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Personal profile



  • 2014: co-winner of a HORIZON 2020 European Commission Grant for the development of new early cancer diagnostics
  • since 2012:   Visiting Professor, Statistical Laboratory, Centre for Mathematical  Sciences, University of Cambridge,  UK
  • since 2012:   Visiting Professor, Department of Brain and Behavioural Sciences, University of Pavia, Italy
  • 2013-:            Master Course in Medical and Genomic Statistics, University of Pavia, Italy; teacher and Faculty member
  • since 2012:  University of Manchester, Professor of Biostatistics
  • since 2012:   Principal Investigator of  european research collaboration in Methods of Causal Inference for genomic epidemiology, within the MIMOmics Project, supported by the European Community under the FP7-HEALTH-2012-INNOVATION scheme
  • since 2013 :   Statistical Reviewer for Translational Research applications to Cancer Research UK
  • since 2013 :   involved in the creation and organization of a MSc in Biostatistics of the Faculty of Medical and Human Sciences of the University of Manchester.
  • (2007 - 2013)      Royal Statistical Society Study Group in Bioinformatics, proponent and chair
  • (2010-2012)   Master Course in Genetic and Molecular Epidemiology, University of Pavia, Pavia, Italy; teacher and Faculty member
  • (2001-2008) Visiting Professor, and Research Associate with Core Honorarium and Project Leader capacity, at the Biostatistics Unit of the Medical Research Council, Cambridge, United Kingdom (2006 MRC Biostatistics Unit QQR Review Committee Report



Carlo Berzuini got his secondary studies Diploma from the Liceo Scientifico "Giovio", Como, Italy, with top grades (60/60). He was then awarded a bourse by the Collegio Ghislieri of Pavia, which fully supported his undergraduate and MSc studies, completed in 1974 with a «cum laude» degree in bioengineering.

He now holds a Research Chair in Biostatistics at the University of Manchester (2011-), United Kingdom. After his employment as Professor of Biostatistics at the italian University of Pavia (1983-2008), he joined the Faculty of Mathematics of the University of Cambridge (2009-2011), where he, encouraged by Prof. Dawid, won an Isaac Newton Institute Award for research in Causal Inference. In Cambridge he also worked at the Cambridge Biostatistics Unit of the Medical Research Council, where he obtained (as Project PI) EU funds as part of the EU-funded pre-2007 Bloodomics Consortium, and used them to establish a MRC-BSU team of statistical geneticists. At Cambridge University he also acted as scientific advisor at the Department of Gastroenterology and, as a member of the Cambridge Inflammatory Bowel Disease (IBD) Research Group, he joined a large UK consortium for the study of genetic determinants of IBD. In 2011 he got the Manchester Chair.

As a leading expert in Statistical Genomics (190990) and Causal Inference (190970), and author of landmark contributions in Causative Interaction, Mediation and Mendelian Randomization, in 2009 he has organized in Cambridge the first major international conference on Causal Inference, jointly with L.Bernardinelli and A.P. Dawid. He has then lead-edited a landmark and successful book on Statistical Causality, for Wiley.

In 2007-2013 he was proponent and chair of the Royal Statistical Society Study Group in Bioinformatics, and Member of the Royal Statistical Society Council.

In 2014, his collaboration with Cambridge Abcodia Ltd for developing a biomarker-based approach to early diagnosis of pancreatic cancer earned him a Horizon 2020 nomination. He has participated with PI capacity in several European Research Projects. For decades he has been doing undergraduate, master and post-doc level teaching in top academic venues (Cambridge, Manchester, Pavia, Leiden, Aarhus, etc.). He authored publicly available R and Stan software. He has been statistical reviewer for such research funding bodies as MRC and NIHR and prestigious statistical and medical journals (https://publons.com/researcher/1669670/carlo-berzuini/).

A partial record of his peer reviewing activity for scientific journals can be found in Publons. In addition, he has been statistical reviewer for such research funding bodies as MRC and NIHR. See also https://orcid.org/0000-0001-6056-04

Research interests

He is the originator of the first (1996) practical method for a joint analysis of longitudinal and medical event history data, based on Markov chain Monte Carlo technology, and a (pioneering, of course) user of the method in biomedicine (190979,190992, 191006).

In collaboration with Wally Gilks, he has introduced the class of statistical inference/prediction methods known as Sequential Monte Carlo (SMC), or particle filtering, now blossomed into an active research area (https://www.turing.ac.uk/research/publications/limit-theorems-sequential-mcmc-methods), where the idea is to approximate expectations with respect to a sequence of probability distributions as well as the corresponding normalising constants (191004,191008). Carlo Berzuini has applied the idea in medical and epidemiological monitoring, in structural biology and causal inference.

In collaboration with David Clayton, he has developed a highly cited and widely used method for age–period–cohort analysis and disease incidence forecasting, where disease rate varies smoothly as a function of multiple and non-independent time scales.

In collaboration with Philip Dawid, he has proposed a new justification and extension (to continuous variables) of concepts of causal (mechanistic) interaction between variables in their effects on a binary outcome, with examples of application in studies of genetic epistasis, pharmacogenomics and epidemiological effect modification. More recently (collaboration with Hui Guo and Luisa Bernardinelli) he brought the Bayesian paradigm into Mendelian randomization analysis, as a way of dealing with situations involving weak instruments and/or missing values (paper with Linyi), extended the method for use with population samples that include unrelated as well as related groups of individuals (lifelines: in progress).

