Dr Beatriz Galindo-Prieto is currently a researcher in the Epidemiology and Biostatistics Department of the School of Public Health (Faculty of Medicine, Imperial College London). She is specialized in developing and applying methods for multiblock/multi-omics and multivariate data analysis, dimensionality reduction of big data, variable/feature selection, and a diverse range of statistical methods for interpretation, prediction and classification of data (including methodologies based on projections to latent structures such as PLS models). She is currently working on epidemiological exposure machine learning models using environmental and omics large datasets to assess the health impacts due to the exposure to specific pollutants. She is an interdisciplinary researcher joining computer science, chemometrics/machine learning, mathematics/statistics, medicine/system biology, and public health policy.

Background/Experience: She obtained her MSc degree in Chemistry at the University of the Balearic Islands (Mallorca, Spain), and her PhD in Chemometrics, with emphasis in method development for multivariate and multiblock data analysis at Umeå University (Umeå, Sweden). She combined her research activity with teaching in courses for undergraduate, MSc and PhD students. Her main contribution from her PhD was the development and implementation of three novel algorithms/methods for variable selection in medium and large datasets with applications in analytical chemistry, spectroscopy, imaging, systems biology, medicine, epidemics, and industrial processes, among other. After defending her PhD thesis (entitled “Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi- block variable selection: VIPOPLS, VIPO2PLS, and MB-VIOP methods”) in February 2017, she decided to move to Trondheim (Norway) for extending her skills and exploring new perspectives in machine learning for big data as postdoctoral fellow (ERCIM funded, Big Data Cybernetics project) at the Department of Engineering Cybernetics of NTNU, in combination with lecturing in undergraduate and MSc courses and co-supervising MSc students. In 2019, she accepted a position as post-doctoral associate at Weill Cornell Medical College (New York, USA) where she worked on implementing methodologies for better understanding brain and serum big data related to metabolomics, transcriptomics and cognition in the neuroscience field (Alzheimer’s disease). In 2021, she decided to move to London (United Kingdom) to join Imperial College London as research associate and apply her computer science skills to real-time modelling and forecast/prediction using epidemiological data related to Ebola and COVID-19 outbreaks. She recently worked at ERG on developing methodologies and using bioinformatics for analysing the effects of air pollution on people's health.

She is interested in research topics that can generate positive and high impact in our society, and she enjoys working in interdisciplinary collaborations when the opportunity arises. Some of the projects in which she is involved relate to the development and the improvement of statistical methods and multidimensional visualizations for data exploration, including methods based on projections to latent structures such as partial least squares regression (PLS-R), data fusion, chemistry and systems biology.