Year: 2019

Our latest series of blogs introduces you to our University of Bristol’s Turing Fellows, where the Jean Golding Institute have been speaking to some of the thirty Alan Turing Institute Fellows to find out a little more about their work and research interests.

Last time we spoke to Genevieve Liveley, Reader in Classics and Turing Fellow who spoke to us about her work in narratology and the ancient and future (hi)stories of AI and robots – you can read more about her on our previous JGI blog.

Next, we spoke to Philip Hamann, NIHR Clinical Lecturer in Rheumatology and Turing Fellow at the University of Bristol.

JGI: What are your main research interests? 

My research interests centre on developing artificial intelligence (AI) methods to monitor and manage individuals with rheumatoid arthritis remotely using remotely captured data from smartphones and wearables. 

Can you give a brief background of your experience?

I am an academic rheumatologist with over 8 years specialist rheumatology experience. I completed my PhD working on data from the British Society for Rheumatology Biologics Register. This is the worlds largest longitudinal database of patients with rheumatoid arthritis who take some of the new high-cost drugs we use to treat rheumatoid arthritis. I examined the data to see if there were any associations between the demographics of patients and how they responded to these drugs over time. I used latent class trajectory modelling and Bayesian methodology to plot the outcomes of patients over time. Whilst undertaking my PhD, I conceptualised and developed an award-winning smartphone app and cloud-based software in collaboration with industry partners which allows patients to securely record and report rheumatoid arthritis disease activity using validated patient reported outcomes which is now in clinical use in the NHS. I am now working on a project to use the app to try to predict flares remotely and optimise when patients are seen in clinic. 

What are the big issues related to data science / data-intensive research in your area? 

The really big issues in health data science research are around data security, patient confidentiality, ethical algorithms, safety and reproducibility of results.  When working with patient sensitive data that influences clinical care, findings must be safe, fair and reliable. The trust in the doctor-patient relationship is central to being able to deliver best care. Therefore, any research and industry partnership needs to adhere to the highest research and ethical standards and ensure potential issues are identified at the beginning and addressed. Transposing these core values and standards into research using novel remote monitoring services that involve cloud computing and AI, presents new challenges that need to be understood and tackled up-front, which is something we’ve been working on from day one.

Can you tell us of one recent publication in the world of data science or data-intensive research that has interested you? 

A recent and important paper published in Sensors from the end of 2017 is of great interest. Developing Fine-Grained Actigraphies for Rheumatoid Arthritis Patients from a Single Accelerometer Using Machine Learning. Sensors (Basel). 2017 Sep;17(9):2113–22. Andreu-Perez J et al. 

In this study, the authors describe specific activity traces that are able to identify, with a high level of accuracy, different day-to-day activities being undertaken by people with rheumatoid arthritis and differentiate these from traces from health volunteers using a single accelerometer. These findings represent a significant proof-of-concept of how remote monitoring with wearables could be used for patients with RA.

How interdisciplinary is your research? 

As with most research in healthcare, my research is very interdisciplinary and involves working with clinical staff and patients who use the app in the NHS, academics who are working with me to develop our next research study and industrial partners who build, develop and maintain the app. One thing I’ve learnt when working across disciplines and with patients is the importance of getting feedback from users of the app and avoiding jargon, making sure everyone understands everyone else, and understanding the differing objectives of different members of the team. An essential rule in multidisciplinary teams is to pause, recap and check everyone understands before proceeding to avoid difficulties later. 

What’s next in your field of research? 

Developments in healthcare are at a really exciting point right now. Wearable technology is becoming mainstream, smartphones are now more powerful than computers from just 2-3 years ago (and are affordable and widely used) and mobile data infrastructure is now robust, covering the vast majority of the UK. The tremendous advances in AI methodology means that the data collected from this widely available hardware can be synthesised in a manageable way, bringing with it the possibilities of remote sensing and personalised real-time adaptive healthcare. Taken together with the open approach to innovation being championed in the NHS, the possibilities for the new models of care to make a difference to patients may not be that far off. 

