What weâre about
đ This virtual group is for data scientists, machine learning engineers, and open source enthusiasts.
Every month weâll bring you two diverse speakers working at the cutting edge of data science, machine learning, AI and computer vision.
- Are you interested in speaking at a future Meetup?
- Is your company interested in sponsoring a Meetup?
Contact the Meetup organizers!
This Meetup is sponsored by Voxel51, the lead maintainers of the open source FiftyOne computer vision toolset. To learn more about FiftyOne, visit the project page on GitHub.
đŁ Past Speakers
* Sage Elliott at Union.ai
* Michael Wornow at Microsoft
* Argo Saakyan at Veryfi
* Justin Trugman at Softwaretesting.ai
* Johannes Flotzinger at UniversitĂ€t der Bundeswehr MĂŒnchen
* Harpreet Sahota at Deci,ai
* Nora Gourmelon at Friedrich-Alexander-UniversitĂ€t Erlangen-NĂŒrnberg
* Reid Pryzant at Microsoft
* David Mezzetti at NeuML
* Chaitanya Mitash at Amazon Robotics
* Fan Wang at Amazon Robotics
* Mani Nambi at Amazon Robotics
* Joy Timmermans at Secury360
* Eduardo Alvarez at Intel
* Minye Wu at KU Leuven
* Jizhizi Li at University of Sydney
* Raz Petel at SightX
* Karttikeya Mangalam at UC Berkeley
* Dolev Ofri-Amar at Weizmann Institute of Science
* Roushanak Rahmat, PhD
* Folefac Martins
* Zhixi Cai at Monash University
* Filip Haltmayer at Zilliz
* Stephanie Fu at MIT
* Shobhita Sundaram at MIT
* Netanel Tamir at Weizmann Institute of Science
* Glenn Jocher at Ultralytics
* Michal Geyer at Weizmann Institute of Science
* Narek Tumanya at Weizmann Institute of Science
* Jerome Pasquero at Sama
* Eric Zimmermann at Sama
* Victor Anton at Wildlife.ai
* Shashwat Srivastava at Opendoor
* Eugene Khvedchenia at Deci.ai
* Hila Chefer at Tel-Aviv University
* Zhuo Wu at Intel
* Chuan Guo at University of Alberta
* Dhruv Batra Meta & Georgia Tech
* Benjamin Lahner at MIT
* Jiajing Chen at Syracuse University
* Soumik Rakshit at Weights & Biases
* Jiajing Chen at Syracuse University
* Paula Ramos, PhD at Intel
* Vishal Rajput at Skybase
* Cameron Wolfe at Alegion/Rice University
* Julien Simon at Hugging Face
* Kris Kitani at Carnegie Mellon University
* Anna Kogan at OpenCV.ai
* Kacper Ćukawski at Qdrant
* Sri Anumakonda
* Tarik Hammadou at NVIDIA
* Zain Hasan at Weaviate
* Jai Chopra at LanceDB
* Sven Dickinson at University of Toronto & Samsung
* Nalini Singh at MIT
đ Resources
* YouTube Playlist of previous Meetups
* Recap blogs including Q&A and speaker resource links
Upcoming events (4+)
See all- Network event65 attendees from 36 groups hostingMay 6 - Image Embeddings: Zero-shot Classification with CLIP WorkshopLink visible for attendees
When and Where
- May 6, 2025
- 6:30 PM to 8:30 PM CET | 9:30 AM to 11:30 AM Pacific
- Workshops are delivered over Zoom
About the Workshop
Join us for a 12-part, hands-on series that teaches you how to work with images, build and train models, and explore tasks like image classification, segmentation, object detection, and image generation. Each session combines straightforward explanations with practical coding in PyTorch and FiftyOne, allowing you to learn core skills in computer vision and apply them to real-world tasks.
In this session, weâll cover image embeddings, vision transformers, and CLIP. Build a model for zero-shot classification and semantic search using CLIP, then inspect how image embeddings influence predictions with FiftyOne.
These are hands-on maker workshops that make use of GitHub Codespaces, Kaggle notebooks, and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!)
Workshop Resources
You can find the workshop materials in this GitHub repository.
About the Instructor
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlinâs Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIAâs Deep Learning Institute.
- Network event55 attendees from 36 groups hostingMay 13 - Object Detection & Instance Segmentation: YOLO in Practice WorkshopLink visible for attendees
When and Where
- May 13, 2025
- 6:30 PM to 8:30 PM CET | 9:30 AM to 11:30 AM Pacific
- Workshops are delivered over Zoom
About the Workshop
Join us for a 12-part, hands-on series that teaches you how to work with images, build and train models, and explore tasks like image classification, segmentation, object detection, and image generation. Each session combines straightforward explanations with practical coding in PyTorch and FiftyOne, allowing you to learn core skills in computer vision and apply them to real-world tasks.
