Athens AI, Machine Learning and Computer Vision Meetup

Sep 09, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!

Physical AI still relies on familiar computer vision tasks—detection, segmentation, depth, tracking. What's changed is the data unit: no longer a single image and label, but an episode—a dozen sensors ticking on independent clocks for minutes, with no frame boundaries.

Most computer vision tooling assumes the old unit and breaks on the new one.

That's why Physical AI teams end up with buckets of .mcap files nobody can characterize. Recording is cheap, so logs pile up faster than anyone curates them. Ask what's actually in there—which tasks, which conditions, how many failures and of what kind—and the honest answer is usually a shrug.
MCAP has been ROS 2's default log format since Iron, and as of FiftyOne 1.19 it opens natively: cameras, LiDAR, GPS, IMU, and logs on one shared timeline, alongside your images and video.

We'll tackle quality, the harder half: what smoothness, sensor-health, and outlier metrics actually measure, where each falls short, and how to turn a score into a defensible decision.

You'll leave knowing how to load your own recordings, query a whole corpus instead of a single file, and which quality signals to trust for which job.

Sep 17, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

AI for Autonomous Driving: From Data to Decisions

Building reliable automated driving systems is as much a data and engineering challenge as a modeling one. In this talk, Tin will share perspectives from his work at Porsche AG on applying modern AI methods across the autonomous driving development process, from making sense of large-scale driving data to understanding and evaluating how AI-based systems behave on the road. He'll discuss lessons learned from real-world development, where today's approaches shine, and where hard problems remain for the ADAS and AV community.

About the Speaker

Tin Stribor Sohn is a PhD Student at Porsche AG and Karlsruhe Institute of Technology in the area of Foundation Models for Scenario Understanding and Decision Making in Autonomous Robotics, Tech Lead at Data Driven Engineering for Autonomous Driving, Prior: Master in CS at University of Tuebingen with focus on Computer Vision and Deep Learning and co-founder of a software company for smart EV charging

Advancing ADAS and Autonomous Vehicle Development with Multimodal Data

ADAS and autonomous vehicle systems rely on increasingly complex data from cameras, video, LiDAR, radar, and other sensor streams. In this session, Murilo will introduce Voxel51 and explore how the latest multimodal capabilities in FiftyOne help teams bring these data sources together to better understand their datasets and model behavior. He’ll discuss how unified workflows for visualization, search, curation, and evaluation can help ADAS and AV teams uncover challenging scenarios, investigate model failures, and build safer, more reliable autonomous systems.

About the Speaker

Murilo Gustineli is a Machine Learning Engineer at Voxel51 working at the intersection of representation learning and computer vision. He holds an M.S. in Computer Science from Georgia Tech, where he co-leads the DS@GT Applied Research & Competitions group, advancing machine learning research through competitive challenges and peer-reviewed publications.

From Survey-Grade Maps to Physical AI: Scaling Real-World Data for Training and Simulation

Physical AI systems are increasingly constrained not by model architectures, but by the availability of scalable, high-fidelity real-world data. This talk explores how Dynamic Map Platform transforms survey-grade road assets collected across 1.8 million km of roads worldwide into training- and simulation-ready datasets, including point clouds, imagery, HD maps, road surface models, and 3D Gaussian Splatting representations.

We will discuss why geometric accuracy, semantic understanding, and real-world diversity are critical to building robust autonomous driving systems. Attendees will learn how real-world geospatial data can be structured and scaled for AI training and simulation workflows.

About the Speaker

Ryoto Miyake is a Software Engineer at Dynamic Map Platform, where he works on transforming large-scale geospatial data into AI-ready datasets for training, simulation, and validation, such as HD maps and 3D Gaussian Splatting. With a background in transportation engineering, he works closely with automotive manufacturers and industry partners to bridge large-scale real-world mapping data with next-generation AI and mobility systems.

Sep 22, 2026
9:00 AM - 10:00 AM Pacific
Online. Register for the Zoom!

Ask it to find and remove duplicate images, run object detection and surface low-confidence predictions, or evaluate a model and summarize where it fails, and the agent handles execution end to end.