If anyone would like to get in touch to talk to you about collaborations / shared interests, how can they get in touch? 

Email is best: Philip.Hamann@bristol.ac.uk 

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More about The Turing Fellows 

Thirty fellowships and twelve projects have been awarded to Bristol as part of the University partnership with the Turing. This fellowship scheme allows university academics to develop collaborations with Turing partners. The Fellowships span many fields including key Turing interests in urban analytics, defence and health. 

Take a look at the Jean Golding Institute website for a full list of University of Bristol Turing Fellows. 

The Alan Turing Institute 

The Alan Turing Institute’s goals are to undertake world-class research in data science and artificial intelligence, apply its research to real-world problems, drive economic impact and societal good, lead the training of a new generation of scientists and shape the public conversation around data. 

Find out more about The Alan Turing Institute. 

Continuing our blog series, ‘Introducing the University of Bristol’s Turing Fellows,’ the Jean Golding Institute have been speaking to several of the the thirty University of Bristol Alan Turing Institute Fellows in order to find out a bit more about their work and research interests.

You can take a look at our last interview with Levi John Wolf, Lecturer in Quantitative Human Geography, about his work developing new mathematical models and algorithms in applications across field of quantitative geography on the JGI Blog.

Next up in the series, we spoke to Genevieve Liveley, Reader in Classics and Turing Fellow, about her work in narratology and the ancient and future (hi)stories of AI and robots.

JGI: What are your main research interests? 

My research and teaching centres upon narratologically inflected studies of the ancient world and its modern reception. My most recent book, Narratology (OUP) exposes the dynamic (mis)appropriation of ancient scripts that gives modern narratology its shape. My new research, on the ancient and future (hi)stories of AI and robots, builds on this work, and seeks a better understanding of the story frames, schemata, and scripts that programme cultural narratives about human interaction with artificial humans, automata, and AI – from across the last 3000 years. 

JGI: Can you give a brief background of your experience? 

I completed my PhD in Classics (on chaos theory and ancient narrative) here at Bristol and, after a post-doc at Berkeley and a year lecturing in Reading, was lucky enough to get a permanent post back here. 

JGI: What are the big issues related to data science / data-intensive research in your area? 

Preliminary research indicates that public attitudes to AI in society are coded by their experience of AI in fiction. So, a better understanding of the narrative dynamics shaping such coding – that is, the narrative scripts and frames that programme human responses to AI – is essential. Not least of all to help us better understand public discourse and private fears around risk and opportunity in AI. As recent controversies over immunization show, the best data can be ‘trumped’ by a single story and significant harms ensue. 

JGI: Can you tell us of one recent publication in the world of data science or data-intensive research that has interested you? 

The Royal Society Report on ‘AI narratives: portrayals and perceptions of artificial intelligence and why they matter.’

JGI: How interdisciplinary is your research? 

Very! I’m a narratologist (working on the science of stories) based in the department of Classics and Ancient History. 

JGI: What’s next in your field of research? 

Bringing theory and praxis together to develop some practical tools for government and other agencies to use in assessing and anticipating the future risks of AI innovation. 

JGI: If anyone would like to get in touch to talk to you about collaborations / shared interests, how can they get in touch? 

Email me at g.liveley@bristol.ac.uk.

JGI: Are there any events coming up that you would like to tell us about? 

In April I’ll be speaking on AI narratives and ethics at the CYBERUK 2019 conference (24 –25 April 2019): this is the UK government’s flagship cyber security event, hosted by the National Cyber Security Centre (NCSC). 

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More about The Turing Fellows 

Thirty fellowships and twelve projects have been awarded to Bristol as part of the University partnership with the Turing. This fellowship scheme allows university academics to develop collaborations with Turing partners. The Fellowships span many fields including key Turing interests in urban analytics, defence and health. 

Take a look at the Jean Golding Institute website for a full list of University of Bristol Turing Fellows. 

