What we’re about
🖖 This virtual group is for data scientists, machine learning engineers, and open source enthusiasts.
Every month we’ll bring you diverse speakers working at the cutting edge of AI, machine learning, and computer vision.
- Are you interested in speaking at a future Meetup?
- Is your company interested in sponsoring a Meetup?
This Meetup is sponsored by Voxel51, the lead maintainers of the open source FiftyOne computer vision toolset. To learn more, visit the FiftyOne project page on GitHub.
Upcoming events (4+)
See all- Aug 20 - In-Person - Raleigh AI, ML and Computer Vision MeetupTEKsystems, Raleigh, NC
Join us for an evening of talks from experts on AI, ML, and Computer Vision co-presented by Voxel51 and Teksystems.
Date and Time
- Aug 20, 2025
- 5:30 PM - 8:30 PM
Location
TEKsystems
4300 Edwards Mill Rd
Raleigh, NCAdapting Vision Foundation Models to Medical Imaging: Strategies and Clinical Applications
Foundation models like SAM and DINO-v2 have shown strong performance on natural image tasks. However, when applied directly to medical imaging, they often underperform due to domain shifts, limited labeled data, and modality-specific challenges. This raises an important question: how can we adapt foundation models to work reliably and meaningfully in medical images?
In this talk, I will share our research efforts toward answering that question. I will begin by exploring several fine-tuning strategies for different data scenarios, ranging from few-shot labeled examples to large collections of unlabeled scans. These strategies aim to help identify the optimal adaptation framework under various data availability settings. I will then introduce a series of models we developed based on these insights. SegmentAnyBone and SegmentAnyMuscle are two SAM-based models designed for accurate bone and muscle segmentation across all body locations and a wide range of MRI sequences. MRI-Core is a self-supervised model that learns general-purpose MRI features from unlabeled data and can be easily adapted to multiple downstream tasks.
Finally, I will present a clinical application where one of these models is used to support abdominal surgical risk prediction. This example shows how I have explored using these models to contribute to real-world clinical decision-making. I hope this talk can share some of my experiences in building foundation models that are both practical for research and adaptable to clinical settings and to spark new insights and discussions in this field!
About the Speaker
Hanxue Gu is a 5th year Ph.D. student in Electrical and Computer Engineering at Duke University, working at the intersection of AI and Healthcare. I am fortunate to be advised by Prof. Maciej A. Mazurowski under Duke Spark Initiative. My research sits at the intersection of machine learning and healthcare, with a focus on developing and adapting deep learning methods for medical image analysis—from application-oriented tools to foundational advancements.
Bias & Batch Effects in Medical Imaging
Medical AI models can exhibit concerning biases, such as the ability to predict race from radiology images, which is impossible for human experts. This talk will examine bias and batch effects in medical imaging, beginning with a histopathology case study to illustrate the origins of some of these biases. I'll cover detection methods, such as exploratory data analysis, and mitigation strategies, including careful cross-validation and model-level interventions. While research has shown that foundation models reduce some biases, they don't eliminate the problem entirely. Bias represents a fundamental challenge in medical AI requiring early detection, careful validation, and tailored mitigation approaches.
About the Speaker
Heather D. Couture is a consultant and founder of Pixel Scientia Labs, where she partners with mission-driven founders and R&D teams to support applications of computer vision for people and planetary health. She has a PhD in Computer Science and has published in top-tier computer vision and medical imaging venues. She hosts the Impact AI Podcast and writes regularly on LinkedIn, for her newsletter Computer Vision Insights, and for a variety of other publications.
Managing Medical Imaging Datasets: From Curation to Evaluation
High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.
We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.
Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.
About the Speaker
Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.
Learning with Small Datasets in Real-World Medical Imaging Applications
The talk will explore approaches that are effective when developing computer vision models for real-world medical imaging applications in situations where available datasets are limited in size. This setting is of special interest because the most challenging prediction problems in the medical domain have usually low incidence rates, thus resulting in (relatively) small datasets. Specifically, it will consider data-efficient architectures, multi-task learning and data augmentation through pseudo interventions. For illustration, a use case in which volumetric ophthalmology images are used to predict geographic atrophy conversion will be discussed.
