معرفی
Dr. Alex Brown serves as Associate Professor (Late Medieval and Early Modern British History) in the Department of History at Durham University's Faculty of Arts and Humanities. He holds additional fellowships at the Durham Research Methods Centre and the Institute for Medical Humanities, with active research projects spanning medieval social structures, economic history, and epidemiological modeling.
Research interests focus on three interconnected domains: (1) The economic and social history of rural England across medieval and early modern periods, particularly path dependency in land tenure systems; (2) The psychology of social mobility, challenging narratives of medieval ambition through analysis of downward mobility fears tied to gender and life-cycle events; (3) Institutional memory practices, examining how entities like Durham Priory manipulated historical records during boundary disputes and enclosure conflicts. His work integrates archival analysis with computational modeling techniques.
Recent publications reveal an evolving trajectory from agrarian economic studies toward interdisciplinary investigations of social psychology and memory. The 15 most recent articles demonstrate increasing engagement with medical humanities and collaborative methodologies, culminating in the current Leverhulme Trust-funded Black Death modeling project that bridges history, archaeology, and physics.
A dedicated supervisor, Brown has guided seven PhD students to completion since 2022, with research spanning coastal risk management (Hibberts 2025), vagrancy law (Hamilton 2025), monastic reform (Irvine 2023), and priory agricultural management (Wicklund 2022). He currently supervises three additional doctoral candidates examining archival practices, community crises, and food systems.
Brown leads the 2024-2027 Leverhulme Trust project 'Modelling the Black Death and Social Connectivity in Medieval England,' directing a cross-disciplinary team of archaeologists, physicists, and postdoctoral researchers. This initiative utilizes pandemic response modeling frameworks to simulate disease spread through reconstructed medieval populations, examining how social networks influenced epidemic trajectories.
