Microorganisms have evolved to thrive in fluids that exhibit non-Newtonian behaviors. However, the intricacy of the relevant non-linear fluid responses has hindered our understanding of the interaction between complex environments and swimming. Here, we propose a model for two experimental systems that show improved swimmer performance in a complex medium: helical swimming in a suspension and rheotaxis in a shear-thinning fluid. In both cases, a mix of theoretical arguments and simulations explains the enhancement in swimming efficiency.
February 22
Yariv Aizenbud, Yale University
Host: Kristina Wicke
Recovering Tree Models via Spectral Graph Theory
Modelling data by latent tree models is a powerful approach in multiple applications. A canonical example of this setting is the "tree of life", where the evolutionary history of a set of organisms is inferred by their DNA. Generally, in latent tree models, the main task is to infer the structure of the tree, given only observations of its terminal nodes. While inferring a tree structure is a common task, in many applications, a robust algorithm for the recovery of large trees is still missing.
In this talk, we will see a new method for the recovery of latent tree models, which is based on spectral graph theory. We show that the hidden tree structure is strongly related to the spectral properties of a graph, defined over the terminal nodes of the tree. Finally, we see that while in terms of accuracy the method performs similarly to state-of-the-art methods, it is significantly more computationally efficient.
March 8
Nessy Tania, Senior Principal Scientist, Quantitative Systems Pharmacology, Pfizer Worldwide Research, Development, and Medical
Host: Casey Diekman
Shaping Your Own Career as a Mathematical Biologist
In this talk, I will share some of my personal journey as a math biologist and applied mathematician who had pursued a tenure-track position in academia and is now working as a research scientist in the biopharma industry. I will discuss similarities and differences, rewards and challenges that I have encountered in both positions. On a more practical aspect, I will discuss how current trainees can prepare for a career in industry (specifically biopharma) and how to seek those opportunities. I will also describe the emerging field of Quantitative Systems Pharmacology (QSP): its deep root in mathematical biology and how it is currently shaping the drug development process. Finally, I will share some of my own ongoing work as a QSP modeler who is supporting the Rare Disease Research Unit at Pfizer. As a key takeaway, I hope to share that there are multiple paths to success and a rewarding and stimulating career in applied mathematics.