration of mathematical concepts. I will show their potential to become an integral part of mathematics education and to modify the curriculum. There is also the opportunity for hands-on experience with a 3D-DGS.

### Scotland Leman

*Virginia Tech, USA*

Date: Tuesday 8 November 2016

**NOTE day and time of this seminar**

In this talk I will primarily discuss the Multiset Sampler (MSS): a general ensemble based Markov Chain Monte Carlo (MCMC) method for sampling from complicated stochastic models. After which, I will briefly introduce the audience to my interactive visual analytics based research.

Proposal distributions for complex structures are essential for virtually all MCMC sampling methods. However, such proposal distributions are difficult to construct so that their probability distribution match that of the true target distribution, in turn hampering the efficiency of the overall MCMC scheme. The MSS entails sampling from an augmented distribution that has more desirable mixing properties than the original target model, while utilizing a simple independent proposal distributions that are easily tuned. I will discuss applications of the MSS for sampling from tree based models (e.g. Bayesian CART; phylogenetic models), and for general model selection, model averaging and predictive sampling.

In the final 10 minutes of the presentation I will discuss my research interests in interactive visual analytics and the Visual To Parametric Interaction (V2PI) paradigm. I'll discuss the general concepts in V2PI with an application of Multidimensional Scaling, its technical merits, and the integration of such concepts into core statistics undergraduate and graduate programs.

### Ivor Cribben

*University of Alberta*

Date: Wednesday 19 October 2016

**NOTE day and time of this seminar**

Spectral clustering is a computationally feasible and model-free method widely used in the identification of communities in networks. We introduce a data-driven method, namely Network Change Points Detection (NCPD), which detects change points in the network structure of a multivariate time series, with each component of the time series represented by a node in the network. Spectral clustering allows us to consider high dimensional time series where the number of time series is greater than the number of time points. NCPD allows for estimation of both the time of change in the network structure and the graph between each pair of change points, without prior knowledge of the number or location of the change points. Permutation and bootstrapping methods are used to perform inference on the change points. NCPD is applied to various simulated high dimensional data sets as well as to a resting state functional magnetic resonance imaging (fMRI) data set. The new methodology also allows us to identify common functional states across subjects and groups. Extensions of the method are also discussed. Finally, the method promises to offer a deep insight into the large-scale characterisations and dynamics of the brain.

### Richard Norton

*Department of Mathematics and Statistics*

Date: Tuesday 18 October 2016

I analyse the efficiency of Metropolis-Hastings algorithms with stochastic autoregressive proposals. These include many existing methods, such as the Metropolis-Adjusted Langevin Algorithm (MALA), the preconditioned Crank-Nicolson algorithm (pCN) and the Hybrid Monte Carlo algorithm (HMC). Previously, each of these algorithms required their own separate analyses. Using my analysis I can extend what is known about these algorithms as well as analysing new algorithms.

### John Tipton

*Colorado State University*

Date: Tuesday 18 October 2016

**NOTE day and time of this seminar**

Many scientific disciplines have strong traditions of developing models to approximate nature. Traditionally, statistical models have not included scientific models and have instead focused on regression methods that exploit correlation structures in data. The development of Bayesian methods has generated many examples of forward models that bridge the gap between scientific and statistical disciplines. The ability to fit forward models using Bayesian methods has generated interest in paleoclimate reconstructions, but there are many challenges in model construction and estimation that remain.

I will present two statistical reconstructions of climate variables using paleoclimate proxy data. The first example is a joint reconstruction of temperature and precipitation from tree rings using a mechanistic process model. The second reconstruction uses microbial species assemblage data to predict peat bog water table depth. I validate predictive skill using proper scoring rules in simulation experiments, providing justification for the empirical reconstruction. Results show forward models that leverage scientific knowledge can improve paleoclimate reconstruction skill and increase understanding of the latent natural processes.

