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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. 

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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 

As part of the celebration of International Women’s Day on Friday 8 March 2019, the Jean Golding Institute will be launching a new blog series interviewing the people behind the Changemakers initiative – bringing hands-on STEM activities to young women.

In the first of this series, Data Scientist Elena Hensinger has given us an insight to her work and her involvement with Changemakers.

Q: How did you get into data science, and what does it involve?

Data Science is a field that emerged with the rise of computing and storage capabilities, and the novel challenges that came with it. Think of a large factory with hundreds of machines, each of which has sensors to monitor the correct functionality of those machines, generating data each second. Cheap storage space allows us to save this data, but how can we gain any insights into it? How can we understand when devices run smoothly and when they are close to failing (which could be very costly)?

This is where Data Scientists fit in – they combine tools and algorithms from Computer Science, and especially from Machine Learning, with analytical thinking, as well as an understanding of the client’s business environment and strong communication skills.

Q: What do you enjoy about your work?

Clients come to us with their data and the questions they would like to have answered, and we help to find the right solution for the problem. This is often in close communication with the client, as, after all, they know their business, processes, and machines better than anyone else!

Every project is unique and allows us to apply different tools and algorithms. I learn something new every day, and find this intellectual challenge very enjoyable.

Q: How did you get the job that you have now?

While studying for my Bachelor’s degree in Computer Science, I stumbled upon a video from a RoboCup football match. I immediately became excited – creating smart machines was where I wanted to be in the future. However, I had no idea how to get started or even what scientific areas were involved. In the end, I never got to work with robotics, but I did discover the area of Machine Learning, in which I later completed my PhD at the University of Bristol.

I realised that, next to learning, I also very much enjoyed sharing my passion about Computer Science. So, after finishing my academic studies, I did a lot of work in training, ranging from workshops for teachers to teaching Computing at a school. However, I did miss programming and algorithms, and thus, once again, my path led me back to Machine Learning – applying the skills from my PhD as a Data Scientist.

Q: Why did you want to support Changemakers?

There are many interesting areas in Computer Science, which will affect everything in our future life: how we travel, communicate, get well after illness, live and make scientific discoveries. This will incorporate all kinds of skills and will need input from all representatives of our diverse society, including all genders, ages and cultural backgrounds.

However, we typically do not think of any other skills than ‘programming’ when we talk about computing. By supporting this event, I want young women to learn about the range of skills and talents that are and will be needed in Computing, as well as some of the exciting professions that they can go into after school.

Changemakers

The Jean Golding Institute, in collaboration with the Faculty of Engineering Outreach team and a network of Bristol-based women working in STEM, are organising a programme of events to tackle gender diversity in tech.

Changemakers will be taking place from 8 – 11 July 2019 in the Merchant Venturers Building, University of Bristol, and will consist of a hackathon to support young women in STEM.

For more information about the Changemakers initiative, take a look at the JGI website.

If you would like to get involved please get in touch with engf-outreach@bristol.ac.uk.

On 4 February 2019, the University of Bristol announced that it had been awarded funding for nine Centres for Doctoral Training from the Engineering and Physical Sciences Research Council (EPSRC), a record boost of over £50 million to train the next generation of highly-skilled researchers across the University. For more information about the announcement and a full list of the CDTs awarded, make sure to check out the University of Bristol website.

In order to find out more about these new CDTs, and the people behind them, the Jean Golding Institute will be interviewing a number of CDT Directors in our latest blog series.

In the first blog of our new series, Guy Nason, Professor of Statistics and Director of the Computational Statistics and Data Science (COMPASS) CDT, has spoken to us a little bit about his CDT.

JGI: Can you tell us a bit about your Centre for Doctoral Training?

COMPASS is a CDT providing training on Computational Statistics and Data Science. It is going to be a unique centre that provides cutting-edge training in the latest statistical methods, meshing that with state-of-the art computing and dripping in with a diverse range of other training opportunities (responsible innovation, entrepreneurship, etc). A key goal is to supply excellent people to meet the massive demand arising from academia, industry and government as part of the ‘data science revolution’.

JGI: What are you most excited about in your new CDT?

In the wild, professional research statisticians are found in all areas. COMPASS has expert research statisticians and other statistical experts in many places including in the Bristol School of Medicine, School of Education, Faculty of Engineering and other colleagues in Science, and we are growing that number. We have a large, and growing, list of excellent external partners: e.g. security companies who are interested in statistical methods arising in medical statistics, as some of the issues are similar. A number of our projects will cross departmental boundaries and we are open to working with anyone.

JGI: What are you looking for in applicants to the doctoral training programme?

Academic excellence, background in highly numerate disciplines (maths, statistics, yes, but also theoretical computer science, physics, econometrics, etc). Also, keen on team workers, tenacity and excellent communication skills.

