Ryan Ju
Data analyst at UA, specializing in data modeling, automation, and analytics integration. With experience in data engineering and business intelligence, he develops efficient workflows that bridge data systems and enhance reporting accuracy. Ryan is passionate about leveraging emerging technologies to make complex data more accessible and actionable for decision-makers across the university. His recent work focuses on applying LLMs and GenAI tools to improve productivity and insight generation.
Sessions in which Ryan Ju participates
Wednesday 25 February, 2026
In this session, I’ll share how I used local large language models (Llamma, Qwen, and GPT) to automatically summarize prospect interaction records. The goal was to keep data secure while making it easier to understand and analyze. I’ll show how the models were tested and refined using both AI and human feedback, and how the final process was automated with Python and Windows Task Scheduler. The results feed directly into Power BI dashboards, turning complex text data into clear, actionable in...
Sessions in which Ryan Ju attends
Tuesday 24 February, 2026
Wednesday 25 February, 2026
Walkthrough of Copilot, our institutional tool that ensures data security. We'll quickly explore how to access it, use it, and build with it.
If you’d rather not share your data with mysterious servers in faraway places, this session might be for you. We’ll start by clarifying what “open source” means and highlighting a few open-source LLMs. You’ll learn the key differences between locally run and cloud-based models, and why those differences matter for privacy and security. Then we’ll introduce Ollama, a simple tool that makes it easy to download small language models and run them locally. We’ll wrap up with a step-by-step demo on...
I propose a hands-on exploration of AI tools for transcription and translation paired with a dynamic conversation about the function, efficacy, and ethical implications of these technologies. By combining demonstration and practice with reflective discussion, this workshop will both empower participants to effectively engage with AI tools for transcription and translation and contribute to broader efforts to establish whether and how these tools should be deployed for research and productivit...
This session explores Concordia’s guidelines surrounding generative AI, including the importance of Privacy Impact Assessments (PIAs), data classification, and contractual safeguards when using platforms like Microsoft Copilot or ChatGPT. Learn practical strategies to prevent unauthorized data use, comply with Quebec’s privacy laws, and align with Concordia policies and guidelines. Gain actionable insights to balance innovation with ethical, secure practices—empowering the community to use AI...
This session explores how to build custom Google Gemini Gems tailored to your unique needs. You will learn how to personalize an AI assistant using persona, task, context, and output-format parameters to deliver accurate, situation-aware responses. We will also compare this recent approach with earlier technologies such as the IBM Watson Assistant used to build an Activity Recommender in Moodle LMS, to highlight improvements in flexibility and ease of integration.
Presentation of 3 o 4 scenarios pertaining to data privacy. Presenters will share tools and tactics to protect privacy.