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<p class="MsoNormal"><span style="font-size:12.0pt">***apologies if you receive this more than once***<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt">As part of the Turing Fellow Research Projects event series taking place across the Institute’s university partner network, I’m pleased to announce the final four presentations being hosted by our Southampton
Fellows in collaboration with Newcastle University and The University of Manchester.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt">Details and registration links for all events are below.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="xmsonormal"><b><span style="font-size:14.0pt">Friday 15 October, 11:00-12:00<o:p></o:p></span></b></p>
<p class="xmsonormal"><b><span style="font-size:14.0pt">Register</span></b><span style="font-size:14.0pt">
<a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fus02web.zoom.us%2Fwebinar%2Fregister%2FWN_zZig_L9USoqZx7nhWVZkkw&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382142793%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=XAsPPoqFcHb%2BhYlsGrM%2FvLJragKX%2B%2BFU6niWZsqNTrI%3D&reserved=0">
here</a><o:p></o:p></span></p>
<p class="xmsonormal"><span style="font-size:14.0pt"><o:p> </o:p></span></p>
<p class="xmsonormal"><b><span style="font-size:12.0pt">Machine learning of seismicity induced by hydraulic fracturing</span></b><span style="font-size:12.0pt"> </span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fthomas-gernon&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382152786%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=OK4JPUOyMBAyeyXlcIelmXUezKjwHQLHSNazGwOV0vM%3D&reserved=0">Thomas
Gernon</a></span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt;color:black">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 show </span>
<o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt;color:black">how 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 </span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt;color:black">to geothermal and carbon capture and storage projects including those in the UK.<o:p></o:p></span></p>
<p class="xmsonormal"><span style="font-size:12.0pt;color:black"><o:p> </o:p></span></p>
<p class="xmsonormal"><b><span style="font-size:12.0pt">Open-source Private Data Integration</span></b><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fgeorge-konstantinidis&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382152786%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=8deY%2Fj9JRP1ojrMVPG3auhU1CJTcML7pF3GByoWk0aE%3D&reserved=0">George
Konstantinidis</a></span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt">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 </span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt">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 </span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt">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</span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt">on real and synthetic datasets and discuss extensions ranging from blockchains to clinical research, to AI reasoning and Knowledge Graphs.</span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt"> </span><o:p></o:p></p>
<p class="xmsonormal"><b><span style="font-size:14.0pt">Monday 18 October, 10:30-12:00<o:p></o:p></span></b></p>
<p class="xmsonormal"><b><i><span style="font-size:14.0pt">Joint with Newcastle University<o:p></o:p></span></i></b></p>
<p class="xmsonormal"><b><span style="font-size:14.0pt">Register <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fus02web.zoom.us%2Fwebinar%2Fregister%2FWN_yLHXVa2TQe-uyaMcFHQnEQ&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382162778%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=jn7muObk7lg0fGTkRcem5f565dS%2F1SdCjDWb2gWE9mw%3D&reserved=0">
here</a><o:p></o:p></span></b></p>
<p class="xmsonormal"><b><span style="font-size:12.0pt"><o:p> </o:p></span></b></p>
<p class="xmsonormal"><b><span style="font-size:12.0pt">Mapping biology from mouse to man using transfer learning</span></b><span style="font-size:12.0pt"> </span><o:p></o:p></p>
<p class="xxmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fben-macarthur&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382162778%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=lHxWY6%2BJ4C6otzBRLtWrEgq%2BWDt7EOm4BgEW0YA%2F4tc%3D&reserved=0">Ben
MacArthur</a></span><o:p></o:p></p>
<p class="xxmsonormal"><span style="font-size:12.0pt">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. </span><o:p></o:p></p>
<p class="xxmsonormal"><span style="font-size:12.0pt">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.</span><o:p></o:p></p>
<p class="xxmsonormal" style="margin-left:36.0pt;text-indent:36.0pt"><span style="font-size:12.0pt"> </span><o:p></o:p></p>
<p class="xxmsonormal"><b><span style="font-size:12.0pt">Decision support algorithms for Emergency Departments</span></b><span style="font-size:12.0pt;color:black"><o:p></o:p></span></p>
