Prof. Trish Greenhalgh: “The algorithm meets the individual: using artificial intelligence applications to support clinical judgements”
In her work, prof. Trish Greenhalgh seeks to celebrate and retain the traditional and the humanistic aspects of medicine and healthcare while also embracing the exceptional opportunities of contemporary science and technology to improve health outcomes and relieve suffering. Three particular interests are the health needs and illness narratives of minority and disadvantaged groups; the introduction of technology-based innovations in healthcare; and the complex links (philosophical and empirical) between research, policy and practice. She has brought this interdisciplinary perspective to bear on the research response to the Covid-19 pandemic, looking at diverse themes including clinical assessment of the deteriorating patient by phone and video, the science and anthropology of face coverings, and policy decision-making in conditions of uncertainty. She is a member of Independent SAGE, an interdisciplinary academic team established to provide independent advice on the pandemic direct to the lay public.
Trish is the author of over 400 peer-reviewed publications and 16 textbooks. She was awarded the OBE for Services to Medicine by Her Majesty the Queen in 2001 and made a Fellow of the UK Academy of Medical Sciences in 2014. She is also a Fellow of the UK Royal College of Physicians, Royal College of General Practitioners, Faculty of Clinical Informatics and Faculty of Public Health. In 2021 she was elected to the Fellowship of United States National Academy of Medicine for “major contributions to the study of innovation and knowledge translation and work to raise the profile of qualitative social sciences”.
Prof. Will Tiemeijer: “How do people deal with uncertainty?”
Will Tiemeijer studied Dutch Language and Literature at the University of Utrecht. In 2006 he obtained his doctorate (cum laude) at Tilburg University for his dissertation The secret of the citizen: about the state and public opinion research. Since 2007 he has worked at the Scientific Council for Government Policy, where he specializes in topics at the intersection of psychology, philosophy and politics. Since 2019 he is appointed professor of Behavioral Sciences and Policy at Erasmus University Rotterdam. Central to this chair is the question of how psychological knowledge and insights can contribute to a better understanding of social problems in general and government policy in particular. In September 2022, Cambridge University Press published his most recent book: Self-Control. Individual Differences and What They Mean for Personal Responsibility and Public Policy.
Prof. Ionica Smeets: “The translation of uncertain epidemiological findings to the public”
Ionica Smeets is a science journalist, mathematician and professor of science communication at Leiden University. She wants to improve the interaction between science and society by studying how science communication works. What goes wrong when those groups communicate with each other? And what can scientists do to improve this process? The general public knows her for her popular science columns, blogs, books and television work. She has also written several books on science communication. She for example writes columns for a prominent Dutch national newspaper, de Volkskrant. She also makes a photo comic for New Scientist with Ype Driessen. She has made various TV programs and has been presenting the Dutch National Science Quiz since 2015.
Smeets has a background in mathematics that she obtained with a cum laude degree at TU Delft. She then obtained her PhD in number theory at Leiden University.
Dr. Daniel Oberski: “Statistics in Personalized Medicine”
Daniel holds the chair of Data Science in Healthcare jointly at the University Medical Center Utrecht (UMCU), department of Data Science & Biostatistics; and at Utrecht University, Department of Methodology & Statistics. He works on developing novel methods useful in the health and social sciences, usually by combining ideas from different areas of statistics with ideas in the field of machine learning. He also works on applications across various disciplines, including cardiology, rheumatology, psychology, economics, transportation, environmental epidemiology, electrophysiology, and forest ecology. Daniel is coordinator of the Social Data Science team at ODISSEI – the Dutch national infrastructure for the social sciences – and chief methodologist at UMCU’s Digital Health program.