PROGRAM:

Professor Carlo Berzuini (Scientific co-director) served as a Professor of Biostatistics at the University of Pavia, Italy, until 2008. At that time, he relocated to Cambridge, where he held the position of Project Leader at the Cambridge Biostatistics Unit of the Medical Research Council and the Faculty of Mathematics at Cambridge University. In 2011, he joined the University of Manchester as a Research Professor of Biostatistics, achieving Emeritus status in 2023. His groundbreaking contributions encompass various fields, including early work in 1976 on mathematical models for diagnosing metabolic diseases. He pioneered the use of graphical modelling and Markov Chain Monte Carlo (MCMC) methods of inference in medical monitoring (1992, collaboration with D. Spiegelhalter), introduced the first practical method for joint modeling of longitudinal and time-to-event data in 1996 and, at the same time, he engaged in Bayesian prediction of epidemic trends over multiple time scales. In partnership with David Clayton, he proposed a Bayesian solution of the unidentifiability problem in age-period-cohort modeling. His collaboration with Sarah Teichmann in 2004 resulted in predicting protein-protein affinity through the innovative concept of “supradomain.”  Alongside Wally Gilks in 1997, he invented the class of Sequential Markov Chain Monte Carlo methods of inference (1500+ citations), now widely applied in data science, epidemic prediction, real-time target identification, medical monitoring, and beyond. More recently, he received a Scientific Award from the Cambridge Isaac Newton Institute for his research in causal inference and secured a Research Grant from the UK Medical Research Council for collaborative work with Philip Dawid on causative interaction and the assessment of dynamic treatment regimes. He was lead editor (jointly with Philip Dawid and Luisa Bernardinelli) of a landmark book in Causal Inference (2012) and the lead organizer of the first international Conference in Causal Inference held in Europe (2009, Faculty of Mathematical and Physical Sciences of the University of Cambridge). Since 2011 he has led a research group (later joined by Dr. Hui Guo) working in particular on Bayesian extensions of Mendelian Randomization methods, modelling of pain perception (collaboration with Deborah Talmi and Hui Guo) and currently collaborating with Hiren Patel on a multicentre observational study inq the treatment of subarachnoid haemorrhage. Besides his methodological contributions, he has an extensive interdisciplinary research record that underscores his multifaceted contributions to various scientific domains.

Dr Hui Guo is a Reader in Biostatistics and the Head of the Centre for Biostatistics at the University of Manchester. She studied for a PhD on Statistical Causal Inference and Propensity Analysis (supervisor: Professor Philip Dawid) and then worked as a Research Associate at the University of Cambridge. Her postdoctoral research (supervisor: Dr Chris Wallace) focused on the development and application of statistical methodologies with the aim of understanding the genetics and mechanisms of common complex diseases. Hui’s research expertise is in statistical causal inference from observational studies. Her current work involves the identification of causal genes for clinical outcomes from large-scale studies. She is also interested in predictive models, genetic causal pathways and disease networks using multi-omics data.

Dr Leonardo Egidi is senior assistant professor (RTD-B) at the ‘Bruno de Finetti’ Department of Business, Economics, Mathematics and Statistics (DEAMS) of the University of Trieste. He teaches Courses in Advanced Statistics, Statistics and Machine Learning, and Bayesian Statistics in the same university. He obtained his PhD in Statistics from the Department of Statistical Sciences of the University of Padova, and during his PhD he was a visiting fellow at the Department of Statistical Sciences of Columbia University, New York. He is a proficient R package developer and his research topics include: Bayesian robust prior specification, hierarchical modeling, clinical trials analysis, clustering, replication crisis in science, probabilistic programming language, single cell data, and sports modeling. In 2023, he became the Principal Investigator (PI) of a PRIN project focused on sports modeling. He provides statistical consultancy to small firms and hospitals and he has several research collaborations, among the others with University of Pavia, Columbia University, Athens University of Economics and Business. Actually, he is the vice-chair of the ISI Special Interest Group in Sports Statistics. He is an affiliated member of the Italian Statistical Society (SIS), the International Society for Bayesian Analysis (ISBA) and the International Statistical Institute (ISI). He acts as referee for many high standard statistical journals, such as Journal of the Royal Statistical Soiety (A and C) , Biostatistics, and Statistics and Probability Letters.

Dr Hiren Patel (PhD FRCS). Clinically and through his research, Manchester-based Mr Patel has developed an expertise in managing patients with all aspect of brain injury, and he has published widely on the subject. His observations published in the Lancet have changed the way head injuries are managed in the UK, and he led the change of how this care is delivered for patients in Greater Manchester. Besides his significant research grant income, he has set up and runs the National SAH outcome database and is a member of a national team looking at improving outcome after ICH.

Dr Davide Luciani (Dipartimento di Ricerca Ambiente e Salute, Mario Negri Research Institute, Milan, Italy; davide.luciani@marionegri.itsi ) has a degree in Medicine and Surgery from the University of Bologna. He has used his experience from years of medical practice for investigating and formalizing clinical judgment and decision making. To this end, he has collaborated with academic experts in Statistics and Computer Science, in Italy, at the University College of London and at the Department of Computer Science of Aalborg. Since 2005 he has led the Clinical Knowledge Engineering Unit at the Mario Negri Institute, where he has worked with real medical data to develop probabilistic expert systems based on graphical models, Markov chain Monte Carlo methods of estimation and knowledge acquisition methods. Among his achievements is the probabilistic expert system BayPAD, that currently covers hundreds of diagnoses of interest in the Emergency Departments, where patients are typically admitted with generic symptoms.