Presenting the Turing Fellow Research Projects Event

Date:: 21 April 2021 - 23 November 2021
Venue:: Online

Event details

The Alan Turing Institute and its university partners are pleased to invite you to ‘Presenting the Turing Fellow Research Projects’, an event series taking place across the Institute’s university partner network between April – November 2021.

About

In 2018 over 300 Turing Fellows were appointed at the Institute following an open call. Some of these received additional funding to deliver research projects that have had substantial impact in the areas of data science and AI. These events will demonstrate the breadth of research and impact of these projects.

At each event, Turing Fellows will:

introduce their Turing Fellow research project and share details of project collaborators
highlight project outputs and demonstrate the successes and impact of the project
outline next steps for the project

Each event, coordinated at each Turing university partner by the respective university liaison team, will consist of 2-3 presentations followed by a Q&A session.

Find out more about presentations by Southampton Turing Fellows below.

For further information and to view the full list of Turing presentations, please visit the Turing event page.

Tuesday 23 November, 14:00-15:30 - Registration open

Register now!
14:00-14:05	Introduction by Peter Smith, Turing University Lead
14:05-14:30	Presentation by Pamela Ugwudike
	A Multidisciplinary Study of Predictive Artificial Intelligence Technologies in the Criminal Justice System
	The project explored a classic predictive policing algorithm to investigate conduits of bias. Whilst many studies on real data have shown that predictive policing algorithms can create biased feedback loops, few studies have systematically explored whether this is the result of legacy data, or the algorithmic model itself. To advance the empirical literature, this project designed a framework for testing predictive models for biases. With the framework, the project created and tested: (1) a computational model that replicates the published version of a predictive policing algorithm, and (2) statistically representative, biased and unbiased synthetic crime datasets, which were used to run large-scale tests of the computational model. The study found evidence of self-reinforcing properties
14:30-14:55	Presentation Jacek Brodzki
	Topology and neural networks generalisations
	DNeural networks are at the centre of many remarkable applications of AI. These powerful classification tools are great when they work well, but have demonstrated weaknesses where they fail at surprisingly easy tasks. This talk will summarise the results of our pilot project devoted to the study of the geometry of the decision boundaries of neural networks as a predictor for their performance.
14:55-15:20	Presentation by Adriane Chapman (University of Southampton) and Mark Elliot (University of Manchester):
	Anonymisation and Provenance: Expression Data Environments With PROV
	The Anonymisation Decision-Making Framework (ADF) is a comprehensive practice designed for assessing and controlling the risks of sharing and disseminating data. This project examines how to use provenance to support anonymization decision-making. To enable this, we analyze the mapping of concepts between ADF and prov. We have operationalized provenance into the framework, and analyse the suitability via real use cases. We have created prototype tool support from simulators to reasoners.
15:20-15:30	Closing remarks by Peter Smith

Wednesday 3 November, 13:15-15:00 - Registration closed

13:15-13:20	Introduction by Peter Smith, Turing University Lead
13:20-13:50	Presentation by Marika Taylor
	Data science approaches to applied mathematical modelling
	In this talk Marika Taylor will describe new relationships between tessellations and codes used for quantum error correction, focussing on tessellations of negatively curved (hyperbolic) spaces. The motivations for constructing such codes will be explored - these range from fundamental physics to understanding the geometry underlying quantum machine learning.
13:50-14:20	Presentation by Thomas Irvine
	Jazz as Social Machine
	Making jazz with machine learning agents turns out to be complicated. Using insights from Web Science, Science and Technology Studies and musicological jazz studies, I survey the techniques currently in use, and explore what it is about jazz's data that makes machine learning jazz more of a "social" problem than other challenges in the growing field of Music Information Retrieval.
14:20-14:30	Closing remarks by Peter Smith
These presentations will be aligned with the Maths Seminar Series.

