Catalina Vallejos

Chancellor’s Fellow Catalina Vallejos, based at the University of Edinburgh’s MRC Human Genetics Unit, operates at what she describes as “the interface between Bayes and biomedicine.” In other words, her work brings the precise, analytical capability of statistics to bear on the messy and unpredictable realm of large scale data – specifically, in fast-growing biomedical research areas. The most prominent feature of biomedical data is its complexity, which comes from different sources of heterogeneity, or diversity in content. This diversity is found across individuals in a population, within the different types of data used, and from aspects of the data collection process such as measurement error. Dr Vallejos is inspired by the development of approaches to biomedical statistics that can be used to tackle real-life challenges, potentially leading to life-enhancing or even life-saving treatments and protocols. Dr Vallejos is also inspired by the ability to turn new methodologies into open-source tools that can be shared by the scientific community.

She leads the Biomedical Data Science research group at the MRC Human Genetics Unit, where she is engaged in producing new statistical methods with which to interrogate the various sources of heterogeneity in biomedical data. Dr Vallejos is also a Fellow at the Alan Turing Institute. Like so much of the work in the realm of Data-Driven Innovation, this is multi-disciplinary and complex, involving advanced biomedical problems and new technologies. Before taking up her Chancellor’s Fellowship, Dr Vallejos undertook post-doctoral research at the European Bioinformatics Institute of the European Molecular Biology Laboratory and at the MRC Biostatistics Unit. “My postdoc was an incredible opportunity to work with interesting people who are top experts in their field,” she told the EMBL-EBI website. “It opened my mind to new possibilities.”

Aiming to take those new possibilities even further, her team’s current research focuses on two areas. The first is single-cell RNA-sequencing, a ‘next-generation’ sequencing technology that can be used to identify rare cell populations, track the development of single cell lineage, and reveal how relationships between different genes are regulated. Her team is also working with electronic health records, exploring the development of predictive models that draw on routine observational data collected by health care providers, such as the NHS.