AWM Upcoming Events Announcement
On behalf of the Association for Women in Mathematics, we are excited to share three more opportunities coming up next week. I would appreciate it if you could share this with your students! Flyers for all three events are attached below.
On Tuesday, November 12th, Graduate Students Gillian Carr and Wanchen Zhao will each be giving a talk, starting at 4:00 P.M. in the Computer Science/Engineering Building, Room E222. Gillian will be presenting on "Image analysis and agent-based modeling of tumor-immune interactions in the glioblastoma microenvironment", and Wanchen's topic is "Persistent Homology, Measure Theory, and Vectorization". The abstracts for both talks are found below, along with a flyer! We hope to see you all there!
Additionally, we are having our "Mentor Men-tea" event on Wednesday, November 13th at 4:00 P.M. for all participants in our Mentorship Program! Come with your mentor or mentee for some tea, snacks, and socialization! This is a great way for you to continue connecting with other people in the program, as well!
We are also excited to announce a joint Graduate School Panel with UMS, happening on Friday, November 15th at 5 P.M. in Little Hall 225. The panel will feature current graduate students and Dr. Michael Jury, the Associate Chair and Graduate Coordinator of the Department of Mathematics (https://people.clas.ufl.edu/mjury/). If you have questions about pursuing a graduate degree in mathematics, we strongly encourage you to attend!
If anyone would like to know more about the club, feel free to visit our website, https://awm.math.ufl.edu, contact our AWM Vice President Iman Kazmi (kazmib@ufl.edu), or our two faculty sponsors, Dr. Pollock (s.pollock@ufl.edu) and Dr. Christodoulopoulou (kchristod@ufl.edu). Thank you!
Best,
Jada Sitchler
Secretary, The
Association for Women in Mathematics
Abstract for Gillian Carr: Glioblastoma is a highly aggressive and deadly
brain cancer with no current treatment options available that can achieve
remission. One potential explanation for minimally effective treatments is the
ability of gliomas to take advantage of processes within the body's immune
system to infiltrate the tumor microenvironment with myeloid-derived suppressor
cells (MDSCs). These cells hinder the ability of T cells to effectively destroy
tumor cells. In this work, we create an agent-based model to simulate the
interactions of these three cell types. Our model also includes three
substrates to influence cell behavior: oxygen, a T cell chemoattractant, and an
MDSC chemoattractant. To validate our model and quantify cell clustering
patterns in glioblastoma, we use pair and cross-pair correlation functions (PCF
and cross-PCF, respectively) on location data for each cell type. This data is
extracted from cross-sectional tumor images of cellular biomarkers using image
analysis techniques. We then conduct the same PCF and cross-PCF analysis on the
cell location outputs from parameter testing trials of our ABM. These methods
allow us to fit a linear model using the least squares method to the tumor
image analysis results and evaluate how well the tests match the experimental
data. From these results, we use our model to identify the impact of our tested
parameters on cell clustering behavior within the glioblastoma tumor and its
microenvironment.
Abstract
for Wanchen Zhao: In topological data
analysis (TDA), a persistence diagram is a summary of the topological features
of data, obtained by applying homology to a filtration of chain complexes built
from the dataset. Each point in the diagram represents the lifespan of a topological
feature, or in mathematical terms, a homology class. In applications, in order
to apply methods in machine learning and statistics, one usually converts
persistence diagrams into a vector summary, such as the persistence landscape.
In this talk, we will explore each step of the TDA pipeline and consider the
limiting behavior of the persistence diagram as the size and complexity of the
data increases. I will define a generalized notion of persistence landscapes
for persistence measures and show that this construction is 1-Lipschitz and
invertible.
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