Raquel Iniesta is a Senior Lecturer in Statistical Learning for Precision Medicine, and leads the Fair Machine Learning and TDA lab at the Department of Biostatistics and Health Informatics.

Raquel’s academic background is in mathematics, statistics and Machine Learning. Her research interests have covered the development of novel models based on Machine Learning and Topological Data Analysis to allow for precision medicine, with main works on treatment personalisation for Depression and Hypertension. Raquel is an active and dedicated lecturer, and leads and teaches regularly in introductory and advanced courses on statistics and Machine Learning for MSc and PhD students, in UK and abroad.

After many years of experience as a researcher and lecturer, she realised the need for bringing back the human to the scene of personalised medicine and put a research focus on identifying the main ethical underpinnings of integrating Machine Learning models in medicine.

Currently, her work combines the development and application of Machine Learning models for precision medicine, the investigation of methodologies that can aid in the building of transparent, fair, non-biased and non-discriminatory Machine Learning and AI models , and the investigation of the key role that human agents —clinicians, developers, patients— have towards enabling an ethical development, implementation and use of AI-based models in healthcare.

Her research interests are:

• Machine Learning for Precision Medicine
• Ethics of AI 
• Topological Data Analysis