About the Speaker
Ricardo Henao, a quantitative scientist, is an Associate Professor in the department of Biostatistics and Bioinformatics, Department of Electrical and Computer Engineering (ECE), Surgery, member of the Information Initiative at Duke (iiD), Duke AI Health and the Duke Clinical Research Institute (DCRI), all at Duke University. He also serves as the Associate Director of Clinical Trials AI at DCRI. His recent work has been focused on the development of machine learning models, predominantly deep learning and representation learning approaches, for the analysis and interpretation of clinical and biological data with applications to predictive modeling for diverse clinical outcomes.
- Network event75 attendees from 26 groups hostingAug 21 - AI, ML and Computer Vision Meetup en EspañolLink visible for attendees
Hear talks from experts on cutting-edge topics in AI, ML and Computer Vision Meetup en Español.
Date and Time
Aug 21 at 9 AM Pacific
Location
Virtual. Register for the Zoom
Quiero ser parte del mundo de AI, como lo logro?
En esta charla, compartiré mi trayectoria personal hacia el mundo de la inteligencia artificial (IA), comenzando con mi formación como ingeniero electrónico y mi doctorado en neuroinformática. Destacaré cómo mi tesis laureada sobre modelos volumétricos realistas para la localización precisa de fuentes EEG abrió puertas a oportunidades en procesamiento digital y visión 3D. Con experiencia docente en la Universidad Nacional de Colombia y certificaciones en machine learning y deep learning, discutiré cómo estos hitos me llevaron a desempeñarme como desarrollador de currículo para DeepLearning.AI, ofreciendo valiosas lecciones para quienes deseen seguir un camino similar.
Presentador
Ernesto Cuartas es un ingeniero electrónico y PhD en neuroinformática. Tesis PhD laureada “Forward volumetric modeling framework for realistic head models towards accurate EEG source localization”. Profesor asociado Universidad Nacional de Colombia. Experto en implementación y desarrollo de proyectos en procesamiento digital de señales, procesamiento digital de imágenes, visión 3D, computación gráfica, geometría computacional, fotogrametría e inteligencia artificial. Con certificaciones profesionales en machine learning, deep learning y data engineering. Actualmente trabajo como curriculum developer/engineer para DeepLearning.AI.
Domina tus Datos Médicos: De la Curación al Impacto Clínico
Los datos de alta calidad son la base de un aprendizaje automático efectivo en el ámbito de la salud. Esta charla presenta estrategias prácticas y técnicas emergentes para gestionar datasets de imágenes médicas, desde la generación de datos sintéticos y la curación, hasta la evaluación y el despliegue.
Comenzaremos con casos de estudio reales de investigadores y profesionales que están transformando sus flujos de trabajo en imágenes médicas mediante prácticas centradas en los datos. Luego pasaremos a un tutorial práctico utilizando FiftyOne, la plataforma open-source para la inspección visual de datasets y la evaluación de modelos. Los asistentes aprenderán a cargar, visualizar, curar y evaluar datasets médicos en distintos tipos de imágenes.
Ya seas investigador, clínico o ingeniero de ML, esta charla te brindará herramientas e ideas prácticas para mejorar la calidad de tus datos, la fiabilidad de tus modelos y su impacto clínico.
Presentadora
Paula Ramos tiene un doctorado en Visión Artificial y Aprendizaje Automático, con más de 20 años de experiencia en el campo tecnológico. Desde principios de la década del 2000 en Colombia, ha desarrollado novedosas tecnologías integradas de ingeniería, principalmente en Visión Artificial, robótica y Aprendizaje Automático aplicados a la agricultura.
Agentes AI Multi-Fuente y Embebidos
Demostraré cómo construir agentes de IA contextualmente conscientes, capaz de responder y tomar acciones entre multiples sistemas privados y la implementación de RAG semántico a través de fuentes de datos dispares, embebidos en sistemas existentes, todo esto sin necesidad de una infraestructura compleja de MLOps.
Presentador
Kevin Blanco es un Senior DevRel Advocate, Charlista Internacional con más de 15 años en liderazgo tecnológico. Ha diseñado estrategias de IA en IBM Watson y desarrollado soluciones para Google, Microsoft y Nintendo.
