Daniel Herman

Daniel Herman serves as Assistant Professor of Pathology and Laboratory Medicine, leveraging dual MD/PhD training to advance cardiovascular diagnostics through computational approaches. His academic foundation includes medical and doctoral degrees, though specific institutions remain unreported in available materials.

Research focuses on biomedical informatics and machine learning applications for cardiovascular disease prediction, with emphasis on hypertension phenotyping, endocrine disorder screening, and electronic health record optimization. Key interests span computable phenotypes for treatment-resistant hypertension, racial equity in prenatal neural tube defect screening, and AI oversight frameworks for clinical laboratories.

Recent publications (2023-2025) reveal concentrated work on EHR-based diagnostic algorithms, particularly for primary aldosteronism and systemic mastocytosis, alongside critical analyses of race-based adjustments in medical screening. His PREDICT-SM project develops machine learning tools for rare disease detection while the CIRCE initiative establishes clinically validated research data infrastructure.

No scientific awards were documented in source materials.

While no student mentorship or grant activities were specified, his CIRCE project involvement indicates collaborative work in clinical data standardization. Current research trajectories suggest continued innovation in AI-driven diagnostic validation and health equity in laboratory medicine.