Raleigh AI, Machine Learning, and Computer Vision Meetup

In Through the Looking-Glass, Lewis Carroll envisioned a world where logic is inverted, rules shift unpredictably, and progress requires very strategic navigation. And if you have attempted to use an open source large language model within an enterprise setting then you have had first hand experience with unpredictable behavior. Today, one of the possible mitigation strategies to address this unpredictability is known AI agents or agentic. Software applications (or bots) that operate in a similarly dynamic landscape—processing vast amounts of unstructured data, collaborating in multi-agent workflows, and self-correcting in ever-changing environments.

Digital twins are powerful tools that create virtual representations of physical objects, systems, or processes, continuously updated with real-world data. In healthcare, they enable patient-specific modeling and simulation, advancing personalized medicine by improving diagnosis, treatment planning, and outcome prediction. A key component of digital twin technology is shape analysis, which quantifies anatomical variations and captures complex morphological patterns across populations.

In this talk, I will explore how shape analysis enhances the fidelity of digital twins in healthcare. I will highlight applications demonstrating how these techniques enable patient-specific predictions and simulations, supporting precision medicine. Finally, I will discuss future directions and challenges in translating these computational tools into clinical practice, bridging the gap between advanced modeling and real-world healthcare applications.

High-quality models require high-quality data, yet identifying problematic samples in massive datasets remains challenging. This session introduces a robust framework leveraging Databricks, FiftyOne’s Data Lens, and Mosaic AI to enhance data quality and model performance. Data Lens enables intuitive querying of billion-scale data samples in a data lake, while Mosaic AI uncovers hidden relationships through multimodal embeddings. Combined with Databricks’ scalable data management, these tools streamline error detection, model refinement, and dataset optimization, advancing the development of reliable foundation models in Visual AI.