Jennifer Visser-Rogers is Vice President, Statistical Research and Consultancy at specialist CRO, PHASTAR, and has a broad portfolio of achievement, particularly in the development of clinical trial methodologies. She directs the research strategy at PHASTAR and provides leadership and advice to statistical consultancy activities. Jen moved to PHASTAR from the University of Oxford, where she was an Associate Professor, and Director of Statistical Consultancy Services. Jen did her BSc and MSc at Lancaster University before moving to the University of Warwick to do her PhD. Her research interests are mainly clinical trial methodologies driven by applied problems and her areas of expertise are survival analysis, the analysis of recurrent events, and joint modelling strategies that combine the two. In 2013, Jen was awarded a NIHR Post-Doctoral Fellowship for her project “Analysis of Recurrent Events in Clinical Trials”.

Jen is a highly active member of the Royal Statistical Society, where her roles have included Honorary Officer for Meetings and Conferences, sitting on Council, and completing a four-year stint as the Society's Vice President for External Affairs. She was also appointed President of the British Science Association Mathematical Sciences Section for 2018, giving a keynote speech at the British Science Festival, and was the London Mathematical Society Popular Lecturer for 2018. Jen is currently a trustee of the Florence Nightingale Museum.

Jen can regularly be found talking all things statistics in schools, theatres and pubs, as well as the odd TV and radio appearance. She’s made a few appearances on BBC Radio 4’s More or Less and is well known for her arguments with Ryanair on BBC Watchdog and in The Times. She presented the popular “Best or Worst” segment on Series 42 of BBC Watchdog in 2019. Jen was the 2020 winner of the annual HealthWatch Award for her work in improving the understanding of statistics through the media.

Throughout the COVID-19 pandemic, Jen carried out numerous interviews including BBC Panorama’s “The Race for a Vaccine”, BBC Radio 4’s “How to Vaccinate the World”, BBC Newscast, and served as ITV's resident COVID-19 statistician. She is also a member of the Royal Statistical Society COVID-19 Task Force.

Richard Wilkinson is a professor of statistics at the University of Nottingham. His research focusses on developing machine learning methodology to help scientists and engineers learn from complex simulators. 

Dave Miller is a senior statistician at Biomathematics and Statistics Scotland (BioSS) and UK Centre for Ecology and Hydrology where he develops methodology for a variety of ecological and environmental problems, focusing on spatial and temporal modelling using structured random effects models like generalized additive models. He worked as a postdoc from 2011 until 2022 at various institutions including: University of St Andrews, University of Rhode Island, the National Oceanic and Atmospheric Administration (NOAA) and the Commonwealth Scientific and Industrial Organisation (CSIRO). At different times he has worked on projects counting animals as diverse as brown bears, oak trees, fin whales, amakihi and loons. He also wrote the emoGG R package, which allows you to plot with emojis in ggplot2.

Nic Freeman completed a DPhil from 2009-2012 at Oxford University. During this time, he was a student of St Anne's College, Oxford and a non-stipendiary lecturer at St Peter's College, Oxford. He then spent three years as a post-doctoral fellow at the University of Bristol, before taking a permanent position at the University of Sheffield in 2015. His research interests are in stochastic processes and applied probability, often towards situations in which finding a reasonable model is a difficult mathematical problem. A common theme throughout much of his work is genealogies. Genealogies record relationships, and transfers, of information across time and space - such as a family tree, or the spread of a news story. He has worked on several situations in which modeling, or analyzing, the large-scale structure of a genealogy is a key issue.