In this session, weâll introduce object detection and instance segmentation methods. Build a YOLO-based network to perform object detection and instance segmentation, and analyze detection results with FiftyOne.
These are hands-on maker workshops that make use of GitHub Codespaces, Kaggle notebooks, and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!)
Workshop Resources
You can find the workshop materials in this GitHub repository.
About the Instructor
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlinâs Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIAâs Deep Learning Institute.
- Network event31 attendees from 36 groups hostingMay 20 - Image Generation: Diffusion Models & U-Net WorkshopLink visible for attendees
When and Where
- May 20, 2025
- 6:30 PM to 8:30 PM CET | 9:30 AM to 11:30 AM Pacific
- Workshops are delivered over Zoom
About the Workshop
Join us for a 12-part, hands-on series that teaches you how to work with images, build and train models, and explore tasks like image classification, segmentation, object detection, and image generation. Each session combines straightforward explanations with practical coding in PyTorch and FiftyOne, allowing you to learn core skills in computer vision and apply them to real-world tasks.
In this session, weâll explore image generation techniques using diffusion models. Participants will build a U-Net-based model to generate MNIST-like images and then inspect the generated outputs with FiftyOne.
These are hands-on maker workshops that make use of GitHub Codespaces, Kaggle notebooks, and Google Colab environments, so no local installation is required (though you are welcome to work locally if preferred!)
Workshop Resources
You can find the workshop materials in this GitHub repository.
About the Instructor
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlinâs Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIAâs Deep Learning Institute.
- Network event121 attendees from 36 groups hostingMay 22 - AI, ML and Computer Vision MeetupLink visible for attendees
When and Where
- May 22, 2025 | 10:00 AM Pacific
- Virtual - Register for the Zoom
CountGD: Multi-Modal Open-World Counting
We propose CountGD, the first open-world counting model that can count any object specified by text only, visual examples only, or both together. CountGD extends the Grounding DINO architecture and adds components to enable specifying the object with visual examples. This new capability â being able to specify the target object by multi-modalites (text and exemplars) â lead to an improvement in counting accuracy. CountGD is powering multiple products and has been applied to problems across different domains including counting large populations of penguins to monitor the influence of climate change, counting buildings from satellite images, and counting seals for conservation.
About the Speaker
Niki Amini-Naieni is a DPhil student focusing on developing foundation model capabilities for visual understanding of the open world at the Visual Geometry Group (VGG), Oxford supervised by Andrew Zisserman. In the past, Niki has consulted with Amazon and other companies in robotics and computer vision, interned at SpaceX, and studied computer science and engineering at Cornell.
GorillaWatch: Advancing Gorilla Re-Identification and Population Monitoring with AI
Accurate monitoring of endangered gorilla populations is critical for conservation efforts in the field, where scientists currently rely on labor-intensive manual video labeling methods. The GorillaWatch project applies visual AI to provide robust re-identification of individual gorillas and generate local population estimates in wildlife encounters.
About the Speaker
Maximilian von Klinski is a Computer Science student at the Hasso-Plattner-Institut and is currently working on the GorillaWatch project alongside seven fellow students.
This Gets Under Your Skin â The Art of Skin Type Classification
Skin analysis is deceptively hard: inconsistent portrait quality, lighting variations, and the presence of sunscreen or makeup often obscure whatâs truly âunder the skin.â In this talk, Iâll share how we built an AI pipeline for skin type classification that tackles these real-world challenges with a combination of vision models. The architecture includes image quality control, facial segmentation, and a final classifier trained on curated dermatological features.
About the Speaker
Markus Hinsche is the co-founder and CTO of Thea Care, where he builds AI-powered skincare solutions at the intersection of health, beauty, and longevity. He holds a Masterâs in Software Engineering from the Hasso Plattner Institute and brings a deep background in AI and product development.
A Spot Pattern Is like a Fingerprint: Jaguar Identification Project
The Jaguar Identification Project is a citizen science initiative actively engaging the public in conservation efforts in Porto Jofre, Brazil. This project increases awareness and provides an interesting and challenging dataset that requires the use of fine-grained visual classification algorithms. We use this rich dataset for dual purposes: teaching data-centric visual AI and directly contributing to conservation efforts for this vulnerable species.
About the Speaker
Antonio Rueda-Toicen, an AI Engineer in Berlin, has extensive experience in deploying machine learning models and has taught over 300 professionals. He is currently a Research Scientist at the Hasso Plattner Institute. Since 2019, he has organized the Berlin Computer Vision Group and taught at Berlinâs Data Science Retreat. He specializes in computer vision, cloud technologies, and machine learning. Antonio is also a certified instructor of deep learning and diffusion models in NVIDIAâs Deep Learning Institute.
Past events (151)
See all- Network event201 attendees from 36 groups hostingApril 29 - Model Optimization: Data Augmentation & Regularization WorkshopThis event has passed