The Alan Turing Institute 

The Alan Turing Institute’s goals are to undertake world-class research in data science and artificial intelligence, apply its research to real-world problems, drive economic impact and societal good, lead the training of a new generation of scientists and shape the public conversation around data. 

Find out more about The Alan Turing Institute. 

In our blog series ‘Introducing the University of Bristol’s Turing Fellows’, we have been finding out more about the thirty academics at the University of Bristol who have recently become Alan Turing Institute Fellows through a series of interviews. 

You can find the first blog of our series, featuring Iván Palomares Carrascosa, as well as an interview with Paul Wilcox on the JGI Blog.

Next up, the Jean Golding Institute (JGI) spoke to Levi John Wolf, Lecturer in Quantitative Human Geography and Turing Fellow, about his work developing new mathematical models and algorithms in applications across a broad range of problems within the field of quantitative geography.

JGI: What are your main research interests?

I am interested in the fundamental computational and mathematical challenges that geography poses for social science. Many social problems involve situations where ‘who you’re around’ can affect what you do, or who you’re connected to can impact what you’re able to do. Quantitative geography as a domain, seeks to use information about geographical structures and relationships to do better social science. I develop new mathematical models or algorithms in applications across a broad range of problems in elections, campaigns, segregation & sorting, urban analytics, and inequality.

JGI: Can you give a brief background of your experience? 

I did my PhD at Arizona State (defended December 2017) and worked as a fellow at the University of Chicago Center for Spatial Data Science during that time. I also took summers off during my PhD to work as a data scientist/engineer at Nextdoor.com, Inc. and CARTO, a spatial data science company. Since then, I moved from Brooklyn to Bristol in 2017, and have been lecturing at the University of Bristol.  

JGI: What are the big issues related to data science / data-intensive research in your area? 

The biggest issue with computational/quantitative geography is the difficulty of using the data we have in an effective way. While there are many data science methods that can be used to analyse spatial data, geographic relationships and models use special structures to model or leverage fundamental geographical relatedness or the distinctiveness of geographical areas. Existing techniques ignore geography most of the time, and we can build better models when we take geography into account; however, this poses a massive mathematical and computational challenge.  

JGI: Can you tell us of one recent publication in the world of data science or data-intensive research that has interested you? 

A very recent article I find really fascinating is the work by Fowler et al (2019) on the ‘contextual fallacy’, the assumption that a geographical ‘box’ or ‘container’ like the neighbourhood or LSOA (Lower Super Output Area) can accurately represent the social or geographical context that an individual experiences. Using individual-level census data, this research group has been able to unpack a half-century old debate about the fundamental way geographic data is structured, and can affect the conclusions drawn from it in quantitative analysis.  

JGI: How interdisciplinary is your research? 

Geography as a discipline tends to be outward-facing, and focuses strongly on problems that are in other disciplines. My own work on elections and redistricting links political science and sociology; my work on inequality reaches into econometrics and political theory alike. My main body of work engages heavily in statistics and computer science, two domains often not considered in the same thought as Geography.  

JG: What’s next in your field of research? 

Geographic data science. The cutting edge of quantitative geography is all about trying to figure out how to leverage geographical relationships and distinctiveness directly in new computational methods to build better predictions.  

JGI: If anyone would like to get in touch to talk to you about collaborations / shared interests, how can they get in touch? 

Follow/DM me on twitter @levijohnwolf, or email me at levi.john.wolf@bristol.ac.uk.

__________________________________________________________________________________________________________________________________________________________________

More about The Turing Fellows 

Thirty fellowships and twelve projects have been awarded to Bristol as part of the University partnership with the Turing. This fellowship scheme allows university academics to develop collaborations with Turing partners. The Fellowships span many fields including key Turing interests in urban analytics, defence and health. 

Take a look at the Jean Golding Institute website for a full list of University of Bristol Turing Fellows. 

