Jan 29 - Silicon Valley AI, ML and Computer Vision Meetup

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

Pre-register to reserve your seat

Date, Time and Location

Jan 29, 2026
5:30 - 8:30 PM
Yugabyte Offices
771 Vaqueros Ave, Sunnyvale, CA 94085

The World of World Models: How the New Generation of AI Is Reshaping Robotics and Autonomous Vehicles

World Models are emerging as the defining paradigm for the next decade of robotics and autonomous systems. Instead of depending on handcrafted perception stacks or rigid planning pipelines, modern world models learn a unified representation of an environment—geometry, dynamics, semantics, and agent behavior—and use that understanding to predict, plan, and act. This talk will break down why the field is shifting toward these holistic models, what new capabilities they unlock, and how they are already transforming AV and robotics research.

We then connect these advances to the Physical AI Workbench, a practical foundation for teams who want to build, validate, and iterate on world-model-driven pipelines. The Workbench standardizes data quality, reconstruction, and enrichment workflows so that teams can trust their sensor data, generate high-fidelity world representations, and feed consistent inputs into next-generation predictive and generative models. Together, world models and the Physical AI Workbench represent a new, more scalable path forward—one where robots and AVs can learn, simulate, and reason about the world through shared, high-quality physical context.

About the Speaker

Daniel Gural leads technical partnerships at Voxel51, where he’s building the Physical AI Workbench, a platform that connects real-world sensor data with realistic simulation to help engineers better understand, validate, and improve their perception systems.

Beyond Vector Search: How Distributed PostgreSQL Powers, Resilient, Enterprise-Grade AI Applications

As enterprises move from GenAI prototypes to in-production applications, standalone vector databases often fall short on synchronization, ACID compliance, and resilience. This session demonstrates how PostgreSQL-compatible distributed SQL databases address these challenges while maintaining a familiar developer experience. We’ll cover scaling RAG architectures with pgvector across regions, multi-agent patterns.

Attendees will learn how to achieve ultra-resilience for peak traffic, grey failures, and disasters, along with key design principles such as unified data sources, open standards, and multi-tenant security. Engineers and architects will leave with practical strategies for building globally scalable, enterprise-grade GenAI applications.

About the Speaker

Karthik Ranganathan is Co-CEO and Co-Founder at Yugabyte, the company behind YugabyteDB, the open-source, high-performance distributed SQL database for building global, cloud-native applications.. Karthik was one of the original database engineers at Meta(Facebook), responsible for building distributed databases such as Cassandra and HBase. He is an Apache HBase committer, and also an early contributor to Cassandra, before it was open-sourced by Meta.

Distributed Training at Scale

As deep learning models grow in complexity, particularly with the rise of Large Language Models (LLMs) and generative AI, scalable and cost-effective training has become a critical challenge. This talk introduces Ray Train, an open-source, production-ready library built for seamless distributed deep learning. We will explore its architecture, advanced resource scheduling, and intuitive APIs that simplify integration with popular frameworks such as PyTorch, Lightning, and HuggingFace. Attendees will leave with a clear understanding of how Ray Train accelerates large-scale model training while ensuring reliability and efficiency in production environments.

About the Speaker

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing framework like Ray, enabling scalable and efficient ML systems.

Self-improving AI-Models via Reasoning in the loop

During this presentation we demostrate efficient uses of reasoning to automate data-flywheels towards continuous model improvement

About the Speaker

Jose Alvarez is Director of Research at NVIDIA, where he leads an applied AV research team within the Spatial Intelligence Lab. His team focuses on scaling deep learning and driving advancements in Autonomous Driving and, more broadly in Physical AI, with work spanning end-to-end models, foundation models, and data flywheels for real-world applications.