Seattle AI, Machine Learning and Computer Vision Meetup

Jun 09, 2026
9:00 AM – 10:30 AM PST
Online. Register for the Zoom!

Medical imaging teams are increasingly fine-tuning foundation models like UNI, MedSAM2, and BiomedCLIP on small in-house datasets. At that scale, label disagreement is a dominant cause of model failures, and the disputed ground truth is what regulators will ask you to defend. We'll build a medical imaging dataset in FiftyOne, surfacing and analyzing the cases where reviewers disagree. From there, we'll fine-tune a foundation model on cleaned data and use FiftyOne to evaluate where our model succeeds and fails, and which data is needed to move the model’s performance forward.

You’ll learn how to:

• Build a medical imaging dataset that preserves multiple expert annotations as first-class fields
• Use FiftyOne views, embedding similarity, and confidence-disagreement signals to find the samples where reviewers split.
• Run label-quality screens, near-duplicate detection, and active-learning sample selection using foundation model embeddings
• Fine-tune a medical foundation model on a defensible dataset, with auditable and versioned experiment tracking
• Filter and slice evaluation for regulatory and clinical readiness
• Drive the pipeline with natural-language agents using the FiftyOne MCP Server and Skills to run the same curation, evaluation, and review workflows from your favorite AI tool

Who This Is For

• ML and computer-vision engineers in the medical imaging space
• Data and annotation operations teams
• Clinical AI and digital pathology leads
• Regulatory and quality leads

Jun 17, 2026
9 AM Pacific
Online. Register for the Zoom!

AI agents are rapidly changing how teams build and scale machine learning workflows—but most implementations still rely on fragmented tools, manual processes, and brittle integrations.

We’ll walk through how modern agentic systems move beyond simple prompts—leveraging structured tools like dataset operations, embeddings, evaluation pipelines, and model execution to take real action. You’ll see how these agents can tag data, run inference, evaluate performance, and surface insights automatically, all within a unified workflow.

By combining natural language interfaces with programmable building blocks, teams can dramatically reduce manual effort, accelerate experimentation, and unlock faster decision-making across the ML lifecycle.

Whether you're building data-centric AI systems, managing large-scale vision datasets, or exploring agentic workflows for the first time, this session will give you a practical blueprint for getting started.

What you’ll learn:

• How AI agents actually work in production: Move beyond hype—understand how agents use tools, memory, and structured workflows to execute real tasks
• Using tools to take action on your data: Program agents to run operations like filtering labels, computing embeddings, evaluating detections, and more
• What “skills” are and how they enable multi-step workflows: Learn how to package complex logic into reusable capabilities your agents can call on demand
• How MCP connects your models, tools, and agents: See how MCP standardizes communication between LLMs and external systems for scalable, flexible architectures
• Automating the ML lifecycle with agents: From data curation to model evaluation, discover how to eliminate repetitive workflows and accelerate iteration
• Best practices for building reliable agentic systems: Design patterns, guardrails, and practical tips for deploying agents in real-world environments

This workshop explores the FiftyOne plugin framework to build custom computer vision applications. You’ll learn to extend the FiftyOne App with Python based panels and server side operators, as well as integrate external tools for labeling, vector search, and model inference into your dataset views.

Time, Date and Location

Jun 24, 2026
9 AM - 10 AM PST
Online. Register for the Zoom!

What you'll learn:

• Build Python plugins. Define plugin manifests and directory structures to register custom functionality within the FiftyOne ecosystem.
• Develop server side operators. Write functions to execute model inference, data cleaning, or metadata updates from the App interface.
• Build interactive panels. Create custom UI dashboards using to visualize model metrics or specialized dataset distributions.
• Manage operator execution contexts. Pass data between the App front end and your backend to build dynamic user workflows.
• Implement delegated execution. Configure background workers to handle long running data processing tasks without blocking the user interface.
• Build labeling integrations. Streamline the flow of data between FiftyOne and annotation platforms through custom triggers and ingestion scripts.
• Extend vector database support. Program custom connectors for external vector stores to enable semantic search across large sample datasets.
• Package and share plugins. Distribute your extensions internally and externally

Jun 25, 2026
9AM PST
Online. Register for the Zoom!