Friday 15 October, 11:00-12:00 - Registration closed

11:00-11:05	Introduction by Peter Smith, Turing University Lead
11:05-11:30	Presentation by Thomas Gernon
	Machine learning of seismicity induced by hydraulic fracturing
	In this talk, Tom Gernon will describe how machine learning can be applied to forecast earthquakes triggered by underground fluid injection, and thereby improve real-time regulation practices in fracking and wastewater disposal regions. As an example, he will showhow Bayesian networks can be used to model joint conditional dependencies between both natural (e.g. geology, seismicity) and operational (e.g. injection volumes, rates, and depth) parameters. This approach is key to unlocking spatial complexity and is applicable to geothermal and carbon capture and storage projects including those in the UK
11:30-11:55	Presentation by George Konstantinidis
	Open-source Private Data Integration
	In this talk George is going to present the latest developments on the new area of collaborative data privacy. In these scenarios the service provider is considered a friend and not an adversary to the data owner, thus privacy enforcement is collaborative and does not rely on encryption or distortion of data. Instead, this area investigates and develops mechanisms for users to encode their custom requirements, data consent, privacy preferences and data policies in a machine-processable language to form data usage contracts that can be automatically (or algorithmically) respected. George will discuss the formal foundations of the area, connections to data privacy, algorithms and open source implementations for supporting these automated agreements in data management. He will present results on real and synthetic datasets and discuss extensions ranging from blockchains to clinical research, to AI reasoning and Knowledge Graphs.
11:55-12:05	Closing remarks by Peter Smith

Monday 18 October, 10:30-12:00 - Registration closed

10:30-10:35	Introduction by Peter Smith, Turing University Lead
10:35-11:00	Presentation by Ben MacArthur, University of Southampton
	Mapping biology from mouse to human using transfer learning
	In this talk Ben MacArthur will outline how tools from machine learning can be combined with experiments to better understand how biology can be mapped between species and thereby improve the biomedical research and development pipeline. As an example, he will show how transfer learning can be used to determine when biology learnt from one organism (the mouse) can be effectively transferred be to another (the human) and when it cannot.
11:00-11:25	Presentation by Neil White, University of Southampton
	Decision support algorithms for Emergency Departments
	In this talk, Neil White and Chris Duckworth will describe the outcomes of the TriagED project. Emergency departments (EDs) are facing unprecedented levels of overcrowding, which delays and impacts patient care. By analysing data collected from EDs, we can use machine learning models to predict patient outcomes (e.g. whether a patient was discharged or admitted to hospital). These models will predict the patient outcome as early as possible in the hospital visit, with an aim to improve the efficiency of EDs and help allocate resources in downstream care. Clinical settings are, however, dynamic environments and the reasons for attending the ED and their severity can change with time (i.e. data drift). This can have serious ramifications for any machine learning model implemented. We demonstrate how explainable machine learning can be used to monitor data drift for a predictive model deployed within a hospital ED. We use the COVID-19 pandemic as an extreme case of data drift, which has brought a severe change in operational circumstances. Furthermore we show how emergent health risks can be identified by using the relative importance of model features.
11:25-11:50	Presentation by Paolo Missier, Newcastle University
	4P Healthcare
	Our project at Newcastle University sets out to investigate how self-monitoring using wearable devices may help detect the early onset of metabolic diseases, potentially leading to early interventions to benefit both individuals and the health care system. Focusing on Type 2 Diabetes (T2D) and on wrist-worn accelerometery traces of physical activity, in this talk we will cover two angles of this research. Firstly, we show that suitable features can be either engineered, or learnt from the raw traces using autoencoders, and that such features can in fact be used to discriminate T2D patients from healthy controls. We have further validated the representation learning approach on a second dataset of T2D patients, provided by the DIRECT IMI consortium. Motivated by the scarcity of high-quality traces associated with metabolic conditions such as T2D, we then explored the idea of generating synthetic traces and in fact to simulate "a day in life of a virtual T2D patient", by learning generative models from the available traces. We report on promising initial results and suggest further research in this area.
11:50-12:00	Closing remarks by Peter Smith