The Alan Turing Institute 

The Alan Turing Institute’s goals are to undertake world-class research in data science and artificial intelligence, apply its research to real-world problems, drive economic impact and societal good, lead the training of a new generation of scientists and shape the public conversation around data. 

Find out more about The Alan Turing Institute 

We are delighted to announce that the University of Bristol and LV= General Insurance (LV= GI), one of the UK’s largest personal insurers, will be embarking on a new partnership with the aim of working together to make advancements in the field of data science by sharing knowledge, skills and opportunities.

As part of the partnership, LV= GI will establish a team of data scientists and engineers who will be based at the University, working closely with the the Jean Golding Institute (JGI) for Data Science and Data Intensive Research, and the Faculties of Engineering and Social Sciences & Law.

The teams from LV= GI and the University of Bristol will carry out research and development projects to better understand the possibilities presented by machine learning and AI in the insurance sector.

Professor Kate Robson-Brown, Director of the JGI, said: “We are looking forward to exploring how organisations can better use data and analytics to inform their strategic and operational decisions and developing novel ways of building academic partnerships with businesses to generate innovative solutions. Working with LV in this partnership will open up exciting new opportunities for the Jean Golding Institute and the researchers we support.”

Collaborative activities also include LV= GI supervising student projects, supporting young data scientists and delivering lectures to students and staff. There will be internships available at the company for Bristol students and a co-designed MSc programme.

Collaborating with the University’s social scientists, the teams will work to better understand the societal challenges and opportunities of digital technologies.

“Our research is driving forward issues that influence our lives every day- from gender equality, sustainability, digital interactions, marketing and international business management, to the NHS and social relationships,” said Professor Jonathan Beaverstock, Head of the School of Management.  “We are excited about this opportunity to work on these important issues with LV.”

Commenting on the partnership, Steve Treloar, LV= GI CEO, said: “We’re incredibly proud to help play a part in developing the data scientists of tomorrow. The University of Bristol is one of the best in the country with exceptionally strong expertise in the fields of data science and digital, so we’re excited to see what we can learn from one another and the synergies we can make.

“As an industry, it’s absolutely crucial that we recognise the dramatic leaps that technology has made in the past few years and look at how we can harness that for the ultimate benefit of our customers. We are confident that this tie-up with the University of Bristol will be hugely beneficial for both parties and we’re excited to see what comes from it.”

The partnership with the University of Bristol is the latest development in the LV= General Insurance Digital Transformation Academy, an internally focused programme which aims to up-skill current and potential employees so that they can play an integral part in the new digital world of insurance.

Professor Guy Orpen, Deputy Vice-Chancellor for the New Campus Development at the University of Bristol, added: “We are delighted to develop this exciting multi-disciplinary relationship with LV= GI. The innovative nature of the relationship, spanning multiple faculties, research and teaching will provide an invaluable forum for tackling some of the greatest societal and economic problems of our time.

“Our students and staff will have a fantastic opportunity to work with, and learn from, an innovative sector leader. This partnership will be a key component in fulfilling our vision as we develop a world-class centre for research, partnership and innovation at our new Temple Quarter Enterprise Campus”.

Read the full announcement from LV= GI and from the University of Bristol.

As a part of our blog series ‘Introducing the University of Bristol’s Turing Fellows’, the Jean Golding Institute (JGI) have been interviewing several of the academics at the University of Bristol who have recently become Alan Turing Institute Fellows. 

Check out the first blog of our series, featuring Iván Palomares Carrascosa and his work with the Decision Support and Recommender Systems Research Group (DSRS). 

In our second blog of this series, the JGI spoke to Paul Wilcox, Professor of Dynamics and Control in the Faculty of Engineering and recently appointed Turing Fellow. 

JGI: What are your main research interests? 

My main research interests centre around ultrasound, signal processing and imaging applied to Non-Destructive Evaluation (NDE) of safety-critical components and structures. 

JGI: Can you give a brief background of your experience? 

