Santa Cruz AI and Machine Learning Meetup Group

Aug 13, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!

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.

About the Speaker

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. I started as a software developer, moved into AI, led teams, and served as CTO. Today, I connect code and community to build open, production-ready AI, making technology simple, accessible, and reliable.

Aug 25, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Using Computer Vision to Advance the Sciences

I'll present some of our ongoing work on using computer vision to create impact in the sciences. These target a two areas, solar physics and evolutionary biology, that deal with objects of radically different sizes but are unified by a need for high quality, trustworthy data.

I'll show off our efforts, done in collaboration with domain experts, that aim to produce the best possible maps of the Sun's powerful magnetic field and have created some of the world's largest repositories of data about bird morphology.

About the Speaker

David Fouhey is an Associate Professor at New York University and a research scientist at Polymathic AI. Before joining NYU, he received a PhD in robotics from Carnegie Mellon, was a postdoc at UC Berkeley, and was a professor at University of Michigan.

Solaris: Building a Multiplayer Video World Model in Minecraft

This talk will introduce Solaris: a multiplayer video world model in Minecraft. I will first present SolarisEngine, the software platform we built to simulate realistic multiplayer gameplay between bots at scale, enabling us to collect a large training dataset of aligned multiplayer actions and frames.

I will then discuss our staged training pipeline, starting with single-player pre-training before converting the model into a long-horizon multiplayer generator through bidirectional training, followed by causal training, and concluding with Self Forcing. I will also cover our memory-efficient implementation of Self Forcing, called Checkpointed Self Forcing.

Finally, I will showcase generated videos illustrating how Solaris maintains coherent long-horizon multiplayer interactions.

About the Speaker

Oscar Michel is a PhD student at NYU advised by Prof. Saining Xie. His research studies world models: generative models of agents interacting in an environment.

Closing the human to robot gap for dexterous hands

Collecting task-specific robot data for multi-fingered hands is challenging due to the many difficulties that arise in teleoperation. That is why recently there has been a major focus on learning robot policies directly from human demonstrations. However, human demonstrations are difficult to work with; there is a major morphological and visual gap between human and robot hands, as well as between the environments they operate in.

In this talk, I'd like to discuss my efforts on closing this gap.

About the Speaker

Irmak Guzey I'm Irmak (she/her), a rising 3rd year PhD student at New York University, currently advised by Lerrel Pinto. My research focuses on robot learning for dexterous manipulation. I have been awarded a Fulbright scholarship and NYU's Best Master's Thesis Award in the past.

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.