RSS Council nominations for 2020 election

***UPDATED 17 September 2020***

At the end of this year, seven Ordinary members of Council (our trustees) will complete their four-year terms of office. This means that we will need to elect seven new Council members to fill these vacancies, with the newly-elected Ordinary members commencing their terms of office on 1 January 2021.

Having taken into account all the suggestions received from the membership, Council has nominated the nine fellows named below to stand election for the seven vacancies (as Society regulations require that Council put forward for election two more nominations than the number of vacancies).

All fellows should have received an email with a secure link to the voting site. Please check your ‘junk’ mailbox or contact us if your email has not arrived. Voting closes at midnight on 14 October 2020.

The nine nominees for the seven vacancies on Council, in alphabetical order, are:

Sophie Carr
Sophie started her career as an aeronautical engineer (fulfilling her childhood dream) when during flight trials she became interested in the concept of information overload. While she continued to work full time on a range of engineering and analytical problems, Sophie completed a part-time PhD in Bayesian Belief Networks at Cranfield University. In 2009, redundancy provided a unique opportunity and Sophie founded her own company Bays Consulting. Her company ethos is all about statistical analysis and data science methods to help businesses and individuals use the data collected throughout business processes and trials. This has allowed Sophie to worked on a wide range of projects from developing forecasting models for supermarkets, to assessing autonomous drone flights. Sophie enjoys explaining statistics and increasing people’s ability to understand, articulate and engage with the values and statistics that impact key business projects, initiatives and insights.  

Outside of work, Sophie is a fellow of the RSS and currently holds the title of the World’s Most Interesting Mathematician and takes part in STEM outreach through activities such as writing and delivering Royal Institution masterclasses on a range of statistics topics to help enthuse and engage the next generation in developing a love of data and statistics.

Jotham Gaudoin
Jotham is a senior lecturer in mathematics and statistics at Sheffield Hallam University, where he teaches at all levels from foundation to postgraduate. He is also an associate lecturer at the Open University. He is predominantly focused on teaching with two particular areas of interest. The first of these is the foundation level, where he teaches basic mathematical and statistical modelling techniques to those who will go on to degrees not only in mathematics and statistics but also to physics and engineering as well. The second is the final year undergraduate level, where he teaches primarily applied statistics courses, while ensuring that these courses are suitably grounded in the underpinning theory. In addition to his teaching, Jotham is also currently supervising several PhD students in machine learning for natural language data, providing statistical input to complement supervision from the computing department while also enabling him to keep abreast of developments in that field.

Having first completed qualifications in modern languages and translation before studying mathematics and statistics at the Open University and then completing a PhD in applied statistics at Southampton in 2016, Jotham is a strong advocate for the communication of statistical ideas to specialists and non-specialists and for the development of these skills in undergraduates. He was jointly responsible for the creation of a new statistics-focussed route through the Sheffield Hallam undergraduate degree, which was carefully aligned with the RSS GradStat requirements and he therefore has a thorough knowledge of these.

Jotham has become a member of the RSS comparatively recently, but has in that time become a member of both the newly renamed Computational Statistics and Machine Learning (CSML) section and the Statistics in Sport section. His contribution to the CSML section is focused on training in statistical computing and the development of skills in this area, while his interest in statistics in sport comes from his belief that this is a potentially rich area to help to drive interest in statistics more widely. In addition, he often supervises undergraduate projects in this area.

Anjali Mazumder
Anjali Mazumder is the Theme Lead on AI and Justice & Human Rights at the Alan Turing Institute for Data Science and Artificial Intelligence. Her work focuses on empowering government and non-profit organisations by co-designing and developing responsible data and AI methods, tools and frameworks to innovate and improve services and policy insights for safeguarding human rights, enabling inclusive societies, and building resilient institutions and communities. She is passionate about fostering multi-disciplinary collaborations and multi-sector partnerships to co-create pathways for innovation that improve services and policy to safeguard human rights and address humanitarian challenges. Her research interests are in developing integrated Bayesian decision support systems to manage uncertainty with complex data structures; causal reasoning in the wild; and socio-technical solutions to harnessing multiple sources of data whilst enabling privacy, security, trust, fairness, robustness, and transparency for human rights and humanitarian challenges such as modern slavery and human trafficking. 

She has over 15 years’ experience tackling fundamental statistical problems of societal importance – human rights, justice, security, the Law, education, public health & safety – working at the interface of research, policy and practice in the UK, the US, and Canada, fostering multi-disciplinary and cross-sector collaborations. She was appointed to Canada’s National DNA Databank Advisory Committee (2012-) and currently serves on the UK’s Forensic Science Regulator’s fingerprint interpretation subgroup, and the senior management board of the UK’s Policy and Evidence Centre for Modern Slavery and Human Rights. She has also served the Royal Statistical Society in a variety of ways, most recently on the Statistics & Law Section and the Data Science Section committees. She holds a doctorate in Statistics from the University of Oxford and two masters’ degrees in Measurement and Evaluation, and Statistics from the University of Toronto.

Robin Mitra
Robin is a lecturer in Statistics at Lancaster University. He completed his PhD in Statistics at Duke University in 2008. He was a lecturer in statistics at the University of Southampton from 2008 – 2017 before moving to Lancaster. His research interests are in missing data and data confidentiality with research interests in Bayesian methods more generally. Robin has active collaborations with the Office for National Statistics and other external research organisations.

When teaching statistics, Robin tries to take an outward facing view wherever possible. At the University of Southampton he designed a module that taught students from a diverse range of backgrounds how to critically appraise statistics reported in the media. He has also taught short courses on missing data and data confidentiality that have a diverse attendance.

