Asentewaa is a PhD researcher at the University of Edinburgh, focusing on causal inference using semiparametric methods to analyse observational healthcare data. She specialises in addressing non-ignorable missingness to enhance healthcare decision-making with robust, data-driven models.

Akchunya, a first-year PhD student at King's College London and Imperial College London, focuses on Causal Representation Learning and Explainability. His research addresses data and concept drift using continual learning to improve cross-task generalisability, exploring causal relationships through Actual Causality theory.

Panagiotis (Pano) is a CHAI Postdoctoral Scholar at the University of Edinburgh, specialising in deep learning for medical imaging under Prof. Sotirios Tsaftaris. His PhD emphasised efficient vision models and Bayesian uncertainty, aiming to improve robustness and calibration in histopathology applications.

Damian is one of CHAI’s Postdoctoral Scholars, currently based at the University of Edinburgh. He is a Computer Scientist and a former Software Developer. His main research interests include treatment effect estimation and causal graph learning from observational data, but also robustness to data shifts, hyperparameters, and performance evaluation.

David, who transitioned from musician and stone carver to academia, completed his PhD at UCL in Information Theory applications in Software Engineering. He now works with Hana Chockler's team at UCL, leading design and development of the AI explanation tool, ReX, focusing on causal explainability of black-box AI models.

Nathan, with 12 years of A&E nursing experience, now applies his expertise in computational biology, focusing on causal reasoning to interpret AI outputs in clinical settings. He collaborates with clinicians, utilising data from neuro-imaging to vibrational spectroscopy for cancer prognosis. Follow his blog for more insights.

Steph is a Postdoctoral Research Associate at the University of Manchester, specialising in statistical methodologies for healthcare prediction models. Using AI, she focuses on causal inference, through which she deciphers complex healthcare data to improve prediction accuracy.

Fabio has recently concentrated on exploring new theoretical identifiability results for deep latent variable models, crucial for reliable representation learning and causal inference. Identifiability helps discern if model estimates reflect true causal relationships or are influenced by confounding factors.

James is an interdisciplinary researcher with a focus on biological variation in translational research. He has worked on projects at the University of Edinburgh and the University of Exeter, investigating scientific practices in genomics, pharmacology, transplantation biology, and agriculture.

Kurt earned his B.E. in Electrical Engineering and Mathematics in 2019 and recently completed his PhD at Stony Brook University, NY. His research covers causal discovery, explainable machine learning, anomaly detection, and their applications in clinical neuroscience, incorporating elements of Bayesian inference, differential geometry, topology, and dynamical systems theory.

Alex is a PDRA at the University of Manchester with expertise in methodology for clinical prediction models (CPMs).

Area of interest include quantifying uncertainty, evaluating the performance of CPMs, and combining CPMs with causal inference to answer questions which can better inform patient care.

Analía is a PhD student at the University of Edinburgh, supervised by Professor Sotirios Tsaftaris.

She is a mathematician, and her research focuses on developing and analysing causal digital twins in healthcare. She specializes in integrating causality into AI-driven digital twins that serve as proxies for cyberphysical twins and into fully AI-driven digital twins for complex processes, aiming to enhance their efficiency and accuracy.