The immune system finds very rare amounts of pathogens and responds against them in a timely and efficient manner. The time to find and respond against pathogens does not vary appreciably with the size of the host animal (scale invariant search and response). This is surprising since the search and response against pathogens is harder in larger animals.

The first part of the talk will focus on using techniques from computer science to solve problems in immunology, specifically how the immune system achieves scale invariant search and response. I use machine learning techniques, ordinary differential equation models and spatially explicit agent based models to understand the dynamics of the immune system. I will talk about Hierarchical Bayesian non-linear mixed effects models to simulate immune response in different species.

The second part of the talk will focus on taking inspiration from the immune system to solve problems in computer science. I will talk about a model that describes the optimal architecture of the immune system and then show how architectures and strategies inspired by the immune system can be used to create distributed systems with faster search and response characteristics.

I argue that techniques from computer science can be applied to the immune system and that the immune system can provide valuable inspiration for robust computing in human engineered distributed systems.

Averaging, either spatial or temporal, is a powerful technique in complex multi-scale systems.

However, in some situations it can be difficult to justify.

For example, many real-world networks in technology, engineering and biology have a function and exhibit dynamics that cannot always be adequately reproduced using network models given by the smooth dynamical systems and fixed network topology that typically result from averaging. Motivated by examples from neuroscience and engineering, we describe a model for what we call a "functional asynchronous network". The model allows for changes in network topology through decoupling of nodes and stopping and restarting of nodes, local times, adaptivity and control. Our long-term goal is to obtain an understanding of structure (why the network works) and how function is optimized (through bifurcation).

We describe a prototypical theorem that yields a functional decomposition for a large class of functional asynchronous networks. The result allows us to express the function of a dynamical network in terms of individual nodes and constituent subnetworks.

Consider the following particle system. We are given a uniform random rooted tree on vertices labelled by $[n] = \{1,2,\ldots,n\}$, with edges directed towards the root. Each node of the tree has space for a single particle (we think of them as cars). A number $m \le n$ of cars arrive one by one, and car $i$ wishes to park at node $S_i$, $1 \le i \le m$, where $S_1, S_2, \ldots, S_m$ are i.i.d. uniform random variables on $[n]$. If a car wishes to park at a space which is already occupied, it follows the unique path oriented towards the root until it encounters an empty space, in which case it parks there; if there is no empty space, it leaves the tree. Let $A_{n,m}$ denote the event that all $m$ cars find spaces in the tree. Lackner and Panholzer proved (via analytic combinatorics methods) that there is a phase transition in this model. Set $m = \lfloor \alpha n \rfloor$. Then if $\alpha \le 1/2$, $\mathbb{P}(A_{n,\lfloor \alpha n \rfloor}) \to \frac{\sqrt{1-2\alpha}}{1-\alpha}$, whereas if $\alpha > 1/2$ we have $\mathbb{P}(A_{n,\lfloor \alpha n \rfloor}) \to 0$. In this talk, we will give a probabilistic explanation for this phenomenon, and an alternative proof via the objective method.

Joint work with Christina Goldschmidt.

In Your Third Year & want to find out about opportunities for summer placements and future graduate study?

Why not visit Oxford and hear from graduate students about their research

Saturday 21 January 2017: 1-6pm

Mathematical Institute, University of Oxford

TALKS ON

• Dynamics of jumping elastic toys
• Vertex models in developmental biology
• Modelling of glass sheets
• Glimpse into the mathematics of information
• Network analysis of consumer data
• Complex singularities in jet and splash flows

Complementary Lunch & Drinks Reception - TRAVEL BURSARIES AVAILABLE (up to £50)

Please RSVP to @email

In Your Third Year & want to find out about opportunities for

summer placements and future graduate study?

Why not visit Oxford and hear from graduate students about their research

TALKS ON

Dynamics of jumping elastic toys

Vertex models in developmental biology

Modelling of glass sheets

Glimpse into the mathematics of information

Network analysis of consumer data

Complex singularities in jet and splash flows

Complementary Lunch & Drinks Reception - TRAVEL BURSARIES AVAILABLE (up to £50)

Please RSVP to @email

Direct Anonymous Attestation (DAA) is a protocol that allows a security chip embedded in a platform such as laptop to authenticate itself as a genuine chip.  Different authentications are not linkeable, thus the protocol protects the privacy of the platform. The first DAA protocol was proposed by Brickell, Chen, and Camenisch and was standardized in 2004 by the Trusted Computing Group (TCG). Implementations of this protocols were rather slow because it is based on RSA. Later, alternative and faster protocols were proposed based on elliptic curves. Recently the specification by the TCG was updated to allow for DAA protocols based on elliptic curves. Unfortunately, the new standard does not allow for provably secure DAA protocols. In this talk, we will review some of the history of DAA and  then discuss the latest protocols, security models, and finally a provably secure realization of DAA based on elliptic curves.