As technology evolves at an exponential rate, so too must our approach to diversity, equity, inclusion, and belonging (DEIB) within the digital landscape. Women, particularly women of color, remain significantly underrepresented in tech and face unique challenges in accessing opportunities for advancement, leadership, and meaningful contribution. This presentation will address key DEIB issues within the tech industry, focusing on how inequities persist and how the digital age offers both challenges and opportunities to combat them.

Our Instructional Design and Technology team was tasked with tracking learning objective achievement in Extension courses to support WASC accreditation. Canvas offers an "Outcomes" feature, but WASC also requires instructors to reflect on how their course design supports student success. While staff could help instructors brainstorm improvements, a more scalable method was needed.

Have you ever needed to leverage enterprise systems to streamline answering challenging operational questions?

Spark is a student-centered web application combining rich student data, Canvas data, and student support resources into a secure, personalized dashboard aimed squarely at helping students succeed in their desired academic pathways. In this session, we will explain - how we architected Spark in C# .NET to be campus-portable - how we utilized the Unizin Data Platform to integrate and normalize data from the SIS and LMS. - how we built the Canvas LTI integration - challenges and solutions - our AWS Cloud infrastructure - our plans for the future and increasing scalability

UCLA Health has embarked on an ambitious multi-year effort to replace an extensive collection of fragmented legacy data platforms with a unified cloud-based analytics infrastructure powered by Microsoft Fabric. In this session, technical and strategic leaders from UCLA and Microsoft will detail how this transition is fundamentally reshaping the organization’s data capabilities—enhancing data performance, minimizing redundancy, and significantly accelerating analytical insights.

UCLA Center for Research in Engineering, Media, and Performance and Advanced Research Computing have collaborated to build a set of cloud-based AI inferencing microservices to support an immersive live production of the musical Xanadu. During the production, audience members interact with the production using an app on their mobile phones that allows them to draw images in the air with cell-phone gestures. Those images are then sent to the AI microservices for real-time inferencing. The inferences are then displayed as 3D meshes on stage-based digital panels during the performance.

RSS has developed an innovative AI-driven, no-code, cloud-based platform to revolutionize digital transformation, addressing compliance requirements like accessibility and cyber security. The platform combines no-code development, AI automation, predictive analytics, and natural language data querying. It enables rapid development of web and native mobile solutions.

The project represents a significant achievement in modernizing the university's financial infrastructure. This panel discussion will delve into the critical lessons learned from this complex, multi-year initiative, focusing on both the technical and functional aspects of the project. Featuring representatives from both the technical and functional teams, the panel will explore the challenges faced, the innovative solutions implemented, and the long-term impact of this transformation.

In this session, we will share our experience migrating our Learning Management System from on-premises to AWS. We will walk through designing a scalable and secure cloud infrastructure using CloudFormation, automating deployments with AWS CodePipeline, orchestrating containers with ECS Fargate, and managing databases with Amazon RDS. The migration process involved moving code via GitHub, files with AWS DataSync, and the database through DBMS export/import.

The UC Residency Determination Project addresses the challenge of accurately determining residency status for incoming undergraduate students. This session will explore how we've implemented an AI-powered solution using Google's Gemini to analyze student documents and answer administrative questions, significantly streamlining the process. We'll discuss the project's architecture, the benefits of AI in handling large volumes of personal data, and the impact on efficiency, accuracy, and student experience.