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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.

Current term

Second term, 2019-2020:

16 January 2020

14:00 - 15:00

MSB2.22

Andreas Vlachos (Cambridge)

Title: Automated Fact Checking: a natural language processing perspective

Abstract:
Fact checking is the task of verifying a claim against sources such as knowledge bases and text collections. While this task has been of great importance for journalism, it has recently become of interest to the general public as it is one of the weapons against misinformation. In this talk, I will first discuss the task and what should be the expectations from automated methods for it. Following this, I will present our approach for fact checking simple numerical statements which we were able to learn without explicitly labelled data. Then I will describe how we automated part of the manual process of the debunking website emergent.info, which later evolved into the Fake News Challenge with 50 participants. Finally, I will present the Fact Extraction and Verification shared task, which took place in 2018, as well as the recent second edition which conducted an adversarial evaluation of the proposed approaches.

4 February 2020

10:00 - 11:00
Unusual room: R0.21

Hanan Samet (University of Maryland) -- ACM Distinguished Speaker

Title: Reading News with Maps by Exploiting Spatial Synonyms

Abstract:
NewsStand is an example application of a general framework to enable people to search for information using a map query interface, where the information results from monitoring the output of over 10,000 RSS news sources and is available for retrieval within minutes of publication. The advantage of doing so is that a map, coupled with an ability to vary the zoom level at which it is viewed, provides an inherent granularity to the search process that facilitates an approximate search. This distinguishes it from today's prevalent keyword-based conventional search methods that provide a very limited facility for approximate searches and which are realized primarily by permitting a match via use of a subset of the keywords. However, it is often the case that users do not have a firm grasp of which keyword to use, and thus would welcome the search to also take synonyms into account. For queries to spatially-referenced data, the map query interface is a step in this direction as the act of pointing at a location (e.g., by the appropriate positioning of a pointing device) and making the interpretation of the precision of this positioning specification dependent on the zoom level is equivalent to permitting the use of spatial synonyms (i.e., letting spatial proximity play a role rather than only seeking an exact match of a query string). Of course, this is all predicated on the use of a textual specification of locations rather than a geometric one, which means that one must deal with the potential for ambiguity.

6 February 2020
14:00 - 15:00

Jose Such (King's College)

Title: TBD
Abstract: TBD

27 February 2020
14:00 - 15:00

Carl Henrik Ek (Bristol)

Title: TBD
Abstract: TBD

5 March 2020
14:00 - 15:00

Gunnar Carlsson (Stanford)

Title: TBD
Abstract: TBD

12 March 2020
14:00 - 15:00

Tugkan Batu (LSE)

Title: TBD
Abstract: TBD

Past terms

First term, 2019-2020:

10 October 2019

14:00 - 15:00

Sunil Prabhakar (Purdue University)
Title: Database Fidelity Without Trust

Abstract:
Ensuring the trustworthiness of data retrieved from a database is of utmost importance to users. Databases are typically subjected to updates from multiple clients, often at very high rates. The correctness of data stored in a database is defined by the faithful execution of only valid (authorized) transactions. In this talk we address the question of whether it is necessary to trust a database server in order to trust the data retrieved from it? The lack of trust arises naturally if the database server is owned by a third party, as in the case of cloud computing, or if it is likely that the server may have been compromised, or there is a malicious insider.

24 October 2019

14:00 - 15:00

Evangelia Kalyvianaki (Cambridge)

Title: THEMIS -- Fairness in Federated Stream Processing under Overload

Abstract:
Federated stream processing systems, which utilise nodes from multiple
independent domains, can be found increasingly in multi-provider cloud
deployments, internet-of-things systems, collaborative sensing
applications and large-scale grid systems. To pool resources from several
sites and take advantage of local processing, submitted queries are split
into query fragments, which are executed collaboratively by different
sites. When supporting many concurrent users, however, queries may exhaust
available processing resources, thus requiring constant load shedding.
Given that individual sites have autonomy over how they allocate query
fragments on their nodes, it is an open challenge how to ensure global
fairness on processing quality experienced by queries in a federated
scenario.

7 November 2019
14:00 - 15:00
Ata Kaban (Birmingham)

Title: Uncovering structure with randomness for learning in high dimensions

Abstract:
Random projection (RP) is a simple, computationally efficient linear
dimensionality reduction technique with interesting theoretical
properties for learning from data. For instance, it can regularise
ill-conditioned covariance matrices, and it preserves dot products
between vectors and their signs to an extent that depends on their
cosine similarity. It can speed up computations when dealing with high
dimensional data sets, while retaining control of generalisation
performance. Furthermore, in this talk we discuss how RP can also be
used as an analytic tool to better understand why learning from high
dimensional data is not always as difficult as it looks. We present
generalisation bounds where the use of RP highlights how learning can
take advantage of the presence of certain benign geometric structures
in the problem, and yields conditions that reduce
dimensional-dependence of error-guarantees in settings where such
dependence is known to be essential in general.

21 November 2019
14:00 - 15:00
Samson Abramsky (Oxford)

Title: Non-classicality, quantum resources and quantum advantage (slides)

Abstract:
A key objective in the field of quantum information and computation is to understand the advantage which can be gained in information processing tasks by the use of quantum resources. While a range of examples has been studied, to date a systematic understanding of quantum advantage is lacking.

Our focus here is on quantum resources which take the form of non-local, or more generally \emph{contextual}, correlations. Contextuality is one of the key signatures of non-classicality in quantum mechanics and has been shown to be a necessary ingredient for quantum advantage in a range of information processing tasks. We will describe a notion of simulation between quantum resources, and more generally between resources described in terms of contextual correlations, in the ``sheaf-theoretic'' framework for contextuality (Abramsky-Brandenburger).
The notion of simulation is expressed as a morphism of empirical models, in a form which allows the behaviour of one set of correlations to be simulated in terms of another using classical processing and shared randomization.

The talk will be essentially self-contained, introducing the various notions we will discuss.

3 December 2019
15:00 - 16:00
Jakob Nikolas Kather (University Hospital Aachen)

Title: Deep Learning for Precision Oncology

Abstract:
Precision oncology requires molecular and genetic testing of tumour tissue. For many tests, widespread implementation into clinical practise is limited because these biomarkers are costly, require significant expertise and are limited by tissue availability. However, virtually every cancer patient gets a biopsy as part of the diagnostic workup and this tissue is routinely stained with hematoxylin and eosin (HE). We have developed a deep learning-based technology to predict molecular features, prognosis and markers for treatment response directly from routine histology. This talk will summarize the state of the art in deep learning histopathology, demonstrate emerging use cases and discuss the clinical implications of deep learning-based molecular testing of solid tumours.

Third term, 2018-2019:

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.