### Benjamin Fitzpatrick

*Queensland University of Technology*

Date: Monday 17 October 2016

**NOTE day and time of this seminar**

When making inferences concerning the environment, ground truthed data will frequently be available as point referenced (geostatistical) observations accompanied by a rich ensemble of potentially relevant remotely sensed and in-situ observations.

Modern soil mapping is one such example characterised by the need to interpolate geostatistical observations from soil cores and the availability of data on large numbers of environmental characteristics for consideration as covariates to aid this interpolation.

In this talk I will outline my application of Least Absolute Shrinkage Selection Opperator (LASSO) regularized multiple linear regression (MLR) to build models for predicting full cover maps of soil carbon when the number of potential covariates greatly exceeds the number of observations available (the p > n or ultrahigh dimensional scenario). I will outline how I have applied LASSO regularized MLR models to data from multiple (geographic) sites and discuss investigations into treatments of site membership in models and the geographic transferability of models developed. I will also present novel visualisations of the results of ultrahigh dimensional variable selection and briefly outline some related work in ground cover classification from remotely sensed imagery.

Key references:

Fitzpatrick, B. R., Lamb, D. W., & Mengersen, K. (2016). Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study.

*PLoS ONE*, 11(9): e0162489.

Fitzpatrick, B. R., Lamb, D. W., & Mengersen, K. (2016). Assessing Site Effects and Geographic Transferability when Interpolating Point Referenced Spatial Data: A Digital Soil Mapping Case Study. https://arxiv.org/abs/1608.00086

### Chris Linsell

*College of Education*

Date: Tuesday 6 September 2016

### Miguel Moyers-Gonzalez

*University of Canterbury*

Date: Tuesday 23 August 2016

### Melissa Tacy

*Australian National University*

Date: Tuesday 23 August 2016

**Note day, time and venue of this special seminar**

Semiclassical analysis arose as a set of techniques for studying the high energy (or semiclassical) limit of quantum mechanics. These techniques have the advantage that intuition derived from the quantum-classical correspondence principle can guide our technical development. In this talk I will introduce some of the key techniques and discuss results such as the $L^{p}$ growth for products of Laplacian eigenfunctions and high energy phase space concentration estimates.

### Fabien Montiel

*Department of Mathematics and Statistics*

Date: Monday 22 August 2016

**Note day and time of this special seminar**

In a one-dimensional homogeneous medium, linear wave scattering by an array of inclusions, e.g. beads on a string, can be reduced to a multiple reflection/transmission problem, in which the reflected and transmitted waves by an inclusion become incident waves on the adjacent inclusions. Under time-harmonic conditions, fast iterative methods can be used to obtain the solution of this class of scattering problems. In a two-dimensional medium, however, such methods cannot be directly extended as there is no natural way of uniquely ordering a finite number of arbitrarily positioned inclusions, e.g. circles, in the plane. A semi-analytical method was devised to solve deterministically the scattering of time-harmonic waves by a large finite array of inclusions in two dimensions. The method consists of clustering the inclusions into adjacent parallel slabs. The solution is obtained by combining plane wave expansions of the scattered field by each slab and a fast iterative technique for slab-slab interactions similar to the one-dimensional method mentioned above.

In this talk, I will describe this so-called

*slab-clustering method*(SCM) and demonstrate how it provides a convenient framework to analyse the evolution of a multi-directional wave field through a large random array of inclusions. I will consider several applications of the methods in acoustics and water waves science. In particular, I will discuss some model predictions based on the SCM that generated key insights into the directional properties of water wave fields propagating in ice-covered oceans.

