Bristi has recently started her PhD in epidemiology at the Julius Center at UMC Utrecht, Netherlands. She has graduated in cardiovascular biology of disease, which is a program in biomedical science. For the Hypermarker project, Bristi will study the association between nutrition and the effectiveness of antihypertensive drugs in patients with hypertension. In her free time, she loves to read, take walks in nature, and travel.

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Xin is a Research Fellow in Artificial Intelligence and Machine Learning at the Institute of Cancer and Genomic Sciences, University of Birmingham. He received his PhD degree from the Department of Computer Science, University of Warwick, and later worked as Data Scientist at Precisionlife Ltd and Research Associate in Machine Learning at the University of Newcastle upon Tyne before joining the University of Birmingham. Xin's research interests include Machine Learning, Recommender Systems, Activity Recognition, Wearable Computing, and Bioinformatics. In Hypermarker, he is working on data analysis and predictive modeling. 

Dr. Yuanwei is a research fellow in health data science at the University of Birmingham in the UK. His research interest lies in developing computational approaches and mathematical tools to understand complex systems in life science, uncover hidden patterns in biological data and networks, and ultimately identify disease biomarkers that lead to changes in strategy to improve human health. Dr. Xu is a data analyst and machine learning modeller for the Hypermarker project. In his spare time, he enjoys long-distance running.

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Jonathan, with a background in Pharmacy, furthered his expertise by completing a PhD focused on clinical research to optimise fluoropyrimidines treatments. He is now a an Assistant Professor at UMC Utrecht - Julius Centre of Health Sciences & Primary Care in The Netherlands. His work involves setting up and managing a clinical trial for the validation of Hypermarker clinical decision support model. Additionally, he will be working on the analysis and review of existing data regarding pharmacometabolomics and metabolomic modifiers of antihypertensive drugs. Jonathan is passionate about golf, enjoying the challenge of the game.

Dr. Animesh is an Assistant Professor of Integrative Analytics and Aritificial Intelligence, University of Birmingham, UK, and Deputy Programme Director, MSc in Health Data Science, University of Birmingham, Dubai. In the Hypermarker project, he will be leading multimodal analytics with clinical and metabolomics data using AI-based methods. His hobbies are swimming and collecting coins from different countries. 

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Elahe, with an academic background in epidemiology, data science, microbiology, and an MBA in health, has experience in disease surveillance, genetics, clinical research, machine learning, pattern recognition, and responsible AI. She is a PhD student at UMC Utrecht – Julius Centre of Health Sciences & Primary Care. In Hypermarker, Elahe's focus is on analysing and systematically reviewing data related to pharmacometabolomics and metabolic modifiers in antihypertensive drugs. Outside her research, Elahe is an avid adventurer, enjoying activities like running, cycling, and kickboxing.

Merys is currently pursuing her PhD at the Metabolomics and Analytics Centre, University of Leiden. In her role within Hypermarker, she conducts systematic reviews of data on pharmacometabolomics and metabolic modifiers of antihypertensive drugs. Merys also investigates observational data to reveal significant associations in these areas. She is committed to developing innovative disease models in metabolomics, aiming to make a substantial impact on patients' lives. When she is not engaged in research, Merys enjoys immersing herself in books and partaking in enjoyable activities with friends, such as visiting museums or trying out new restaurants.

Daniella Okyere is a PhD student at the University of Birmingham, with an academic background in Biomedical Science and Health Data Science. In Hypermarker, she will focus on using AI-based methods for multimodal analytics with metabolomics and clinical data. Daniella is passionate about harnessing the transformative potential of data science in healthcare with the goal of improving the lives of patients. In her spare time, she enjoys baking and travelling.