We will also walk through the newest capabilities shipping with this release, including code generation and plugin generation. You will see how to go from a conversational prompt to a custom dashboard, visualization, or full FiftyOne application, and how to package multi-step workflows as reusable skills the agent can call on demand.

Because the agent runs inside FiftyOne's secure guardrails, teams keep full control. Connect your preferred models from over 100 LLM providers, route requests through your own enterprise gateway, and maintain audit logging and user attribution for every action the agent takes.

Sep 24, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

How Do Mercedes-Benz AI Principles Drive our Innovation?

At Mercedes-Benz, our AI Principles guide every step of innovation, emphasizing responsible use, safety and reliability, explainability, and the protection of privacy. These principles go beyond statements and actively shape how we design, test, and deploy AI systems in real-world automotive and enterprise settings. In this talk, I will present how these principles inspired our recent research on when reusing LoRA (Low-Rank Adaptation) is effective. By combining theoretical analysis with synthetic data as a proxy for enterprise scenarios, we uncovered the strengths and limitations of modular AI components under constrained data access. Our findings provide practical guidance on when reused LoRAs could deliver high-quality results.

About the Speaker

Mei-Yen Chen is a Senior Data Scientist at Mercedes-Benz Tech Innovation GmbH in Germany with 10 years of industry experience in AI and data solutions. She leads early-stage AI projects across business functions and collaborates with research institutions on machine learning and responsible AI.

Region Tokens as the Visual Primitive: From Recognition to World Modeling

Patch-based tokenization has become the default interface between vision encoders and downstream models, yet patches carry no semantic structure and scale poorly with resolution and temporal extent. This talk presents a research program centered on replacing patch tokens with region-level representations — semantically dense tokens grounded in visual entities rather than arbitrary grid crops.

I will describe RELOCATE, REN, and T-REN, a progression of methods that produce region tokens via pooling, train them with region-level objectives, and extend them to video with temporal coherence. I will then present ongoing work integrating region tokens into VLMs to directly expand visual context capacity, and preliminary results on future region trajectory prediction as a foundation for world modeling.

The broader thesis is that region-level tokens are a more natural unit of visual computation than patches, and their advantage compounds as task complexity, resolution, and temporal horizon increase.

About the Speaker

Savya Khosla is a second-year Ph.D. student at the University of Illinois Urbana-Champaign, advised by Prof. Derek Hoiem and Prof. Alex Schwing.

Leveraging Text-To-Image Diffusion Models for Consistent Set-to-Set Generation

Image collections are humans' primary way of capturing the world, yet advances in generative editing remain largely inapplicable to this modality. We address this gap by introducing Match-and-Fuse - a zero-shot, training-free method for consistent set-to-set generation from image collections that share a common visual element but differ in viewpoint, capture time, and surrounding content.
Our key idea is a unified graph-based framework that combines dense correspondences with an emergent prior in text-to-image diffusion models to generate coherent canvases. We achieve state-of-the-art consistency and visual quality, and unlock new creative capabilities for content generation.

About the Speaker

Kate Feingold is a PhD student in Computer Vision at the Weizmann Institute of Science. Her research sits at the intersection of generative models, 3D/4D perception, and multimodal learning, focusing on problems where vision meets other modalities or paradigms in creative tasks.

Yield Estimation of a Coffee in a dense environment

This presentation provides a detailed workflow related to coffee yield estimation in a dense environment. With photos of pre-harvest coffee plants from a couple of coffee estates, details related to pre-processing, annotation to detect regions of interest (ROI), object detection training and inferencing results with various Yolo models and finally segmentation with SAM2 and Yolo*-seg with training and inference results to determine the count of raw, pre-mature, mature and over-mature coffee berries and finally the yield of the entire estate. All this is based on real world data captured on iPhone and android phones.

About the Speaker

Raghu M. Rao is a consultant working on applications of computer vision AI models. He was previously with AMD and Xilinx. He has a Ph.D. in Wireless Communications from UCLA and is a Senior Member, IEEE. His current interests are in applications of AI for agriculture, health care and wireless communications.