<p class="xxmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fneil-white&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382172776%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=66p8mT7xfziSKe0Qm7GzQjVokQxcUtMn8OgnJQaoxLA%3D&reserved=0">Neil
White</a></span><o:p></o:p></p>
<p class="xxmsonormal"><span style="color:black">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</span><o:p></o:p></p>
<p class="xmsonormal"><span style="color:black">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. </span><o:p></o:p></p>
<p class="xmsonormal"><span style="color:black">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. </span><span style="font-size:12.0pt;color:black"> </span><span style="color:black">We demonstrate how explainable machine learning can be used
</span><o:p></o:p></p>
<p class="xmsonormal"><span style="color:black">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 </span><o:p></o:p></p>
<p class="xmsonormal"><span style="color:black">relative importance of model features.</span><o:p></o:p></p>
<p class="xxmsonormal"><span style="font-size:12.0pt"> </span><o:p></o:p></p>
<p class="xxmsonormal"><b><span style="font-size:12.0pt">4P Healthcare</span></b><o:p></o:p></p>
<p class="xxmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fpaolo-missier&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382172776%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=pt230tHXaTEnGiaDHm8A7YunbW5pF3F5XYhxq7JWU7U%3D&reserved=0">Paolo
Missier</a>, Newcastle University</span><o:p></o:p></p>
<p class="xxmsonormal"><span style="font-size:12.0pt">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. </span><o:p></o:p></p>
<p class="xxmsonormal"><span style="font-size:12.0pt">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 </span><o:p></o:p></p>
<p class="xxmsonormal"><span style="font-size:12.0pt">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.</span><o:p></o:p></p>
<p class="xxmsonormal" style="margin-left:72.0pt"><span style="color:black"> </span><o:p></o:p></p>
<p class="xmsonormal"><b><span style="font-size:14.0pt">Wednesday 3 November, 13:15-14:30<o:p></o:p></span></b></p>
<p class="xmsonormal"><b><span style="font-size:14.0pt">Register <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fus02web.zoom.us%2Fwebinar%2Fregister%2FWN_XeILjhwERlGOKKaHc9TI_w&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382182768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=efgcz79zfV2BuUS1%2B%2B%2BW35sKROLr4WwY0mWS90aIGYU%3D&reserved=0">
here</a><o:p></o:p></span></b></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="xmsonormal"><b><span style="font-size:12.0pt">Data science approaches to applied mathematical modelling</span></b><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fmarika-taylor&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382182768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=xZ3cyygsGJVNkpqT0Zh5iCx5cjeW9tsXqJLjKHDo0D8%3D&reserved=0">Marika
Taylor</a></span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt">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 </span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt">from fundamental physics to understanding the geometry underlying quantum machine learning.<o:p></o:p></span></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="xmsonormal"><b><span style="font-size:12.0pt">Jazz as Social Machine</span></b><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fthomas-irvine&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382192764%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=chIX%2FEXe0ux0cONf8e9vjTc8q3SJ5qkd7QqPJe2L2GM%3D&reserved=0">Thomas
Irvine</a></span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt;color:black">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 </span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt;color:black">machine learning jazz more of a "social" problem than other challenges in the growing field of Music Information Retrieval.</span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt"> </span><o:p></o:p></p>
<p class="xmsonormal"><b><span style="font-size:14.0pt">Tuesday 23 November, 14:00-15:30<o:p></o:p></span></b></p>
<p class="xmsonormal"><b><i><span style="font-size:14.0pt">Joint with The University of Manchester<o:p></o:p></span></i></b></p>
<p class="xmsonormal"><b><span style="font-size:14.0pt">Register <a href="https://teams.microsoft.com/registration/-XhTSvQpPk2-iWadA62p2A,1bh6qC4isUeT-gmiMP7McA,tfrNasdn50-4SBzv6YT4tw,bim-TalsAUSNJvHouy6iYg,zLVSjJcH4Eqt2ilJ3rgl2g,u3tnKBmtu0auqx8zwV8YFQ?mode=read&tenantId=4a5378f9-29f4-4d3e-be89-669d03ada9d8">
here</a><o:p></o:p></span></b></p>
<p class="xmsonormal"><b><span style="font-size:14.0pt"><o:p> </o:p></span></b></p>
<p class="xmsonormal"><b><span style="font-size:12.0pt">A Multidisciplinary Study of Predictive Artificial Intelligence Technologies in the Criminal Justice System</span></b><span style="font-size:12.0pt"> </span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fpamela-ugwudike&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382192764%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=vpfUZyyUYa3X5ff2tSTLDsBfTwBrtf6NDdBGoX6qamY%3D&reserved=0">Pamela
