I am a multidisciplinary research scientist working across evolutionary biology, systems biology, physiology, metabolism, neuroscience, biochemistry, epidemiology, data analytics, and the philosophy of science. My research is particularly concerned with the relationship between empirical observation, causal and mechanistic explanation, mathematical representation, epistemology, and scientific realism.
I work as part of a three-person research team with Dr James Barkell (University of Sydney) and Silas Njiru John (Jomo Kenyatta University of Agriculture and Technology). Together, we lead the Evolutionary Frameworks Project, a multidisciplinary research programme comprising twenty-five interconnected framework models spanning human biology, evolutionary science, physiology, metabolism, neuroscience, cognition, epistemology, institutional systems, human adaptation, and human flourishing.
Our work combines conceptual and mechanistic modelling, evidence synthesis, biostatistics, mathematical reasoning, and open-science methodology. Components of the programme are formally registered or archived through OSF and Zenodo, contributing to a developing open-science research record.
My broader work examines how scientific knowledge is constructed, tested, revised, and translated into explanatory models. I am particularly interested in the distinction between observation, association, causation, mechanism, and mathematical constraint; the epistemic limits of conventional evidence classification; the role of ontology and scientific realism in scientific explanation; and the development of integrative models capable of connecting evidence across biological and organisational scales.
My scientific approach emphasises empirical accountability, logical and mathematical consistency, mechanistic explanation, transparent uncertainty, competing-model evaluation, falsifiability, and openness to revision. The objective is not to defend predetermined conclusions, but to construct models that make their assumptions explicit, generate discriminating predictions, and remain answerable to observable reality.