Large-Scale Scene Reconstruction via Local View Transformers

Transformer-based models have advanced 3D scene reconstruction, but their quadratic attention limits scalability to large scenes. We introduce the Local View Transformer (LVT), which replaces global attention with locality-aware attention over neighboring views, conditioned on relative camera geometry. LVT decodes directly into 3D Gaussian splats with view-dependent color and opacity for high-fidelity rendering. Our approach enables scalable, single-pass reconstruction of large, high-resolution scenes.

About the Speaker

Tooba Imtiaz is a PhD candidate in Electrical and Computer Engineering at Northeastern University, working in the Machine Learning Lab. Her research focuses on 3D computer vision, novel view synthesis, and robust machine learning. She has published in top venues including SIGGRAPH Asia, CVPR, and ICLR, and has industry experience at Google.

Lessons learned from running AI workloads in production

He’ll share his “tales from the engine room” - practical insights from operating AI systems at scale, including the challenges of abstraction layers, the realities of data movement and hardware constraints, and how systems thinking is essential for building high-performance, secure, and responsible AI infrastructure.

About the Speaker

Dave Hughes is CTO at Stelia. He was formerly CTO at Genesis Cloud, which pioneered what is now commonly known as 'neoclouds', and Principal Engineer/Interim Director of Engineering at Adjust GmbH where he built large-scale data warehousing and processing. Dave has a strong background in software engineering, data engineering, systems admin and network engineering. He has worked in traditional HPC, early GPU-accelerated computing (ML) and now AI.

Enhancing Low-Field MRI with Deep Super-Resolution for Improved Nipah Virus Neuroimaging

Advances in deep learning make very-low-field (VLF) MRI systems a viable alternative for in vivo neuroimaging. Zero-shot super-resolution, self-supervised learning, and generative AI were explored to improve the quality of low-field MRI images. We present a framework for the first deployment of a VLF scanner for imaging Nipah virus-inoculated nonhuman primates (NHPs) using a 0.05 T MRI system.

First, a retrospective simulation study assessed the feasibility of imaging NiV infection at low field, followed by a prospective deployment (0.05 T) that enabled longitudinal imaging. The VLF-NiV imaging was characterized by low image quality and included multiple contrasts. A deep learning-based unpaired domain adaptation (CycleGAN) conditioned on acquisition parameters was used to harmonize contrast, and a simulation-based ResUNet model was used to reduce unwanted noise and preserve T2-weighted structural fidelity. We also highlight studies involving zero-shot super-resolution and denoising experiments that are advantageous for accessible neuroimaging.

About the Speaker

Ajay Sharma is a deep learning engineer with a broad background in biomedical image analysis. My research focuses on developing advanced deep learning methods for computer-aided disease detection and diagnosis. Currently, my work centers on improving image analysis in magnetic resonance imaging (MRI), with emphasis on low-field MRI (LF-MRI), image acquisition, image enhancement, brain tracking, segmentation, and reporting. Previously, I developed explainable AI (XAI) approaches for chest and pediatric brain imaging that increase clinicians’ confidence in AI-assisted diagnostic systems.

And Now for Something Completely Different with FiftyOne

Often the best way to understand what a tool is truly capable of, is to use in ways it was never intended to be used. This session pushes FiftyOne past its computer vision roots through a series of demos showing how to push the boundaries with FiftyOne. A few practical, some playful, all built with open source code. You'll see how FiftyOne's core building blocks generalize far beyond labeled datasets, and leave with patterns and ideas you can take in your own direction.

About the Speaker

Burhan Qaddoumi is a ML DevRel Engineer at Voxel51 and perpetual "new guy" as a life long learner. Active in communities all across the web, eager to help, learn, and share with others that demonstrate initiative, interest, and drive.