Skip to main content Skip to navigation

Computer Science Colloquium

Organiser: Tom Gur

Colloquia take place fortnightly on Thursdays 14:00-15:00, Lecture Room CS1.01. They are directed towards a general computer science audience. After the talk, we convene in the staff common room for coffee/tea and informal discussions with the speaker.

Additional colloquia, titles and abstracts will be added as details become available.

Third term, 2019:

Thu 2 May, '19

14:00 - 15:00

Sarah Meiklejohn (UCL)
Title: Anonymity in Cryptocurrencies
(This talk will take place in CS1.04)
Abstract: A long line of recent research has demonstrated that existing cryptocurrencies often do not achieve the level of anonymity that users might expect they do, while at the same time another line of research has worked to increase the level of anonymity by adding new features to existing cryptocurrencies or creating entirely new cryptocurrencies. This talk will explore both of these lines of research, demonstrating both de-anonymization attacks and techniques for anonymity that achieve provably secure guarantees.
Thu 9 May, '19
14:00 - 15:00
Edith Elkind (Oxford)

Title: Fair Division of a Graph

Abstract: We consider fair allocation of indivisible items under an additional constraint: there is an undirected graph describing the relationship between the items, and each agent's share must form a connected subgraph of this graph. This framework captures, e.g., fair allocation of land plots, where the graph describes the accessibility relation among the plots. We focus on agents that have additive utilities for the items, and consider several common fair division solution concepts, such as proportionality, envy-freeness and maximin share guarantee.In particular, we prove that for acyclic graphs a maximin share allocation always exists and can be computed efficiently. We also discuss a continuous variant of this problem, where agents need to divide a collection of interconnected "cakes" and each agent's piece has to be connected, or, more generally, have a small number of connected components.

Based on joint work with Sylvain Bouveret, Katarina Cehlarova, Ayumi Igarashi, Dominik Peters, Xiaohui Bei and Warut Suksompong

Thu 6 Jun, '19

14:00 - 15:00

Andreas Damianou (Amazon, Cambridge)

(This talk will take place in CS1.04)

Title: Probability and uncertainty in Deep Learning

Abstract:
In this talk I motivate deep learning from the perspective of probability and uncertainty. The resulting models can maintain and, crucially, communicate their degree of belief regarding their states (e.g. how certain they are in their predictions) using Bayesian inference. I will also highlight the deep Gaussian process family of approaches, which can be seen as a means of performing probabilistic, Bayesian deep learning directly in the space of functions, rather than in the space of finite parameters. Prior knowledge of deep learning is not essential to follow this talk, in fact I will offer a quick overview in the beginning.

Thu 20 Jun, '19
14:00 - 15:00
Mario Berta (Imperial College London)

Title: Quantum Technologies for Cryptography

Abstract:
The rise of quantum technologies opens exciting possibilities for performing cryptographic tasks, but it also poses new threats. On the one hand, quantum mechanical devices are the basis of a variety of novel cryptographic protocols like key distribution or secure identification, which offer a significant advantage over standard routines. On the other hand, quantum computers will render some existing cryptographic schemes insecure. The interplay between these two aspects is best understood by analysing the power of adversaries which have access to a quantum computer - quantum adversaries. In my talk, I will discuss methods to analyse the power of quantum adversaries which shine light on both aspects.

Thu 11 Jul, '19

11:00 - 12:00

Radu Calinescu (University of York)

Title: Learning, synthesis and efficient analysis of probabilistic models

Abstract:
Probabilistic modelling is a powerful tool for establishing performance, dependability and other quantitative properties of systems and processes during design, verification and at runtime. However, the usefulness of this tool depends on the accuracy of the models being analysed, on the efficiency of the analysis, and on the ability to find models corresponding to effective system and process architectures and configurations. This talk will describe how recent approaches to probabilistic model learning, analysis and synthesis address major challenges posed by these prerequisites, significantly advancing the applicability of probabilistic modelling.