Dr. Ruodan Liu completed her Ph.D. in Mathematics at the University at Buffalo in 2024, focusing on network science, evolutionary dynamics, mathematical biology, and gender inequality.

Her dissertation made significant contributions in several key areas:

Epidemic Dynamics in Temporal Networks

Dr. Liu examined how the concurrency of edges—the simultaneous connections between nodes at specific times—affects the spread of epidemics within temporal networks. She developed Markovian temporal network models to study disease propagation, providing insights into how timing and network structure influence epidemic spread.

Evolutionary Dynamics on Hypergraphs

Extending her research to hypergraphs, which generalize traditional graphs by allowing multiple nodes to be connected by a single edge, Dr. Liu explored evolutionary dynamics within these structures. Her work focused on the fixation probability of various node types, offering analytical methods to model evolutionary behavior under diverse scenarios.

Gender Imbalance in Academia

Dr. Liu’s dissertation also addressed gender disparities in academia, particularly in East Asia. She analyzed systemic differences in academic careers, research output, and citation practices, shedding light on challenges faced by female academics in China, Japan, and South Korea.

Publications and Impact

Her research has been published in prestigious journals, including the European Journal of Applied Mathematics, the SIAM Journal on Applied Mathematics, and the Journal of Informetrics. Her work has garnered over 20 citations, reflecting its impact and relevance in the academic community.

Current Endeavors

Following her doctorate, Dr. Liu joined Santa Clara University as a postdoctoral fellow in the Department of Sociology. Her current research continues to explore network dynamics and gender studies, with a focus on gender differences in co-authorship across a global landscape.

Dr. Liu’s interdisciplinary approach, combining mathematical rigor with societal issues, positions her as a notable scholar in both network science and gender studies.