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UL Lafayette Association for Women in Mathematics Chapter


The purpose of the Association for Women in Mathematics is to encourage women and girls to study and to have active careers in the mathematical sciences, and to promote equal opportunity and the equal treatment of women and girls in the mathematical sciences.

The UL Lafayette Chapter of the Association for Women in Mathematics is just getting off the ground this, Spring 2019, semester. We are developing a full roster of activities for undergraduate and graduate students, as well as faculty.

This semester, the AWM will organize four sponsored colloquia by prominent female mathematicians. Prior to each colloquium, we will host an informal luncheon for students to meet with the colloquium speaker and discuss topics such as research, work-life balance, and career development. Undergraduate math majors are encouraged to attend!

AWM Sponsored Colloquia and Luncheons - Spring 2019

Using Mathematics to Fight Cancer
Ami Radunskaya, Professor of Mathematics, Pomona College

14 February 2019
Oliver Hall Auditorium (room 112)

Ami Radunskaya


What can mathematics tell us about the treatment of cancer? In this talk I will present some of work that I have done in the modeling of tumor growth and treatment over the last fifteen years. Cancer is a myriad of individual diseases, with the common feature that an individual's own cells have become malignant. Thus, the treatment of cancer poses great challenges, since an attack must be mounted against cells that are nearly identical to normal cells. Mathematical models that describe tumor growth in tissue, the immune response, and the administration of different therapies can suggest treatment strategies that optimize treatment efficacy and minimize negative side-effects. However, the inherent complexity of the immune system and the spatial heterogeneity of human tissue gives rise to mathematical models that pose unique challenges for the mathematician. In this talk I will give a few examples of how mathematicians can work with clinicians and immunologists to understand the development of the disease and to design effective treatments. I will use mathematical tools from dynamical systems, optimal control and network analysis.
This talk is intended for a general math audience: no knowledge of biology will be assumed.

About the speaker

Ami Radunskaya is a professor of mathematics at Pomona College. Among her areas of expertise are mathematical modeling of tumor growth and treatment, dynamical systems and analysis of non-linear models of power systems. She is co-director of EDGE (Enhancing Diversity in Graduate Education) as well as the President of the EDGE Foundation. EDGE is a national program designed to increase the number of women students, particularly minority women, successfully completing graduate programs in the mathematical sciences.

As President of the Association for Women in Mathematics (AWM), one of her goals is to build community between all types of mathematicians by supporting a unified network of members, and by paying attention to the needs of all members. Aspiring mathematicians don’t all have equal access to research opportunities, graduate school, internships, post-docs, jobs and recognition. As AWM President, she will work within the association and with other groups to facilitate access to opportunities in mathematics.

Luncheon - From Music to Mathematics to Medicine: One Woman's Journey
Ami Radunskaya, Professor of Mathematics, Pomona College

Maxim Doucet Hall 206

I have always been fascinated by the relationship between pattern, prediction, order and disorder. Mathematics is the perfect language to describe the evolution of patterns, and music is a great way to listen to them. My first career as a musician led to a new career in mathematics, as I pursued patterns and prediction through the study of dynamical systems and ergodic theory. In more recent years, I have discovered that mathematical descriptions of physical processes can be used to address problems in medicine, such as understanding how cancer evolves, or developing treatment strategies. In this talk I will describe my own journey from musician to mathematician to modeler, highlighting this path with mathematical examples.
Note: There will be time for conversations and getting to know each other. Be a part of this UL Lafayette math community that supports and inspires girls and young women who love math. Men are welcome and encouraged to attend.
Contact Madi Angerdina to register for the luncheon.

Gini Distance Correlation and Feature Selection
Xin Dang, Professor of Mathematics, The University of Mississippi

21 March 2019

Dr. Xin Dang


Big data is becoming ubiquitous in the biological, engineering, geological and social sciences, as well as in government and public policy. Building an interpretable model is an effective way to extract information and to do prediction. However, this task becomes particularly challenging for the scenario of big data, which are large scale and ultra-high dimensional with mixed-type features being both structured and unstructured. A common practice in tackling this challenge is to reduce the number of features under consideration via feature selection by choosing a subset of features that are "relevant" and useful. The work in this talk aims at proposing a new dependence measure in feature selection. The features having strong dependence with the response variable are selected as candidate features. We proposes a new Gini correlation to measure dependence between categorical response and numerical feature variables. Compared with the existing dependence measures, the proposed one has both computational and statistical efficiency advantages that improve the feature selection procedure and therefore the resulting prediction model.

About the speaker

Xin Dang received a Bachelor of Science degree in Applied Mathematics from Chongqing University, China, in 1991, and Master's and PhD degrees, in 2003 and 2005, in Statistics from the University of Texas at Dallas. Currently she is a professor in the Department of Mathematics at the University of Mississippi. Her research interests include robust and nonparametric statistics, statistical and numerical computing, and multivariate data analysis. In particular, she has focused on data depth and applications, bioinformatics, machine learning, and robust procedure computation. Dr. Dang is a member of the IMS, ASA, ICSA and IEEE.

28 March 2019
Linda Allen, Texas Tech University

Dr. Linda Allen

2 May 2019
Jo Nelson, Rice University

Jo Nelson

Organizing Committee

  • Hayriye Gulbudak (Chair)
  • Robin Koytcheff
  • Yongli Sang
  • A.S. Vatsala
  • Amy Veprauskas
  • Madi Angerdina (Undergraduate Student Representative)
  • Srijana Ghimire (Graduate Student Representative)

Please direct any inquiries to Dr. Hayriye Gulbudak.