Max Signaevsky

WORK HISTORY

EDUCATION

ABOUT MAX SIGNAEVSKY

I’m a physician–scientist and neuropathologist focused on integrating AI/ML in the digital pathology framework and on clinical–pathological–molecular/genetic correlations. I lead teams to close knowledge gaps, develop annotation standards with rigorous rubrics, and design domain-relevant tasks with gold-standard solutions. My work combines pathology, advanced imaging, and machine learning: I build and validate end-to-end pipelines that quantify regional neuropathology on whole-slide images (WSI) and link those readouts to molecular and genetic signals. In 2019, I published what is widely cited as one of the first peer-reviewed AI applications in neuropathology (Lab Investigation; nature.com/articles/s41••••••••24). I design end-to-end classifier pipelines and methodological frameworks, run QA/QC on ground-truth annotations—including F1-based ground-truth sample-size checks—and perform post-hoc QA/QC “reality checks” on model outputs against clinicopathologic expectations. I also mentor junior researchers who apply these standards in our projects and collaborate with geneticists to integrate tissue-anchored phenotypes with genomic signals. I develop annotation standards and evaluation frameworks that keep analysis clinically grounded—not just statistical—ensuring AI advances stay anchored in real clinical reasoning and human expertise.