Name: July 23 - Chicago AI, ML and Computer Vision Meetup
Start: 2026-07-23T22:30:00.000Z
Location: 10 S Riverside Plaza suite 800

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

**Date, Time and Location**

Jun 25, 2026
9AM PST
Online. **[Register for the Zoom!](https://voxel51.com/events/ai-ml-and-computer-vision-meetup-june-25-2026)**

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](https://www.linkedin.com/in/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](https://www.linkedin.com/in/-davehughes-/) 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](https://www.linkedin.com/in/ajaysharma1996/) 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](https://www.linkedin.com/in/burhan-qa/) 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.

Tokyo AI, Machine Learning and Computer Vision Meetup

Jimmy Guerrero

sadia.r037

taka

June 25 - AI, ML and Computer Vision Meetup

Online event

In this workshop we’ll demonstrate workflows for image and video annotation, instance segmentation, polylines, QA and review, collaborative labeling operations in [FiftyOne](https://docs.voxel51.com/), and smart data selection strategies that help teams reduce wasted labeling spend.

**Date, Time and Location**

Jun 30, 2026
9 AM PST
Online. **[Register for the Zoom!](https://voxel51.com/events/beyond-annotation-tools-building-a-complete-physical-ai-data-engine-with-fiftyone-june-30-2026)**

Annotation is no longer just about drawing boxes. Modern physical AI teams need an end-to-end system for labeling, QA, dataset curation, project management, auto-labeling, and video understanding — all tightly integrated into the workflows where models are actually built and evaluated.

You’ll also get an early look at new agentic labeling workflows powered by “Labeling Agents” — intelligent systems that can learn from text prompts and visual examples to automatically label datasets at scale. We’ll walk through how teams can rapidly create reusable labeling agents, validate outputs, and apply them across large datasets as background tasks.

Whether you’re building computer vision models for robotics, autonomous systems, manufacturing, retail, or multimodal AI applications, this session will show how integrated annotation and data-centric workflows can dramatically accelerate iteration speed while improving dataset quality.

**What You’ll Learn**

* How smart data selection strategies reduce annotation costs and improve model performance
* Why integrated annotation is becoming a core requirement for modern physical AI platforms
* How to unify data curation, annotation, evaluation, and model iteration inside a single workflow
* How FiftyOne supports annotation workflows for Classification, Object detection, Instance segmentation, Polylines, Video detection and tracking
* How to create, edit, QA, and manage 2D and 3D labels directly in context
* How annotation project management workflows help coordinate labeling teams and reviews
* How SAM2-powered click-to-segment workflows enable fast browser-based segmentation
* How agentic labeling works, including training reusable “Labeling Agents”, prompting with text + visual examples, iterating on outputs before deployment and running large-scale auto-labeling workflows

June 30 - Beyond Annotation Tools: Building a Complete Physical AI Data Engine

Join our in-person meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision

**[Register to reserve your spot!](https://voxel51.com/events/seattle-ai-ml-and-cv-meetup-july-15-2026)**

**Date, Time and Location**

Jul 15, 2026
5:30 PM - 8:30 PM PT

Union.ai Offices
400 112th Ave NE #115
Bellevue, WA 98004

**Building Foundation Models for Robotic Perception**

3D spatial understanding is a critical skill for robotics which typically requires tedious manual design, expensive data collection and per-domain training. This presentation will focus on the development and application of foundation models to address several fundamental challenges in robotic perception, and how they facilitate robotic loco-manipulation skills.

First, we introduce FoundationStereo (CVPR'25 best paper candidate), a novel architecture optimized for zero-shot performance. The model leverages a 1M-pair self-curated synthetic dataset, bridges the sim-to-real gap using monocular priors, and incorporates an advanced filtering module for long-range context reasoning.

Second, we address its computational bottlenecks with Fast-FoundationStereo (CVPR'26). We propose a "divide-and-conquer" acceleration strategy that retains the teacher model's robustness while achieving a 10x speedup, making it suitable for real-time applications.

*About the Speaker*

[Bowen Wen](https://www.linkedin.com/in/bowen-wen/) is a Staff Research Scientist at NVIDIA Research. His research areas include robotic perception and computer vision. More recently, he focuses on large foundation models for 3D visual perception and learning to facilitate robotics or embodied AI.

