Summary
Sage Malingen is a research scientist with 9+ years applying computational modeling, data science, and machine learning to biophysical problems, spanning all-atom molecular dynamics, continuum-scale modeling, and ML pipelines. They have published six peer-reviewed papers, presented internationally, and delivered quantitative insights for an NIH center through simulations and enhanced sampling to probe protein function relevant to cardiac disease. Comfortable in Linux, Python, GitHub, Amber, Keras and xGBoost, Sage builds reproducible workflows, SOPs, and trains collaborators to use them. Their cross-disciplinary collaborations with bench scientists, clinicians, and engineers sharpen their ability to translate technical results for diverse teams and to teach using evidence-based practices. Currently developing biomechanics-focused MuJoCo models at the University of Washington, they pair deep technical rigor with a practical knack for identifying the core of hard problems and choosing the right tool to solve them.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (PhD) Biology, Doctor of Philosophy (PhD) Biology at University of Washington
Bachelor of Science (B.S.) Mathematics, Bachelor of Science (B.S.) Mathematics at North Dakota State University