Ugwudike</a></span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:12.0pt;color:black">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</span><span class="xapple-converted-space"><span style="color:black"> </span></span><span style="font-size:12.0pt;color:black">of
the computational model. The study found evidence of self-reinforcing properties</span><span style="font-size:12.0pt;mso-fareast-language:EN-GB"><o:p></o:p></span></p>
<p class="xmsonormal"><span style="font-size:12.0pt"> <o:p></o:p></span></p>
<p class="xmsonormal"><b><span style="font-size:12.0pt">Topology and neural networks generalisations<o:p></o:p></span></b></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fjacek-brodzki&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382202756%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=0prjcGHgddHhMg7WBF%2BOud4Dhf9%2F1uyw4H6s%2F%2BhY4lM%3D&reserved=0">Jacek
Brodzki</a></span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt">Neural 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.<o:p></o:p></span></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="xmsonormal"><b><span style="font-size:12.0pt">Anonymisation and Provenance: Expression Data Environments With PROV</span></b><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size:12.0pt"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpeople%2Fresearchers%2Fadriane-chapman&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382202756%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=a2PKB4RQ9VHyvqInH97F4BXMcFykm2r5yRE1hYcFD3g%3D&reserved=0">Adriane
Chapman</a> and <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.research.manchester.ac.uk%2Fportal%2Fmark.elliot.html&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382212751%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=52BSVTV4rgWPPep9AUYF6dITJybRV7xwuEEO1a8J65E%3D&reserved=0">
Mark Elliot</a>, The University of Manchester</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:12.0pt">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.<o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:12.0pt"><o:p> </o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:14.0pt">Background<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-size:12.0pt">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 artificial intelligence. The Institute and its university partners are delighted to host the events which will showcase the breadth of research and demonstrate the impact of these research projects. Events will be added to the website over
the coming weeks, visit regularly for more information.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt">Details of <b>all</b> university partner presentations and how to register can be found
<a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fpresenting-turing-fellow-research-projects&data=04%7C01%7Csdd1%40soton.ac.uk%7C7db7722952644dae64a908d988d4b247%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637691269382212751%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=5K7o%2F2ASA10FaageJLm1AVlFJFOtYL2%2BVDKs8qgZib4%3D&reserved=0">
here</a> – we look forward to seeing you there.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt">Please feel free to disseminate more widely.
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt">Best wishes<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt">Susan<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;mso-fareast-language:EN-GB">_________________________________<o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:12.0pt;mso-fareast-language:EN-GB">Susan Davies<o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-size:12.0pt;mso-fareast-language:EN-GB">Coordination Manager,
<a href="https://www.southampton.ac.uk/wsi/index.page?">Web Science Institute</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;mso-fareast-language:EN-GB">University Liaison Manager,
<a href="https://www.southampton.ac.uk/wsi/alan-turing-institute/alan-turing-institute.page">
The Alan Turing Institute</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;mso-fareast-language:EN-GB">Web Science Institute<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;mso-fareast-language:EN-GB">University of Southampton<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;mso-fareast-language:EN-GB">Southampton SO17 1BJ<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;mso-fareast-language:EN-GB">M 07768 266464<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;mso-fareast-language:EN-GB"><a href="https://www.southampton.ac.uk/wsi/index.page">https://www.southampton.ac.uk/wsi</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><i><span style="font-size:12.0pt;mso-fareast-language:EN-GB">My working days are Monday to Thursday.<o:p></o:p></span></i></b></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="mso-fareast-language:EN-GB"><o:p> </o:p></span></p>
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