Colloquium Archive

How Collaborative is the California Collaboration Network?

Theresa Migler
Dept. of Computer Science, Cal Poly State University, San Luis Obispo

11/08/2022

Understanding how people work together is the first step towards creating a more equitable environment. In this talk we will present the construction and analysis of the California Collaboration Network. In this network vertices represent University of California and California State University researchers along with their collaborators. Two researchers are connected if they have ever published a paper together. We will discuss preliminary findings about this network with respect to the gender and ethnicity of the researchers.

Build Your Own Game Engine

Scott Gordon
Dept. of Computer Science, Sacramento State University

11/15/2022

Video games today are built using engines such as Unity, Unreal, Lumberyard, CryEngine, and dozens of others.  The engine handles the basic tasks common to all games: 3D real-time rendering, object and scene-graph management, lighting, cameras, animation, etc.  Perhaps you've used an engine to make your own game, but have you ever thought of building your own engine?  It's a fun and challenging project that appeals to aspiring hard-core coders.  Dr. Scott Gordon is a professor at Sacramento State University, where students in his Game Architecture course build video games atop his own game engine "TAGE".  But a few of his most ambitious students opt instead to first build their own engine from scratch.  In this talk, Dr. Gordon describes how game engines are organized, and how to build your own.

Non-invasive blood glucose monitoring using breath volatile organic compounds

Sudhir Shresta
Dept. of Engineering Science, Sonoma State University

11/22/2022

We are developing a smart breath glycemia reader (BGR), a breathalyzer-type hand-held device, that can predict blood-glucose levels from human breath. The device has a microcontroller, volatile organic compound (VOC) sensors, a rechargeable battery, and a wireless chip. We are currently collecting data from patients with type-2 diabetes. We aim to use the data to train machine learning (ML) models and implement into the device for real-time glycemia predictions. Diabetes is a major health problem in the United States that affects more than 122 million people. It requires continual management of blood glucose (BG) to avoid acute and long-term complications, yet, about half of patients with type-2 diabetes do not adhere to their BG treatment plan. Our device, when fully developed, will give patients an accessible modality to read the glycemic status without having to prick their fingers and allow them to test as many times as they desire and easily track their BG history. This will help address the nonadherence and improve BG management among patients with type-2 diabetes.

Fall 2022 Short Presentations Of Student Research

11/29/2022

Short presentations of research carried out by Sonoma State Computer Science Students.

Fall 2022 Short Presentations Of Student Research

11/29/2022

Short presentations of research carried out by Sonoma State Computer Science Students.

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