**STELLAR: Learning Sparse Visual Concepts for Unified Vision Models**

Modern vision models often split into two regimes: models that learn strong semantics for recognition, and models that preserve spatial detail for reconstruction.

In this talk, we present STELLAR, a self-supervised framework for learning sparse visual concepts as a unified representation for vision models. The key idea is to factorize visual features into semantic concept tokens (the "what"), and spatial assignment maps (the "where"), allowing the model to align concepts across views while preserving the geometry needed for reconstruction.

This sparse, low-rank representation creates a compact interface that supports recognition, dense prediction, and image reconstruction, while also suggesting future directions for efficient visual encoding, video self-supervision, generative modeling, and world-model-style visual reasoning.

We discuss the core method, empirical results, and why concept-centric visual representations may be a useful building block for the next generation of unified vision systems.

*About the Speaker*

[Theodore Zhao](https://www.linkedin.com/in/zhengde-theodore-zhao/) \- is a researcher working on multimodal foundation models\, visual representation learning\, and biomedical AI\. His recent work focuses on learning compact\, interpretable\, and grounded visual representations that support both semantic understanding and reconstruction\.

**Motivation and Challenges in working with Multimodal Timeseries Data**

Physical AI is having its moment, with many companies and research teams focussed on this space. But working with physical AI data means wrestling with high cardinality, complex, non-synced, multi sensor streams that are hard to explore, align and curate. In this talk, we will break down the challenges that come with multimodal time series data, and then look at the research directions this industry is pursuing, the ones that are unlocked when you can actually work with your data effectively.

*About the Speaker*

[Prerna Dhareshwar](https://www.linkedin.com/in/prernamd/) \- is a Machine Learning Engineer at Voxel51\, where she helps customers leverage FiftyOne to accelerate dataset curation\, model development\, and evaluation in real\-world AI workflows\. She brings extensive experience building and deploying computer vision and machine learning systems across industries\.

**Orchestrating Scalable AI Workflows with Flyte and Union.ai**

Modern AI systems require infrastructure that can reliably orchestrate training, inference, and production workflows at scale. This session explores approaches to AI orchestration, distributed compute, and resilient ML infrastructure for real-world machine learning and computer vision applications.

Topics may include production AI pipelines, workflow automation, scalable deployment strategies, and operating AI systems securely within cloud environments. Attendees will gain a high-level look at emerging patterns shaping the next generation of AI infrastructure and operational workflows.

*About the Speaker*

[Sage Elliott ](https://www.linkedin.com/in/sageelliott/)\- is an AI Engineer at Union\.ai \(core maintainers of Flyte\)\.

Seattle AI, Machine Learning and Computer Vision Meetup

codeshack

Deep Desai

July 15 - Seattle AI, ML and Computer Vision Meetup

Union AI

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

**Date, Time and Location**

Jul 23, 2026
9:00 AM - 11:00 AM PST
**[Online. Register for the Zoom!](https://voxel51.com/events/ai-ml-and-computer-vision-meetup-july-23-2026)**

**Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity**

The domain of automatic video trailer generation is currently undergoing a profound paradigm shift, transitioning from heuristicbased extraction methods to deep generative synthesis. While early methodologies relied heavily on low-level feature engineering, visual saliency, and rule-based heuristics to select representative shots, recent advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), and diffusion-based video synthesis have enabled systems that not only identify key moments but also construct coherent, emotionally resonant narratives.

This survey provides a comprehensive technical review of this evolution, with a specific focus on generative techniques including autoregressive Transformers, LLM-orchestrated pipelines, and text-to-video foundation models like OpenAI's Sora and Google's Veo. We analyze the architectural progression from Graph Convolutional Networks (GCNs) to Trailer Generation Transformers (TGT), evaluate the economic implications of automated content velocity on User-Generated Content (UGC) platforms, and discuss the ethical challenges posed by high-fidelity neural synthesis.

By synthesizing insights from recent literature, this report establishes a new taxonomy for AI-driven trailer generation in the era of foundation models, suggesting that future promotional video systems will move beyond extractive selection toward controllable generative editing and semantic reconstruction of trailers.