Más allá del modelo: Metodología y buenas prácticas para liderar proyectos exitosos de IA con CPMAI
El éxito de los proyectos de IA no depende solo del modelo o de los datos, sino de cómo se gestionan desde el inicio. En esta charla exploraremos la metodología CPMAI (Cognitive Project Management for AI) avalada por el Project Management Institute - PMI, un marco estructurado que permite a los equipos de IA alinear sus iniciativas con objetivos de negocio, gestionar riesgos éticos y mejorar los resultados. Compartiremos buenas prácticas que pueden ser adaptadas por profesionales técnicos para mejorar la entrega de valor en cada fase del proyecto e implementar soluciones de IA éticas y responsables.
Presentadora
Ivonne Mejía B. es especialista en gestión de proyectos tecnológicos, con más de 20 años de experiencia internacional en el sector privado y académico en México, Canadá y Estados Unidos. Está certificada en CPMAI™, PMP®, Prosci®, y cuenta con un diplomado en Liderazgo Tecnológico por UC Berkeley. Disfruta colaborar, aprender en comunidad y compartir su experiencia para ayudar a las organizaciones a definir estrategias de transformación con IA y liderar soluciones éticas y responsables.
- Network event295 attendees from 44 groups hostingAug 22 - Visual Agent Workshop Part 2: From Pixels to PredictionsLink visible for attendees
Welcome to the three part Visual Agents Workshop virtual series...your hands on opportunity to learn about visual agents - how they work, how to develop them and how to fine-tune them.
Date and Time
Aug 22, 2025 at 9 AM Pacific
Part 2: From Pixels to Predictions - Building Your GUI Dataset
Hands-On Dataset Creation and Curation with FiftyOne
The best GUI models are only as good as their training data, and the best datasets are built by understanding what makes GUI interactions fundamentally different from natural images. In this practical session, you'll build a complete GUI dataset from scratch, learning to capture the precise annotations that GUI agents need.
Using FiftyOne as your data management backbone, you'll import diverse GUI screenshots, explore annotation strategies that go beyond bounding boxes, and implement efficient labeling workflows. We'll tackle the real challenges: handling platform differences, managing annotation quality, and creating datasets that transfer to new domains. You'll also learn advanced techniques like synthetic data generation and automated prelabeling to scale your annotation efforts.
Walk away with a production-ready dataset and the skills to build more—because in GUI agents, data quality determines everything.
By the end, you'll have both a dataset and the methodology to build the next generation of GUI training data.
About the Instructor
Harpreet Sahota 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.
- Network event338 attendees from 44 groups hostingAug 28 - AI, ML and Computer Vision MeetupLink visible for attendees
Date and Time
Aug 28, 2025 at 10 AM Pacific
Location
Virtual - Register for the Zoom
Exploiting Vulnerabilities In CV Models Through Adversarial Attacks
As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.
About the Speaker
Elisa Chen is a data scientist at Meta on the Ads AI Infra team with 5+ years of experience in the industry.
EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation
Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments.
In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.
About the Speaker
Md Mostafijur Rahman is a final-year Ph.D. candidate in Electrical and Computer Engineering at The University of Texas at Austin, advised by Dr. Radu Marculescu, where he builds efficient AI methods for biomedical imaging tasks such as segmentation, synthesis, and diagnosis. By uniting efficient architectures with data-efficient training, his work delivers robust and efficient clinically deployable imaging solutions.
What Makes a Good AV Dataset? Lessons from the Front Lines of Sensor Calibration and Projection
Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.
In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.
Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation
About the Speaker
Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.
Clustering in Computer Vision: From Theory to Applications
In today’s AI landscape, these techniques are crucial. Clustering methods help organize unstructured data into meaningful groups, aiding knowledge discovery, feature analysis, and retrieval-augmented generation. From k-means to DBSCAN and hierarchical approaches like FINCH, selecting the right method is key: including balancing scalability, managing noise sensitivity, and fitting computational demands. This presentation provides an in-depth exploration of the current state-of-the-art of clustering techniques with a strong focus on their applications within computer vision.
About the Speaker
Constantin Seibold leads research group on the development of machine learning methods in the diagnostic and interventional radiology department at the university hospital Heidelberg. His research aims to improve the daily life of both doctors and patients.
Past events (51)
See all- Network event547 attendees from 44 groups hostingAug 15 - Visual Agent Workshop Part 1: Navigating the GUI Agent LandscapeThis event has passed
