Saul is an interdisciplinary researcher with a career spanning genomics, medicine, plant science, and demography. Coming from a background in medical science but a strong focus on statistics and methods, Dr Newman has a history of pursuing research questions outside of his field. This has led to disruption, but also a deeply enriching career path through academia.
Joining in the University of Oxford has given Saul the opportunity to work on intensive, broad-scale human data. He aims to use these data to disrupt accepted wisdom in ageing research: a field that he views as ‘currently more interested in pitching fundable ideas to wealthy investors, than it is in solving fundamental scientific problems’.
Dr Newman’s previous work involves theoretical solutions to the limits of human life, and an explanation of late-life mortality deceleration that has considerable cross-cultural predictive accuracy. This work illustrated that late-life ‘plateaus’ in mortality are caused by errors and, in some cases, rather curious statistical choices. In addition, Saul has worked with a broad spectrum of government, industry and academic stakeholders on diverse topics with a general focus on kin selection and networks, agronomy-via-AI, and the evolution of ageing. Saul has also made international news for debunking the concept of ‘blue zones’, and areas with high frequencies of people surviving past age 105, by highlighting such regions are predicted by an absence of birth and death certificates, higher old-age poverty rates, and (remarkably) a lower probability of reaching old age.
Dr Newman is currently welcoming the opportunity to collaborate, and is willing to consider students across a broad range of research fields ‘including and especially the interdisciplinary odd-bods’.
Dr Newman received his PhD from the Australian National University’s John Curtin School of Medical Research in 2015 under Prof Simon Easteal, before working as a plant scientist for the Australian government. Dr Newman then returning to academia through in a joint appointment as a senior postdoctoral fellow, working in both the Research School of Biology and the newly-founded Biological Data Science Institute at the Australian National University.
RESEARCH PAPERS AND COMMENTS
2021 S.J. Newman, R.T. Furbank. In Press. “Explainable machine learning models of major crop traits from satellite-monitored continent-wide field trial data.”
2021 S.J. Newman, R.T. Furbank. Scientific Data. “A Multiple Species, Continent-Wide, Million-Phenotype Agronomic Plant Database.” 8:116.
2020 S. Easteal et al. The American Journal of Human Genetics. “Equitable Expanded Carrier Screening Needs Indigenous Clinical and Population Genomic Data.” 102:2, pp.175-182
2020 S.J. Newman. bioRxiv. “Supercentenarian and remarkable age records exhibit patterns indicative of clerical errors and pension fraud.”
2019 S.J. Newman. The Lancet. “Ending government support for pro-alcohol research.” 393:10177, p.1200.
2019 M. Arcos-Burgos et al. Translational Psychiatry. “ADGRL3 (LPHN3) variants predict substance use disorder.” 9:42.
2018 S.J. Newman. PLOS Biology. “Errors as a primary cause of late-life mortality deceleration and plateaus”. 16:12, e2006776.
2018 S.J. Newman. PLOS Biology. “Plane inclinations: a critique of hypothesis and model choice in Barbi et al.” 16:12, e3000048.
2018 S.J. Newman. Science. “Unsupported model choices generate a plateau.” (eLetter).
2017 Voss-Fels et al. Molecular Plant. “VERNALIZATION1 modulates root system architecture in wheat and barley.” 11:1, pp.226-229.
2017 S.J. Newman, S. Easteal. bioRxiv. “Global patterns of human ageing.”
2017 S.J. Newman, S. Easteal. f1000Research. “The dynamic upper limit of human lifespan.” 6:569
2016 S.J. Newman et al. f1000Research. “Reproductive success is predicted by social dynamics and kinship in managed animal populations.” 5:870
2015 S.J. Newman, S. Easteal. PLOS one. “A new metric of inclusive fitness predicts the human mortality profile.” 10(1), e0117019.
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