JGI: How do people find out more about your CDT?

Please visit our website.

JGI: What are your main research interests?

My main research interests are time series analysis, statistical science, official and government statistics.

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

Originally, my research was in theoretical statistics, but over the years I have gradually got more interested in areas where my knowledge could be used in applied areas, and currently this is official and government statistics. It may sound dry, but such statistics, such as inflation, have an enormous impact on the lives and wellbeing of citizens.

I have always been interested in and passionate about teaching. I trained as a schoolteacher with a PGCE and have worked hard on teaching ever since.

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

In my area (time series) I’m always struck by how much we don’t know, rather than what we do and quite impressed by those who attempt to analyse data with inappropriate methods! Good examples are the lack of methods to deal with irregularly-spaced or nonstationary time series. People routinely, erroneously, analyse their data with standard methods, without thinking whether they are appropriate. Statisticians have been called “methodological terrorists”, but I prefer to think that statisticians are just interested in getting it right.

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

Several things. However, Andrew Gelman’s blog is always a good read.

JGI: How interdisciplinary is your research?

Extremely. John Tukey (one of the inventors of the fast Fourier transform, and a leading statistician) said that the advantage of being a statistician is that you get to play in everyone’s back yard. Statistics gets involved in many, many areas.

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

In time series, I think there will be much more collection, modelling and analysis of series that are collected at multiple sampling rates. If your series gets boring, then reduce the sampling rate; if it gets exciting, then increase it. We will be developing the methods to do this, and econometrics is ahead of the game here. I’m also interested in cases where your sampling rate is probably not enough to properly acquire all the information in your series.

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

Email – but you will find all statisticians massively overloaded. Our methods are popular, so we get asked to join in many projects.

Upcoming posts

Make sure to keep an eye on the JGI blog for more posts in this series in the coming weeks. If you enjoyed this blog, don’t miss our other series, including ‘Introducing the Turing Fellows’, which kicked off with a post featuring Iván Palomares Carrascosa earlier this month, and our ‘Spotlight on Data’ series, the latest of which focussed on Rebecca Barnes’ work with the ‘One in a Million: A study of primary care consultations’ dataset.

 

In our latest blog series, the JGI will be interviewing some of the academics at the University of Bristol who have recently become The Alan Turing Institute Fellows.

In the first of this series, Turing Fellow Iván Palomares Carrascosa has given us an overview of his exciting work with the Decision Support and Recommender Systems Research Group (DSRS).

JGI: What are your main research interests?

My main research interests are decision making under uncertainty, decision support systems, recommender systems for data-driven personalisation (with applications on tourism and leisure in smart cities, health and wellbeing, and person-to-person recommendation) and intelligent data fusion approaches.

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

I have investigated group decision making and group recommendation approaches since my doctoral studies. My main areas of research are large group decision making, i.e. how to support effective and consensual decisions when large and highly diverse groups of participants are involved. Recently, I have been increasingly devoted to investigating personalisation approaches for users and groups via recommender systems, incorporating novel data science and AI methods.

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

Incorporating multiple sources of data, e.g. users’ preferences and behaviour, contextual information and connected sensors data, to produce highly tailored recommendations in smart cities. In particular, we are studying the problem of context-aware recommendation in the domains of leisure activities, tourism and healthy habit development by citizens.

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

The following paper on reciprocal (people to people) recommendation is interesting: Optimally Balancing Receiver and Recommended Users’ Importance in Reciprocal Recommender Systems (AMC Recsys 2018).

JGI: How interdisciplinary is your research?

Reaching out to applications across disciplines is a top priority for us, hence we are keen on collaborations to deploy our personalisation and decision support solutions in areas such as management, participatory democracy, nutrition and physical activity, tourism, etc.

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

Collective decision-making involving not only human participants with subjective judgements, but also artificial stakeholders, e.g. autonomous agents playing a role in the decision-making problem.

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

I’m very easily contactable in person, via e-mail, LinkedIn or Twitter (@DSRS_uob). Feel free also to drop us a message via our DSRS website.

Take a look at the DSRS flyer for more information about the research group.

Upcoming events

Iván will be running a workshop on recommender systems for data-driven personalisation as a part of Data Week 2019 (20-24 May 2019). The workshop, RESULTS: Interdisciplinary workshop on ‘REcommender Systems for engaging Users with healthy Living habiTS’, will constitute a discussion forum on the challenges and opportunities of personalisation approaches, data-driven Decision Support and Recommender Systems (RecSys), in the areas of fitness, wellbeing and promoting healthy living. Keep an eye on the JGI website for more details in the coming weeks!

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.

More information about The Alan Turing Institute can be found on their website.