*About the Speaker*

[Abhishek Dharmaratnakar](https://www.linkedin.com/in/abhid95/?isSelfProfile=false) is an Engineering Leader at Google leading YouTube Premium. His work focuses on the intersection of hyperscale media infrastructure and generative artificial intelligence, directing cross-functional engineering organizations to redefine how billions of users consume and create content

**Making Agent Systems Observable, Reliable, and Testable**

In this talk, I’ll share practical lessons from building real agent systems in computer vision workflows, focusing on how to design evaluation loops, observability pipelines, and sandboxed environments that make agents reliable in practice. We’ll explore how to measure behavior end-to-end, test components independently, and build feedback loops that help agents improve over time, even as tools, models, and pipelines evolve. This talk is for engineers and builders who want to move beyond demos and learn how to make agent systems production-ready.

*About the Speaker*

[Adonai Vera](https://www.linkedin.com/in/adonai-vera/) \- Machine Learning Engineer & DevRel at Voxel51\. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV\.

**Training-Free Object and Associated Effect Removal in Videos**

I will be presenting our recent work, Object-WIPER, which focuses on removing objects and their associated effects from videos. Instead of fine-tuning models for each editing task, our method reuses the priors of pre-trained text-to-video models to perform object and effect removal in a training-free manner. We also curate a real world associated-effect benchmark and evaluation metric for more realistic assessment of video object removal.

*About the Speaker*

[Saksham Singh Kushwaha](https://www.linkedin.com/in/saksham-singh-kushwaha/) is a candidate at UT Dallas, with research interests in audio-visual learning, spatial audio, and computer vision. I received my master’s degree from NYU and bachelor’s degree from IIT Delhi.

**Turning Models into Systems: AI Architecture That Works**

This talk explores what it really takes to make "intelligent systems" work in the messy, high-stakes reality of production environments – not just in demos or pilots. Most AI initiatives do not fail because the algorithms are weak, but because the surrounding system is not designed to handle uncertainty, change, and operational demands.

The session shows how to separate the concerns of building and improving models from their use in daily operations, and how to create a stable core of rules, safety, and business meaning around which smarter components can evolve.

Instead of treating AI as a magic add-on, the talk frames it as a capability that must be grounded in the organization's language, workflows, and responsibilities. It demonstrates how to design that core so that new models, tools, and data sources can be plugged in, compared, and replaced without breaking trust.

Attendees will leave with a clear mental model and a set of practical design ideas for turning clever prototypes into robust, understandable, and adaptable intelligent systems that people on the ground are willing to rely on.

*About the Speaker*

[Dr. Nikita Golovko](https://www.linkedin.com/in/dr-nikita-golovko/) is a seasoned Solution Architect with over 16 years of experience in designing scalable, secure, and cost-effective software architectures for industrial and business-critical systems.

Zürich AI, Machine Learning and Computer Vision Meetup

Abdelrahman Mahfouz

lisha

Mark

Christoph

Ghufran

Nils Holgerson

July 23 - AI, ML, and Computer Vision Meetup

Join our in-person meetup in Chicago to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

**[Pre-registration is mandatory to clear building security.](https://voxel51.com/events/chicago-ai-ml-and-cv-meetup-july-23-2026)**

**Date, Time and Location**

Jul 23, 2026
5:30 PM - 8:30 PM CT
10 S Riverside Plaza, Suite 800, Chicago, IL 60606

Talks will include:

**Teaching Vision-Language Models to Read Spine MRIs**

Radiologists face mounting report volumes and rising burnout, creating demand for assistive tools that can draft structured findings directly from imaging. We fine-tuned Qwen2.5-VL with LoRA on roughly 5000 paired lumbar spine MRI studies and reports, training the model to generate spinal level-specific findings evaluated with lexical, semantic, and clinical metrics.

We are extending the work with self-supervised pretraining on an additional 20000 unlabeled studies to build domain-specific backbones for downstream tasks including lumbar spine MRI segmentation and classification. The talk shares current results, challenges faced, and why evaluating structured radiology reports is harder than standard metrics suggest.

