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I’m a curious researcher passionate about untangling the complexity of our inner experiences and the intricate world around us, all with the aim of enhancing our health and well-being. My interests span multiple levels:

• Methodologically, I focus on applying explainable machine learning as a scientific tool to model complexity. Specifically, within the realm of public health and epidemiology, these tools hold the potential to uncover patterns, mechanisms, and dynamics of diseases arising from the interplay of diverse biopsychosocial factors.
• Academically, I have contributed to the fields of bioinformatics, immunology, and infectious diseases (mainly HIV and COVID-19). More recently, I have broadened my scope to explore how mental health and societal factors contribute to or moderate the onset of diseases.
• On a personal level, I strive to challenge and overcome limiting assumptions in statistics and biomedical research by integrating interdisciplinary insights from philosophy, cognitive and contemplative science, causal inference, artificial intelligence, and complexity science.
• Socially, I advocate for open-source and reproducible research. I also actively explore innovative formats for scientific communication and teaching to the general public.