I began working in NDE with a PhD on the use of guided elastic waves for testing water pipes. My specific area of expertise is quantitative assessment of an object’s properties using elastic or acoustic waves over a frequency range from kilohertz (audible) to tens of megahertz (ultrasound). I have worked on applications ranging from critical welds in nuclear power stations to testing the ripeness of avocados. I was a founder of a spin-out company (Inductosense Ltd.) with other colleagues from Bristol in 2015.  

I enjoy the intellectual challenge of NDE, the interaction with a diverse range of industries and the sense of common purpose in the NDE community. The latter is especially evident in the UK Research Centre in NDE (RCNDE), of which Bristol was a founding member in 2003. Over the last decade, I have spent more and more time on signal processing and physics-based data analysis to squeeze ever more information out of individual NDE measurements. The natural next step is to start to lever data science to extract more information from multiple different NDE measurements over the lifetime of a component to allow more accurate prognosis of its current state and remaining life. 

JGI: What are the big issues related to data science / data-intensive research in your area? 

The NDE data for a component can come from many different measurement modalities (e.g. X-ray CT, electromagnetic eddy current measurements, ultrasonic testing, visual inspection) each probing different quantities with different resolutions and over different timescales ranging from daily to once a decade. Dealing systematically with data that is heterogeneous in time, space and modality is the first major challenge.

Furthermore, NDE is driven by the ‘edge cases’: most components do not contain dangerous defects and the purpose of NDE is to find the few that do. Proving to regulators that an NDE technique can achieve the necessary defect detection reliably is a major issue anyway, and techniques that use data science will face the same challenge.

A final point, which stems from the fact that most components do not contain dangerous defects, is that there is a shortage of ‘true-positive’ data on which to test data analysis methods. For this reason, high-fidelity simulations that accurately reproduce experimental measurements are increasingly important. 

JGI: Can you tell us of one recent publication in the world of data science or data-intensive research that has interested you? 

I’m still feeling my way in data science, trying to get to grips with the main schools of thought and work out which approaches are likely to be suitable for NDE. To this end, I’m starting with some popular science books on AI (e.g. The Master Algorithm by Pedro Domingos, which I am now reading for a second time).

Recent publications from the NDE field (including some from my group) showing how numerically-simulated ultrasonic data from defects can be merged with experimental data to produce vast quantities of exceptionally realistic ‘true-positive’ data will, I think, prove crucial for the field of NDE data science going forwards. 

JGI: How interdisciplinary is your research? 

NDE is a multi-disciplinary field that is essential across many industries. Research involves physicists, engineers of all flavours (mechanical, aerospace, civil, electronic), material scientists and mathematicians. 

JGI: What’s next in your field of research? 

Building the basic mathematical framework for drawing heterogenous NDE data together; understanding more about the other constraints in industry (logistic, commercial, legal, regulatory etc); working with industry to select a few exemplar demonstrator applications. 

JGI: If anyone would like to get in touch to talk to you about collaborations / shared interests, how can they get in touch? 

Email me at p.wilcox@bristol.ac.uk

JGI: Are there any events coming up that you would like to tell us about? 

I’m in the process of putting together a kick-off workshop to bring NDE experts from academia and industry together with data scientists. It will probably be held late spring at a venue either in Bristol or at the Alan Turing Institute in London. If you are interested in contributing or attending, please get in touch. 

__________________________________________________________________________________________________________________________________________________________________

More about The Turing Fellows 

Thirty fellowships and twelve projects have been awarded to Bristol as part of the University partnership with the Turing. This fellowship scheme allows university academics to develop collaborations with Turing partners. The Fellowships span many fields including key Turing interests in urban analytics, defence and health. 

Take a look at the Jean Golding Institute website for a full list of University of Bristol Turing Fellows. 

The Alan Turing Institute 

The Alan Turing Institute’s goals are to undertake world-class research in data science and artificial intelligence, apply its research to real-world problems, drive economic impact and societal good, lead the training of a new generation of scientists and shape the public conversation around data. 

Find out more about The Alan Turing Institute