Robin is an active member of the RSS. From 2016 he has been a member of the RSS Medical Section and chaired the Medical Section from January 2017 – January 2020. He has organised a scientific meeting on multiple imputation methods in medical studies that was well attended, and has planned a meeting on data confidentiality in clinical trial and medical data sets. Robin has also delivered a course on Statistical Inference for the African Institute for Mathematical Sciences (AIMS) Cameroon that was supported by the RSS.

Brendan Murphy
Brendan is full professor and head of the School of Mathematics and Statistics in UCD. He works on statistical models for complex data with a particular focus on clustering and latent variable models. He has published widely in both statistical methodology and applied statistics journals.

He is currently the editor for social science and government for the Annals of Applied Statistics journal. He is an interdisciplinary researcher and has collaborated with political scientists, social scientists, biologists, animal scientists, among others. He contributes to open source statistical software and has co-authored a number of R packages.

He teaches statistics to undergraduate and graduate students and has sixteen graduated PhD students. He is on the executive committee of APTS. He has been a fellow of the RSS for over twenty years and a chartered statistician for over five years.

Hira Naveed
Hira is a passionate, creative and enthusiastic ‘Data and Research Analyst’ at Cancer Research UK. Her work involves analysing routinely collected health data from the Office for National Statistics, NHS Digital and Public Health England to provide intelligence that will improve cancer care for patients.

She holds a BSc in Actuarial Mathematics and Statistic, achieving a first-class honours, and was presented with a Gold Award by her university. Her first RSS conference presentation in 2017 was on the analysis of a new model of care for a chronic joint pain advisory role in the NHS. Since then, she has won best presentation at a national cancer data event and her work on health and care inequalities being shortlisted for the NHS Visual Data Challenge this year.

Hira is an active fellow of the society, a Statisticians for Society volunteer and secretary for the Official Statistics Section. She has previously fulfilled the role of scientific sub-committee member for the University of Oxford’s Young Statisticians’ Meeting in 2018. Hira is passionate about visualising data in an engaging way and aims to draw beautiful conclusions from complex datasets with the help of storytelling and interactivity that allows people to explore the data for themselves. Hira is also passionate about women having more representation in the world of data and is currently leading on planning a collaborative event with the RSS Women in Data Science and Statistics special interest group (SIG).

Gesine Reinert
Gesine is a Professor of Statistics at the Department of Statistics, University of Oxford. Before joining the department in 2000, she worked at the University of Cambridge, the University of California Los Angeles, and the University of Southern California. She received her PhD at the University of Zurich in 1994. Her research links applied probability with statistics, with a strong focus on the analysis of networks. Gesine is a fellow of the Alan Turing Institute and has collaborated with biotech companies as well as with Accenture. In her department she was director of graduate studies and is currently an early career researcher champion.

Gesine is an active member of the RSS and currently chairs its Applied Probability Section. She is the vice-chair of the European Cooperation for Statistics of Network Data Science (COSTNET). She is a member of the council of the Institute of Mathematical Statistics, of which she is a fellow. As a member of the Bernoulli Society she serves on its council, and she is editor-in-chief of their series SpringerBriefs in Probability and Mathematical Statistics.

Lucy Teece
Lucy is a research fellow in medical statistics in the Department of Health Sciences at the University of Leicester. She is conducting a programme of methodological research alongside studies in the NIHR Applied Research Collaboration, which includes evidence synthesis, design and analysis of pragmatic clinical trials, and prognostic modelling using real world evidence. She has a BSc in Mathematic and Statistics from Newcastle University; an MSc in Medical Statistics from the University of Leicester; and completed her PhD at Keele University last year. Her research interests include prognostic modelling, competing risks analysis, analysing large registries, and multi-morbidities.

Lucy is an active member of the RSS and currently serves on the committee for both the Young Statisticans Section (YSS) and the East Midlands Local Group. During her six-year term volunteering for the YSS committee, Lucy has sat as meeting secretary, secretary, vice chair and chair. Lucy has made major contributions to the YSS Programme at the RSS conference, including organizing and presenting the 'Have I got stats for you!' quiz show in Glasgow. Lucy is an RSS statistical ambassador and has promoted numerous RSS initiatives such as; AIMs, Statisticians for Society, and STEM ambassadors. She is passionate about encouraging diversity and increasing accessibility to RSS events, as evidenced by the organisation of the successful 'Women in Statistics and Data Science' event held last year for International Women's Day.

James Weatherall
Jim is the vice president of data science and artificial intelligence in research and development at AstraZeneca.  A physicist by training, Jim obtained his doctorate in High Energy Particle Physics from the University of Manchester, before embarking on a three-year postdoctoral fellowship studying the subtle differences between matter and antimatter. On leaving academia, he joined Tessella as a scientific software consultant, working directly as a lead software engineer and data scientist on many large scale projects across the consumer products, petrochemicals, life sciences, automation and public sectors. He joined the Biomedical Informatics team in AstraZeneca in 2007, and via a series of promotions led a number of teams specialising in clinical and biomedical informatics, statistics and advanced analytics. 

Jim has made critical contributions to, and published in, a number of diverse fields such as data visualisation, cryptography, text mining, machine learning and health data science.  Jim is committed to driving the application of the quantitative sciences, advanced analytics and related approaches as a way of unlocking the full potential of data – transforming the way medicines are discovered, developed and make a difference to patients’ lives.  Jim is an honorary reader in computer science at the University of Manchester, and vice chair of the Data Science Section at the Royal Statistical Society.

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