Marie-Francoise Roy: 11AM---> 11:20/11:30
Title: The Committee for Women in Mathematics of the International Union and its activities
Summary: I shall present the history and activities of the Committee for Women in Mathematics of the International Union with a focus on the Standing Committee for Gender Equality and the Gender Gap in Science project.
Hania Mahassen: 11:30--->12:00
Title: “Plasmon Dispersion in Strongly Coupled Coulomb Systems”
Abstract: A dielectric matrix is developed, and an analysis is presented on the plasmon dispersion in strongly coupled charged particle 2D and bilayers in the quantum domain.
Lama Tarsissi: 12:00--> 12:30
Title: Combinatorics on Words and its application in digital convexity.
Abstract: Combinatorics on words is related to several branches of mathematics and considered as an interesting topic of studies. In combinatorics on words, we can find many families of words over a finite alphabet. We have the family of Sturmian words that generates infinite words. The family of Christoffel words that is restricted to the family formed by finite Sturmian words. We will be interested also in a third family called family of Lyndon words. As an application, we will use some tools of Combinatorics on words to define the digital convexity which is one of the useful geometric properties of digital sets in digital image processing.
Coffee break ( 1h )
Linda Smail: 1:30---->2PM
Title: Bayesian Networks - Theory and Applications.
Bayesian networks are a network-based framework for representing and analysing models involving uncertainty. They are used by the artificial intelligence, decision analysis, and statistics communities. Bayesian networks are different from other probabilistic analysis tools because of the network representation of problems, the use of Bayesian statistics, and the interaction between them. Currently they are one of the most rapidly growing areas of research in computer science and statistics in the public and private sectors alike. Their popularity is due to their graphical representations which are easy to interpret and very useful in explaining complex models.
The talk will introduce Bayesian networks, the theory behind them, and will illustrate their use through examples of applications in different fields from medical diagnosis to education. Inference in Bayesian networks, which is a NP-Hard problem, will be presented along with the existing algorithms. Also, learning Bayesian networks from data will be presented
Elena Beretta: 2PM----> 2:30
Marwa Banna: 2:30--->3PM
Title: High-dimensional Probability with Applications to Big Data Sciences
Abstract: With the fast growth of data sciences, there was a dramatic surge of interest and activity over the past two decades in high-dimensional probability that provides vital methods and tools for a wide range of applications. High-dimensional probability is the area of probability theory that studies random objects in R^n, where the dimension n can be very large and where classical probabilistic tools are no longer sufficient. In this talk, I will give a glimpse on some of these applications and show how methods in high-dimensional probability provide a foundation for such advances.
United Arab Emirates