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Warwick Turing Fellow Hosted Catch Up Series 2023

This event will showcase latest developments in data science and artificial intelligence, with talks from Turing Fellows and lead researchers at The Alan Turing Institute. The event is also designed to facilitate connections between Warwick Turing Fellows and the broader University community.

When: Friday 27th January 2023, 10.00 - 14.00
Where: Scarman, Meeting Space 11  


09.30 – 10.00

Arrival, Refreshments

10.00 – 10.15

Welcome by Professor Ioannis Kosmidis, Turing University Lead at the University of Warwick

10.15 – 10.30

Professor Ram Gopal, Professor of Engineering Systems Management

Talk Title: Gaining a Seat at the Table: Enhancing the Attractiveness of Online Lending for Institutional Investors


Although online lending enjoyed explosive growth in the past decade, its market size remains small compared to other financial assets. The risk of losing money, stringent government regulations, and low awareness of the benefits of online loans have hampered the realization of the full potential of the online lending market. Because online loans are an emerging asset class, investors may not be aware of the investment performance of online loans compared to other assets, and it remains an open question whether online loans offer sufficiently attractive returns to warrant inclusion in an asset allocation decision. To attract lenders, platforms must provide an appealing investment opportunity which entails construction of portfolios of loans that investors find attractive.
We propose general characteristics-based portfolio policies (GCPP), a novel framework to overcome the difficulties associated with portfolio construction of loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive dataset spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average internal rate of return (IRR) of 8.86% to 13.08%, significantly outperforming a naïve equal-weight portfolio of loans. We then address the question of whether online loans can earn competitive rates of return compared to traditional investment vehicles through six market indices covering stocks, bonds, and real estate.

The results demonstrate that a portfolio of online loans earn competitive or higher rates of return compared to traditional asset classes. Furthermore, the IRRs of the loan portfolios have small correlations with the benchmark index IRRs, pointing toward significant diversification benefits. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.

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10.30 - 10.45

Professor Frances Griffiths, Professor of Medicine in Society
Dr Abimbola Ayorinde, Assistant Professor (Research Focussed)

Talk title: Healthcare Professionals’ Experiences of The Use of Artificial Intelligence


Healthcare Professionals’ Experiences of The Use of Artificial Intelligence

Abstract: There has been significant increase in the use of Artificial intelligence (AI) in healthcare settings. We conducted a systematic review to synthesise existing evidence on the experiences of healthcare professionals in using AI that is fully deployed in the healthcare settings globally. We focused on non-knowledge based clinical decision support systems. We searched four electronic databases, contacted experts and searched reference lists of included studies. We used a theoretically informed thematic approach to synthesis the findings. We included 18 studies of various designs conducted in various countries. The studies covered different AI applications.

For example, Face2Gene® which is used in the diagnosis of children with rare genetic syndromes, Robot Laura used for early identification of sepsis and InsightRx, used in determining the optimal dosing regimen for vancomycin. Many studies highlighted issues with healthcare professionals’ understanding of AI applications. For example, some healthcare professionals were concerned about not understanding the AI outputs or the rationale behind them. There are also issues with trust/confidence in the accuracy and recommendations by AI applications. Studies also reported issues with absence of official guidelines, and infrastructure to support the use of AI in healthcare settings. Overall, some healthcare professionals believed AI provided added value and improved decision-making, some reported it only served as a confirmation of their clinical judgment while some did not find it useful.

The findings suggest that for AI-based technologies providing support to healthcare professional decision-making, the health professional needs to trust AI and understand how it works to benefit patient. This requires involvement of healthcare professionals in all stages of the AI application development and deployment.

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10.45 - 11.00

Dr Nicole Wheeler, Institute of Microbiology and Infection, School of Computer Science, Birmingham Fellow, Turing Fellow

Talk Title: Computational approaches to bolster biosecurity


The COVID pandemic has highlighted the world’s vulnerability to the sudden expansion of a high-risk pathogen strain. However, it has also provided an illustration of the power of genomics to detect, understand and control outbreaks.

In this talk, I will outline some of the initiatives that aim to make better use of genomic data for the detection of novel threats to human health and some of the challenges posed by the analysis of large-scale biological sequence data. In particular, I will explore different approaches to representing biological sequences computationally and the implications these have for what we can ultimately learn from the data. I will also explain how this computational work sits within a broader translational framework and how these computational considerations interact with stakeholder values, such as policy makers, public health professionals and the security community.

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11.15 – 11.30

Break, Refreshments

11.30 - 11.45

Professor Rob Procter, Professor of Social Informatics, Department of Computer Science

Talk Title: Recent Research on the Theme of Data Science for Social Good


Data science for social good focuses on the application of data science to domains of social concern, one goal being improving the design and implementation of policies and programmes for fostering social well-being. In this talk, I will provide a brief overview of some recent projects in which I have been involved at the Alan Turing Institute on this theme.

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11.45– 12.00

Professor James Smith, Professor in the Department of Statistics

Talk Title: Causal Discovery of a Covert Aggressor


Causal discovery algorithms have had widespread success in constructing from observational multivariate data sets hypotheses about what might happen if the underlying observed processes were intervened upon. However, in a domain where the possible interventions defend against the covert attacks of an aggressor - because of the rationality of the adversary means that they can think out new ways of breaching the defences - standard causal discovery algorithms will not work.

In this joint project between a team of researchers drawn from Warwick, Turing and DSTL, I will outline how we are using new graphical methodologies to construct new causal discovery algorithms to help predict the efficacy of different defensive interventions. Here the fact that the available observed data comes from many different sources, only indirectly measures the processes of interest data and is patchy, disguised and dynamic and the processes not natural expressed by a Bayesian Network make this task challenging. However, we demonstrate that by using Bayesian formalisms we are able to construct new causal algebras customised to this domain and on the basis of these algorithms propose new bespoke causal discovery algorithms.

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12.00 – 12.15

Professor Peter Triantafillou, Professor of Data Science

Talk Title: Machine Learning and Machine Unlearning for Data Systems


Learned Data Systems (that is, data systems with machine learning components) bear the promise of increased performance, especially for resource-hungry analytics tasks over massive datasets. As such, they are enjoying large attention by researchers. However, DB systems differ from other domains where machine learning (ML) plays a key role in that DBs are continuously updated.

How can we ensure then that previously trained neural-network ML models continue to be accurate in the face of DB updates, such as data insert and/or delete operations? New data insertions may carry out-of-distribution (OOD) data for which models may be highly inaccurate. Likewise, for data deletions, which additionally introduce an additional challenge, namely that of unlearning. How can we then surgically unlearn what was previously learned and now deleted without erasing knowledge about relevant retained data? And how can we ensure the above efficiently, i.e., without retraining the models from scratch (which is a time-consuming operation)?

In this talk, I will highlight our research results for the above problems. To our knowledge this is the first research results achieving the above goals.

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12.30 – 14.00

Informal networking lunch