### Mark Flegg

*Monash University*

Date: Wednesday 17 August 2016

**Note day, time and venue of this special seminar**

Biological cells are the fundamental building blocks of life. At a molecular level, a cell operates according to the hard mathematical laws of physics and chemistry. Encoded in the network of molecular interactions are robust mechanisms which collectively determine the properties of life itself. Mathematical insight into cell scale behaviour is fundamentally limited by the computational scalability and convergence of mathematical frameworks that are used to describe physical systems at molecular scales (both spatial and temporal). In this presentation, I will highlight the main problems with classical mathematical approaches used to study intracellular spatio-temporal environments and present multiscale methods I have developed in the last 5 years which have allowed for improved accuracy, and efficiency. The objective of this research is to lay mathematical foundations for progress in the highly interdisciplinary mission of whole cell simulation at the level of individual molecules, a goal which has been termed a `Grand Challenge of the 21st Century'. The mathematical content of this talk is rather varied, as is the nature of applied mathematics. This research draws on partial differential equation theory, perturbation theory, N-body theory, random walks and stochastic processes as well as a number of miscellaneous areas of mathematics.

### Chris Stevens

*Department of Mathematics and Statistics*

Date: Tuesday 16 August 2016

The CFEs are a different mathematical representation of Einstein's field equations that allow one to study “infinity” of a space-time without any sort of limiting procedure. This is of interest as in general relativity infinity is the only place that energy is well defined.

In this talk, the main ideas of the CFEs will be discussed, along with the issues associated with forming an IBVP for them. A framework for the IBVP will be presented and numerical evidence of its success will be given. As an application I will discuss the problem of shooting a gravitational wave into a black hole. In particular, I will discuss how the IBVP is formulated for this situation and how to calculate the so-called "Bondi-energy" at infinity. The resulting expression is found to reproduce the famous Bondi-Sachs mass loss.

### Petru Cioica-Licht

*Department of Mathematics and Statistics*

Date: Monday 15 August 2016

**Note day and time of this special seminar**

Stochastic partial differential equations (SPDEs, for short) are mathematical models for evolutions in space and time, which are influenced by noise. They are aimed at describing phenomena in physics, chemistry, epidemiology, economics, and many other disciplines. Although we can prove existence and uniqueness of a solution to various classes of such equations, in general, we do not have an explicit representation of this solution. Thus, in order to make those models ready to use for applications, we need efficient numerical methods for approximating their solutions. And to determine the efficiency of an approximation method, we usually need to analyse the regularity of the target object, which is, in our case, the solution of the SPDE.

The aim of this talk is to present some recent results concerning the regularity of SPDEs and to point out their relevance for the question of developing efficient numerical methods for solving these equations. Before doing this, we first explain the meaning of the different parts of a typical SPDE. For simplicity, we focus on the most basic example, the stochastic heat equation driven by a (cylindrical) Wiener process. It arises from the common deterministic heat equation if we add what is called 'white noise'.

### Kay Jin Lim

*Nanyang Technological University*

Date: Wednesday 3 August 2016

This is a joint work with Susanne Danz.

**Note day, time and venue of this special seminar**

### Igor Klep

*University of Auckland*

Date: Thursday 9 June 2016

### Richard Norton

*Department of Mathematics and Statistics*

Date: Thursday 2 June 2016

### Honours and PGDip students

*Department of Mathematics and Statistics*

Date: Friday 27 May 2016

Michel de Lange :

*Deep learning*

Georgia Anderson :

*Probabilistic linear discriminant analysis*

Nick Gelling :

*Automatic differentiation in R*

15-MINUTE BREAK 2.40-2.55

MATHEMATICS

Alex Blennerhassett :

*Toeplitz algebra of a directed graph*

Zoe Luo :

*Wavelet models for evolutionary distance*

Xueyao Lu :

*Making sense of the λ-coalescent*

Terry Collins-Hawkins :

*Reactive diffusion in systems with memory*

Josh Ritchie :

*Linearisation of hyperbolic constraint equations*

**Also**

CJ Marland :

*Extending matchings of graphs: a survey*

This one mathematics project presentation takes place at 12 noon on Thursday 26 May, room 241

### Mike Hendy

Date: Thursday 12 May 2016

In this seminar I will reflect on the role that each of these 4 problems had in my own career as a researcher in mathematics and give an outline of each problem. I hope others might also see that "playing" with such problems could be useful in motivating and training future mathematics researchers.

