Tutorial 1

Title: Community Detection in Complex Networks.


Many natural and social systems organize as networks, a few well-known examples of networks include the animal brain, electrical power grids, the internet, online social networks such as Facebook and Twitter, the relationships between genes and diseases, collaboration and citation among scientists, trade among countries, as well as interactions between financial markets. Most of these networks have nontrivial structural properties, hence the name complex networks. Mathematical analysis of complex networks has led to many successes, such as improving our understanding of the human brain's working and developing novel intervention and vaccination strategies to stop the spreading of diseases.

Numerous biological, social, and technological networks have modular structures: networks that consist of modules of nodes called communities, where the connectivity is dense within these communities. We will learn various algorithms for detecting community structures in networks during this tutorial. We will use Python's NetworkX package and apply these algorithms to several real-world data sets.

Software: Python

Instructor: Nishant Malik, Assistant Professor, RIT

Tutorial 2

Title: An Introduction to Bayesian Thinking: Gaining Insights into a Theory that would not die!


 This two-parts tutorial will introduce its audience to the basic tenets of the Bayesian Statistics School of thought using both intuitive and practical hands-on demonstrations along with basic and historical facts around this theory that would not die.

Several examples of the spectacular success and appeal of the Bayesian approach to modeling will be provided to help the audience appreciate the breadth and depth of this way of tackling scientific problems.

Examples will be provided in R.

Software: R

Instructors: Dr. Gregory Babbitt (School of Life Sciences, RIT) and Dr. Ernest Fokoué (School of Mathematical Sciences, RIT)