*About the Speaker*

[Mattia Perrone](https://linkedin.com/in/mattia-perrone) is a Research Scientist at Rush University Medical Center, where his work focuses on deep learning, computer vision and vision-language models for medical imaging. He holds a dual Master’s Degree in Biomedical Engineering from Politecnico di Milano and the University of Illinois Chicago.

**Improving Efficiency of DNN Stereo Depth Estimation models**

Stereo depth estimation is a core perception capability in robotics and autonomous systems, converting rectified stereo image pairs into dense disparity and depth maps. While modern deep stereo methods achieve strong accuracy, state-of-the-art models, especially transformer-based architectures often incur high computational and energy costs, limiting deployment on resource-constrained devices.

This studies stereo depth estimation and proposes an efficiency-oriented modification to a transformer-based stereo pipeline by incorporating Walsh–Hadamard Transform (WHT) operations into the feature extraction stage. Specifically, we experiment with a WHT-based convolutional substitute (WHTConv2D) to reduce multiply-accumulate operations while preserving representational capacity via structured ±1 transforms.

We inferenced classical and neural stereo models on a specific dataset to compare, culminating in an STereo TRansformer (STTR) baseline and a WHTConv2D-enhanced variant. The proposed design achieves an observed 18.33% efficiency improvement relative to the baseline configuration while maintaining competitive long-range disparity accuracy.

*About the Speaker*

[Prathyush Sajith](https://www.linkedin.com/in/prathyush-sajith-b95160202/), MS ECE, University of Illinois Chicago. Experienced in building Machine Learning Models - including but not limited to ViTs and LLMs.

**Testing AI Systems in Production: Data Quality, Drift, and Model Evaluation**

AI systems can pass offline evaluation and still fail in production when real-world data changes, features become stale, labels or feedback signals are incomplete, or model behavior drifts away from expected outcomes. This talk shares practical patterns for testing and evaluating AI systems after deployment, including data quality checks, drift detection, online/offline metric comparison, model monitoring, and rollback analysis.

Using personalization and recommendation systems as examples, we will examine how teams can build evaluation workflows that catch quality issues before users do. Attendees will leave with a practical checklist for making AI-backed systems easier to evaluate, debug, and operate as data changes over time.

*About the Speaker*

[Jayakumar Ramalingam](https://www.linkedin.com/in/jayakumarramalingam) is a Staff Software Engineer and Cloud Architect at SiriusXM with over 16 years of experience building cloud-native platforms, real-time data pipelines, resilient APIs, and AI/ML-enabled applications at production scale.

**Building Real-World Computer Vision Systems**

This talk will explore practical workflows for building, evaluating, and improving modern computer vision systems. We’ll dive into real-world approaches to dataset curation, model analysis, multimodal AI workflows, and production-ready vision pipelines using open-source technologies.

The session is designed for engineers, researchers, and AI practitioners looking to better understand how teams are developing and scaling computer vision applications today. Expect practical demos, technical insights, and discussions around the evolving AI tooling ecosystem.

*About the Speaker*

[Harpreet Sahota](https://www.linkedin.com/in/harpreetsahota204/) is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

Chicago AI, Machine Learning and Computer Vision Meetup

Juan Chiwah

Scott Morgan

aguchija

July 23 - Chicago AI, ML and Computer Vision Meetup

10 S Riverside Plaza suite 800

AI, Machine Learning and Computer Vision Meetup Network

그룹

{PRO_NETWORK_NAME}은(는) Meetup 프로로서 전 세계 회원 수가 {PRO_MEMBER_COUNT}명 이상이며 Meetup이 {PRO_GROUP_COUNT}개에 달합니다. Meetup 프로는 사용자, 파트너, 기여자, 회원의 네트워크를 조직하고 소통할 수 있게 해주는 전문적인 툴입니다.

This virtual group is for data scientists, machine learning engineers, and open source enthusiasts.

Every month we’ll bring you two diverse speakers working at the cutting edge of data science, machine learning, AI and computer vision.

This Meetup is sponsored by Voxel51, the lead maintainers of the open source FiftyOne computer vision toolset.

AI, Machine Learning and Computer Vision Meetup Network

소개

예정된 이벤트

소개

그룹