His best theoretical work has appeared on Biometrika, Statistics in Medicine, the Journal of the American Statistical Association, Journals of the Royal Statistical Society, Series A, B and C, Bioinformatics, Genetic Epidemiology, Biostatistics, IEEE Transactions BME and PAMI. He has personally analyzed the worldwide-first large cohort of hospital Covid-19 data (from Bergamo) and discovered evidence of beneficial role of renin-aldosterone system inhibitor antihypertensives on mortality of Covid-19 patients (collaboration with Antonello Gavazzi, Gianfranco Parati et.al.). Equally important results he obtained (collaboration with Andy Vail) from UK Covid-19 data.

In his vast interdisciplinary activity he explored usefulness of his methods for advancement of cardiovascular, renal, inflammation, cancer and immune disease sciences, including (collaboration with Manchester and Princeton neuroscientists) a novel approach to the study of inter-individual differences in pain perception (Cognition paper). His applicative works have appeared on Nature, Nature Genetics, Nature Communications, Journal of the American College of Cardiology, Circulation, Cognition, Journal of Alzheimer's Disease, Multiple Sclerosis Journal, Gastroenterology, Diabetes, Cancer Research, British Journal of Cancer, European Journal of Neuroscience, Journal of Alzheimer's Disease, Cytometry and Journal of Molecular Biology, and others.

In 2014 his collaboration with Cambridge Abcodia Ltd for developing a biomarker-based approach to early diagnosis of pancreatic cancer earned him a Horizon 2020 nomination.

He counts about 150 peer-reviewed papers, including both theoretical and interdisciplinary publications.

He has participated with PI capacity in European Research Projects. In 2007-2013 he was proponent and chair of the Royal Statistical Society Study Group in Bioinformatics, and Member of the Royal Statistical Society Council.



My collaborations

A worldwide net of collaborators,  local members of his research groups and Manchester clinical research collaborations

Memberships of committees and professional bodies

  • (2014) Program Committee member and Reviewer. Uncertainty in Artificial Intelligence 2014 (UAI2014) Conference, Quebec, Ontario
  • (2014) Senior Program Committee member and reviewer, AISTATS 2014, Reykjavik
  • (2013) Program Committee member and reviewer, Conference  on Uncertainty in Artificial  Intelligence 2013, Washington
  • (2007-2013)  Committee member, Medical Section of the Royal Statistical Society


2014: invited talk on "Biomarker based early ptediction of pre-clinical cancer", InterOmics Annual Meeting, September 23-26, 2014, Rome.

2014: invited talk on "Causal Mediation and Mechanistic Interaction Analysis, with applications in Genomic Medicine", UNAM/ITAM Universities, Mexico DF, Mexico, 30th of July, 2014.

2014: invited talk on "Causal Inference methods to probe the molecular mechanisms of disease", International Conference on "Bringing Maths to Life", 27-29 october 2014 , Centro Congressi Federico II, Napoli, Italy

2014: one-week Course on "Causality in Health Sciences",  Department of Brain and  Behavioural Sciences, University of Pavia, Italy,  26-30 may 2014 

2014: invited talk on "Mechanistic interaction and other causal inference tools, with an application in the investigation of molecular mechanisms in Multiple Sclerosis". one-dayorkshop on "The Statistical Contributions of A. Philip Dawid: Causal Inference, Graphical Models and Prediction", Wednesday 30th
April 2014, Centre for Mathematical Sciences of the University of Cambridge

2014: invited lecturer on the topic of “Causality in Genomics” at the International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB2014), Cambridge 26-28 June 2014.  

2013: invited discussant at the International Workshop on "Recent Advances in Statistical Inference: Theory and Case Studies", University of Padova, Italy, march 21–23, 2013.

2012; Vote Seconder and Discussant at the Read Paper on by Imai et al., Royal Statistical Society, London

2012: Isaac Newton Institute of Mathematics, Cambridge: ... (video)

2011 Invited Lecture on "Mechanistic Interaction", International Workshop "Causal Inference in the Health Sciences", University of Bologna, May 27-28.

2010 Invited talk : ”Causal Inference in Genetic Epidemiology”, Leiden University Medical Center. Department of Medical Statistics and Bioinformatics. 21 september.

2010 Invited Discussant. Ninth Valencia International Meeting on Bayesian Statistics, Benidorm (Alicante, Spain). June 2010. Discussant of the paper ”Incorporating Biological Information in Bayesian Models for the Selection of Pathways and Genes”, by Vannucci and Stingo.

2010 Invited Lecture at the Workshop on ”Epidemiology, Risk and Genomics”, organized by the Department of History and Biomedical Science in Society, University of Cambridge. January 2010.

2009 Invited lecture on "The investigation of genetic patterns of coronary artery disease within a predictive and a causal perspective", Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge. Monday 11 May 2009, 16:30-17:30

March 2009 Invited talk : ”Identifiability of Sequential Action Plans”. International Meeting on ”Causal Inference : the State of the Art”, Cambridge.

2008 Invited talk : ”Development and Prequential Validation of a genome wide predictor of disease occurrence : a Sequential Monte Carlo approach”. XXIVth International Biometric Conference, July 13-18t, 2008, Dublin.



2012: recipient of an award from the Isaac Newton Trust for research in statistical methods for causal inference at the Centre for Mathematical Science of Cambridge University

2009-2012: awarded MRC Research Grant G0802320, for methodological research on "Genetic Variation, Disease Prediction and Causation"

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

Areas of expertise

  • Q Science (General)
  • Biostatistics
  • Epidemiology
  • mathematics/ statistics
  • Mathematical modelling
  • genomics
  • causality

Research Beacons, Institutes and Platforms

  • Digital Futures
  • Institute for Data Science and AI
  • Christabel Pankhurst Institute


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