Brian Schilder

Background

About Brian Schilder

Passionately pursuing transdisciplinary research to advance human health and knowledge.Throughout my career, I have focused on creating massively scalable computational frameworks to address some of the most complex problems in multiscale biology and precision medicine. In my PhD thesis, I developed in silico models to uncover the cell types-specific mechanisms underlying the genomic signatures of all diseases and traits in a joint latent space.Why? Because, in the parable of the blind men and the elephant, we can better understand the parts by looking at the whole. Modern AI/ML is remarkably well suited to modeling complex biological systems (e.g. non-linear genome-phenome relationships, multi-omic layers) so that we may better understand its parts (e.g. variant-specific effects on neurodegenerative disease risk). Holistic models also empower us to much more accurately predict how to fix biological systems when they break through data-driven diagnostics and drug discovery.I am also a relentless devotee of reproducibility as I believe it is a prerequisite for impactful and sustainable science. To this end, all of my manuscripts are now 100% reproducible with GitHub version control and directly embedded code. I also develop tools to make this as easy as possible for others.Additionally, in my role as Lead Data Scientist at 120/80 I offer data-driven consultation and business insights for the world's top companies in digital health, pharma and biotech.🧠🦠🧬🤖🩺