Colloquium Archive

Probabilistic Methods in Computer Science, Data Science, and Public Health

Aravind Srinivasan
Professor of Computer Science
University of Maryland, College Park, MD


Probabilistic methods---randomized algorithms as well as the stochastic modeling of inputs to a problem---play a fundamental role in computer science and data science. We will discuss some of their foundational aspects, along with key applications in public health and in the Internet economy. We will cover aspects including the Lovasz Local Lemma, data science in E-commerce and Internet advertising, fairness, and probabilistic methods in the control of infectious diseases. The talk will be accessible to a broad computer-science audience.

eXtended Reality 101: What is it and how to get started

Sara Kassis
Department of Engineering Science, Sonoma State University


Are you interested in VR, AR, or even MR? Perhaps you've already played a game in one of these realms, but what does it take to develop content? In this talk, we will explore the continuum that makes up eXtended Reality, learn about the various gaming engines you can use, and hear about the resources available to you at SSU to make it all happen.

Performance analysis and optimization of C++ standard libraries

Aditya Kumar


C++ standard libraries are some of the most widely running system libraries on consumer devices and server machines. Most engineers assume the performance of standard libraries are as good as it can be. Contrary to the popular opinion there are several performance issues in these libraries as I will show with concrete examples. I will discuss optimizations that were incorporated into open source libc++ and libstdc++ in recent years so these issues could very well be in other libraries. I will discuss how to pick the right data structures based on actual use cases rather than relying on orthodox knowledge. Lastly I will present a systematic analysis of widely used data structures and algorithms of C++ standard libraries.

Social Network Analysis and its broader applications

Ayat Hatem
Assistant Professor
Dept of Computer Science, CSU Stanislaus


Social Network Analysis (SNA) is the process of analyzing structures in social networks using graph theory techniques.  It does not only span social networks but also covers different types of networks such as, biological, disease, and transportation networks. Networks help us visualize data and extract relationships between the different data points.  
In this talk, we will cover the basics of the SNA field. We will talk about the different tools existing, e.g., networkx and gephi, to analyze the different networks and which one to use for which situation. Afterwards, we will go over an example for how information could be extracted using SNA techniques. Additionally, we will cover biological networks analysis and the similarities between the social and biological networks.  

The Fractal Beauty of Compound Symmetry Groups

Bob Hearn
Founder & CEO
H3 Labs


Symmetry is at the heart of much of mathematics, physics, and art. We generalize the traditional mathematical notion of geometric symmetry by considering groups generated by distinct but overlapping isometries. In particular, we look at the groups generated by discrete rotations of overlapping disks. This yields a rich trove of new, intricate mathematical patterns, containing symmetries that are classically forbidden, as well as a new family of fractals. There is also a connection to quasicrystals, and a surprising connection to puzzles.

I’ll formally introduce compound symmetry groups, show what is known and what remains mysterious about them, and discuss the algorithmic techniques needed to generate the images and probe the dynamics behind the fractal transitions.

This is joint work with Brandon Enright, William Kretschmer, Tomas Rokicki, Ben Streeter, and Eric Vergo.