### Petru Cioica-Licht

*Department of Mathematics and Statistics*

Date: Thursday 28 April 2016

The aim of this talk is to present some recent results concerning the regularity of SPDEs and to point out their relevance for the question of developing efficient numerical methods for solving these equations. Before doing this, we first explain the meaning of the different parts of a typical SPDE. For simplicity, we focus on the most basic example, the stochastic heat equation driven by a (cylindrical) Wiener process. It arises from the common deterministic heat equation if we add what is called `white noise'.

### Julia Gog

*University of Cambridge; NZMS Forder Lecturer*

Date: Tuesday 5 April 2016

**NOTE TIME AND VENUE**

Detailed medical insurance claims data from the US in 2009 allow us to explore the spatial dynamics of a pandemic in greater depth than ever before. This talk will outline what we observed in terms of spatial and temporal dynamics of the pandemic in the US. Modelling work allows us to test hypothesis on the importance of different factors such as whether schools were in session, climate and city population size, to see which were important in determining the dynamics of disease spread.

Here I will also show results from ongoing studies with collaborators and some of the challenges. We have very fine-grained spatial data, and clearly we would like to us this but disaggregating too far leaves us with little signal. With fitted models and a bit of mathematical creativity, we can infer likely transmission routes during the pandemic and hypothesize what the phylogeography (spatial distribution of viral variants) might look like. Finally, looking at different age groups separately reveal a little more about why the pandemic wave was so slow.

### Julia Gog

*University of Cambridge; NZMS Forder Lecturer*

Date: Monday 4 April 2016

**UNIVERSITY OF OTAGO PUBLIC LECTURE**

Mathematics is an essential tool for helping us understand and control infectious diseases, from the scale of a single virus particle through to a global pandemic. Using detailed data and the toolkit of mathematical modelling,we explore the 2009 influenza pandemic at a greater depth than was possible for any previous pandemic. The results are surprising. We know the modern world is astonishingly well connected internationally so things should spread quickly. However, influenza does not like to conform to our expectations!

### Mark Kayll

*University of Montana*

Date: Thursday 24 March 2016

### Jörg Frauendiener

Date: Wednesday 23 March 2016

**University of Otago Public Lecture**

In 1916 Einstein predicted on the basis of his new theory of general relativity that gravitational waves should exist. Since the early 1960s scientists tried to measure them but the search has been unsuccessful until very recently. On the 14th of September 2015 the two LIGO detectors measured a gravitational wave signal which could only have come from a binary black hole system. What does this measurement mean for science and for us?

### Francesc Fàbregas Flavià

*École Centrale de Nantes*

Date: Thursday 17 March 2016

Such models have been developed as specialized software, generally using Boundary Element Methods (BEM), in the framework of the theory of potential flow for the description of wave/device interaction. They are globally efficient for the optimization of one device alone or a small group of devices under simplified and rather idealized conditions.

But now as we advance towards application to real cases of multiMW farms featuring, for instance, O(100) machines, these models can no longer be used for optimization and a new generation of fast-running computer codes must be developed.

### Gordon Hiscott

*Department of Mathematics and Statistics*

Date: Thursday 3 March 2016

### Hyuck Chung

*Auckland University of Technology*

Date: Tuesday 17 November 2015

### Project presentations, Maths honours students

*Department of Mathematics and Statistics*

Date: Friday 23 October 2015

*Wavelets and direct limits*

2.25 : Pareoranga Luiten-Apirana,

*Morita equivalence of Leavitt path algebras*

2.50 : Tom McCone,

*Primitive ideals in graph algebras*

### Dimitrios Mitsotakis

*Victoria University of Wellington*

Date: Tuesday 6 October 2015

### Robert Calderbank

*Duke University*

Date: Tuesday 29 September 2015