Join us every other week as we dive into data science over coffee and cookies!  

Coffee + Data && Code is a casual event that brings together curious minds to explore interesting projects and questions in the world of data analysis and statistics at UO. Each session features a talk or conversation on research, a project, or a topic in the data world. We encourage discussion and questions.

This is an excellent opportunity to network with other curious individuals, whether you are a student, faculty member, or staff member with a passion for learning and data. Discussions will likely continue during Coding Circle, which immediately follows Coffee & Code in the same space. Feel free to register for the entire term but only join for topics that interest you.

We will be meeting in-person in the sofa area of the DREAM Lab in Knight Library. Coffee and cookies will be provided. Attendees will also have the option to join via Zoom. 

January 10th: Hannah Waight on AI and State Propaganda

Sociology Professor Hannah Waight will be giving a talk on the influence of propaganda on large language models (LLMs) in the context of Chinese state media. Her research suggests there may be strategic incentives for states and other actors to increase the prevalence of propaganda in the future as generative AI becomes more ubiquitous.

January 24th: Sheldon Sandbekkhaug on Software Engineering for Aspiring Developers

Applying to jobs or internships in the tech industry? Staff Software Engineer Don Sandbekkhaug will lead a casual chat about what he's learned about software engineering from his nine years at Google and his advice for undergraduates entering or aspiring to the profession.

February 7th: Explore retrieval augmented LLMs with Benjamin Riesenberg

Curious about how LLMs can be improved? Join Librarian Benjamin Riesenberg and the Data Services team while we explore a Jupyter Notebook that demonstrates a Retrieval-Augmented Generation (RAG) pipeline. None of us are experts, so this will be a session where we read through the notebook together and try to make it work. The notebook we'll be exploring uses "the Ollama library for generating responses, ChromaDB for efficient vector-based retrieval, and Jinja for dynamic prompt templating. RAG combines the power of large language models with a vector database to enable dynamic and context-aware response generation based on external knowledge."