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

Dr Iniesta'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.

Research interests

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

Teaching

Dr Iniesta leads the Machine Learning module and is deputy lead of the Prediction Modelling module at the MSc in Applied Statistical Modelling and Health Informatics.

She also leads the Introduction to Statistics MSc central program and is the PhD Admissions tutor for the Biostatistics and Health Informatics department.

Expertise and public engagement

Dr Iniesta is an active disseminator of research and teaching outputs from the BHI department. Aside from having worked in radio for years, she followed training on Scientific Communication at the University of Bristol and on video journalism at the University of Arts London. She works as the Scientific Communicator from the Prediction Modelling group, for which she designed a website, writes web content, manage social media accounts and produces video interviews to advertise teaching programs and leverage the public profile of the research group. She is also producing video interviews for other research groups and courses at the BHI, that are used at King’s site and in social media to promote the department.