CS Colloquium

Explainability helps people understand and interact with the systems that make decisions and inferences about them. This should go beyond providing explanations at the moment of a decision; rather, explainability is best served when information about AI is incorporated into the entire user journey and AI literacy is built continuously throughout a person's life. We share resources that can be used in both industrial and academic environments to encourage AI practitioners to think more broadly about what explanations can look like across products and ways to provide people with a solid foundation that helps them better understand AI systems and decisions.

For most, the reason for obtaining a computer science degree is to ultimately pursue a career as a software engineer. While there is inevitably a great deal of programming involved in a computer science program, students may still end up anxious about whether what they’re learning is actually useful or not. In this talk I will explain that the answer is an unequivocal “Yes”. The foundational knowledge created through studying computer science is invaluable in a career as a software engineer. Through personal examples from working as an engineer at a AAA game developer, I will show how theoretical knowledge translates to concrete skills and shine a spotlight on parts of computer science that are significantly more important than most students might realize. I will also talk about important skills for software engineers that aren’t directly related to computer science.

From its earliest days, computing has been an eclectic project of capitalism, war, colonialism, and white supremacy. Its central Western values of utility, efficiency, rationality, and mathematical beauty have enabled sweeping changes to culture and communication, but also amplified some of the worst parts of these oppressive systems. At the heart of many of these forces are programming languages, which deeply embed many assumptions about their users: English fluency, normative ability, and a devotion to speed. These assumptions create a culture of computing that structurally excludes vast parts of humanity from participating. In this talk, I describe some of my nascent efforts to design the opposite: programming language that seeks to be global, accessible, playful, and simple, embracing all of humanity’s natural languages and abilities, while trading computing’s devotion to efficiency for simplicity and silliness. Throughout, I’ll provide demonstrations of these gestures toward programming language justice, pointing to alternative visions for how we might make with computing and who might do the making.

Why are the Physics and Astronomy majors coding? Isn't that what computer science majors do? How is coding different in Computer Science, Engineering, and Science? If there are so many differences, how should we teach coding in the K-12 classrooms? This talk answers these questions and brings up more questions. Specifically, the presentation will include examples of how coding in various careers can differ and/or be similar. This presentation also showcases what the EdEon STEM Learning Department is doing to address several challenges to teaching coding in middle and high school science classrooms. 

Short presentations of research carried out by Sonoma State Computer Science Students, and CS awards

1. Tony Rago
2. Obinna Kalu
3. Adam Said