Centre Director – Professor Mihaela van der Schaar
Mihaela van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Turing Faculty Fellow at The Alan Turing Institute in London, a Chancellor’s Professor at UCLA and an IEEE Fellow. In 2019, she was identified by NESTA as the UK-based female researcher with the most publications in the field of AI.
Professor van der Schaar’s many awards include the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation Career Award, numerous best paper awards, including the IEEE Darlington Award. She holds dozens of patents.
Above all, Professor van der Schaar’s passion is to make a positive impact on people’s lives by applying machine learning and AI to the unique challenges in healthcare and medicine.
Centre Co-Director – Professor Andres Floto
Andres Floto is a Professor of Respiratory Biology at the University of Cambridge, a Wellcome Trust Senior Investigator, and Research Director of the Cambridge Centre for Lung Infection at Papworth Hospital, Cambridge. Clinically, he specialises in the treatment of patients with Cystic Fibrosis (CF), non-CF bronchiectasis, and infections with nontuberculous mycobacteria.
Professor Floto research explores how immune cells interact with bacteria, how intracellular killing and inflammation are regulated and sometimes subverted during infection, how population-level whole-genome sequencing can be used to reveal biology of bacterial infection, and how therapeutic enhancement of cell-autonomous immunity may provide novel strategies to treat multi-drug-resistant pathogens.
Dr Sarah Teichmann
Head of Cellular Genetics and Senior Group Leader, Sanger Wellcome Institute.
Elected fellow of the Royal Society.
Dr Teichmann is a world-leading scientist who combines her expertise in computational and systems biology with single-cell biology, genomics and immunology. She applies her knowledge using novel approaches to answer questions fundamental to our understanding of biology and medicine. She is a founder and principal leader of the Human Cell Atlas international consortium.
Professor Stefan Scholtes
Dennis Gillings Professor of Health Management,
Cambridge Judge Business School.
Director of the Centre for Health Leadership & Enterprise.
Professor Scholtes‘s research is strongly practice-based and embedded in close collaborations with the Cambridge University Hospitals NHS Foundation Trust, Cambridgeshire and Peterborough Foundation Trust, and Public Health England.
He also engages closely with local GP practices and has co-founded the Primary Care Innovation Academy at Judge Business School to support the transformation of out-of-hospital services.
Dr Alexander Gimson
Consultant Transplant Hepatologist,
Cambridge University Hospitals NHS Foundation Trust.
Chair of the Care Advisory Group, Cambridgeshire & Peterborough Sustainability and Transformation Partnership.
Dr Gimson led the national team which developed in a new organ allocation offering scheme whereby organs are offered to the person on a national waiting list who has the greatest calculated net life years gained from the particular donor organ.
He is running a project which aims to discover if an AI/machine learning model can beat existing models, to make that organ-offering even more equitable.
Professor Pietro Liò
Professor of computational biology in the department of Computer Science at the University of Cambridge.
Member of the Artificial Intelligence group of the Computer Laboratory.
Professor Liò has PhDs in Complex Systems and Non Linear Dynamics, and in Theoretical Genetics. He is the author of over 400 papers. His specialties include bioinformatics algorithms, predictive models in personalised medicine, modeling comorbidity and aging, methods for combining multi-scale biological processes, statistics of multi omics and multi physics modelling of molecules-cell-tissue-organ interactions.
Dr Ari Ercole
Dr Ercole is a consultant in anaesthesia and intensive care, and a researcher.
Fellow in Clinical Medicine at Magdalene College, University of Cambridge.
Dr Ercole is a consultant in anaesthesia and intensive care medicine. He also holds a PhD in experimental physics from the University of Cambridge. He divides his time between clinical practice and research.
His particular focus on data-driven research is in developing novel analytical techniques, including machine learning and feature discovery for intensive, acute and perioperative care. He is interested in the use of statistical and non-linear signal processing techniques to better understand and extract digital biomarkers from millisecond-resolution clinical data for prediction, clinical phenotyping and for precision care.
Dr José Miguel Hernández-Lobato
Dr Hernández-Lobato is a University Lecturer (US Assistant Professor) in Machine Learning at the University of Cambridge.
Dr Hernández-Lobato‘s research revolves around model-based machine learning with a focus on probabilistic learning techniques and with a particular interest on Bayesian optimisation, matrix factorization methods, copulas, Gaussian processes and sparse linear models.
Dr Eoin McKinney
Dr McKinney is a University Lecturer in Renal medicine at the University of Cambridge and an honorary consultant in nephrology and transplantation, Cambridge University Hospitals NHS Foundation Trust.
Dr McKinney’s research explores the interface between immune responses to infection and those driving inflammatory pathology, applying machine learning methods to the integration of multi-omics data, building interpretable predictive models for rapid translation into clinical practice while informing underlying disease biology and identifying novel therapeutic strategies.
Dr Angela Wood
Dr Wood is a Lecturer in Biostatistics at the University of Cambridge.
Dr Wood‘s research interests are centered on the development and application of statistical methods for advancing epidemiological research. She has focused on developing statistical methodology for handling measurement error, using repeated measures of risk factors, missing data problems, multiple imputation, risk prediction and meta-analysis.