معرفی
Caitlin Lienkaemper is a Research Fellow at Boston University, affiliated with the Applied Mathematics and Dynamical Systems research groups. She holds an office in CDS 342 and can be reached at clienk@bu.edu. Her work bridges neuroscience, combinatorics, and dynamical systems, focusing on neural coding, network dynamics, and mathematical modeling of biological systems.
Education: She earned her Ph.D. in Mathematics from Penn State University in 2022, advised by Carina Curto. Her doctoral thesis, Combinatorial geometry of neural codes, neural data analysis, and neural networks, laid the foundation for her interdisciplinary research.
Research Interests: Lienkaemper’s research integrates algebraic and geometric tools with neuroscience, exploring topics such as oriented matroids in neural data analysis, inhibition-stabilized neural networks, and olfactory system dynamics. Her recent work emphasizes low-dimensional structure in nonlinear systems and combinatorial approaches to neural code analysis.
Publications: Her recent articles reflect a focus on neural network dynamics, olfaction models, and combinatorial methods. Key themes include mean-field theory, inhibition-driven network stability, and geometric interpretations of genetic interactions.
Lienkaemper has presented her research at conferences like the Graduate Student Combinatorics Conference (2021) and the Fields Institute’s New Mathematical Methods for Neuroscience (2020), highlighting her contributions to theoretical and applied neuroscience.
