talk-data.com

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025 9:00-10:00 AM Pacific Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

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.

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025 9:00-10:00 AM Pacific Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

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.

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025 9:00-10:00 AM Pacific Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

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.

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025 9:00-10:00 AM Pacific Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

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.

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025 9:00-10:00 AM Pacific Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

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.

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025 9:00-10:00 AM Pacific Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

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.

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025 9:00-10:00 AM Pacific Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

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.

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025 9:00-10:00 AM Pacific Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

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.

Join Voxel51 and NVIDIA as they unveil a breakthrough that’s changing how Physical AI systems are built. In this first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll learn how to create validated, simulation-ready data pipelines—cutting testing costs, eliminating manual data audits, and accelerating development from months to days.

Date and Location

Nov 5, 2025 9:00-10:30 AM Pacific Online. Register for the Zoom

Developing autonomous vehicles and humanoid robots requires rigorous simulations that capture real-world complexity. The critical barrier that keeps teams from achieving success isn’t the simulation engine itself, but the data that powers it.

As Physical AI systems ingest petabytes of multisensor data, converting this raw input into validated, simulation-ready data pipelines remains a hidden bottleneck. A camera-to-LiDAR projection off by a few pixels, timestamps misaligned by a few milliseconds, or inaccurate coordinate systems will cascade into flawed neural reconstructions and synthetic data.

Without a well-orchestrated data pipeline, even the most advanced simulation platforms end up consuming imperfect data, wasting weeks of effort and thousands of dollars in testing and compute costs.

In a first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll discover how to:

• Eliminate manual data audits with an automated workflow that calibrates, aligns, and ensures data integrity across cameras, LiDAR, radar, and other sensors
• Curate and enrich the data for neural reconstructions and synthetic data generation
• Reduce Physical AI testing and QA costs by up to 80%
• Accelerate Physical AI development from months to days

Who should attend:

• Data Engineers, MLOps & ML Engineers working with Physical AI data
• Technical leaders and Managers driving Physical AI projects from prototype to production
• AV/Robotics Researchers building safety-critical apps with cutting-edge tech
• Product & Strategy leaders seeking to accelerate development while reducing infra costs and risks.

About the Speakers

Itai H Zadok is a Senior Product Manager l Autonomous Vehicles Simulation at NVIDIA

Daniel Gural is a Machine Learning Engineer and Evangelist at Voxel51

Join Voxel51 and NVIDIA as they unveil a breakthrough that’s changing how Physical AI systems are built. In this first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll learn how to create validated, simulation-ready data pipelines—cutting testing costs, eliminating manual data audits, and accelerating development from months to days.

Date and Location

Nov 5, 2025 9:00-10:30 AM Pacific Online. Register for the Zoom

Developing autonomous vehicles and humanoid robots requires rigorous simulations that capture real-world complexity. The critical barrier that keeps teams from achieving success isn’t the simulation engine itself, but the data that powers it.

As Physical AI systems ingest petabytes of multisensor data, converting this raw input into validated, simulation-ready data pipelines remains a hidden bottleneck. A camera-to-LiDAR projection off by a few pixels, timestamps misaligned by a few milliseconds, or inaccurate coordinate systems will cascade into flawed neural reconstructions and synthetic data.

Without a well-orchestrated data pipeline, even the most advanced simulation platforms end up consuming imperfect data, wasting weeks of effort and thousands of dollars in testing and compute costs.

In a first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll discover how to:

• Eliminate manual data audits with an automated workflow that calibrates, aligns, and ensures data integrity across cameras, LiDAR, radar, and other sensors
• Curate and enrich the data for neural reconstructions and synthetic data generation
• Reduce Physical AI testing and QA costs by up to 80%
• Accelerate Physical AI development from months to days

Who should attend:

• Data Engineers, MLOps & ML Engineers working with Physical AI data
• Technical leaders and Managers driving Physical AI projects from prototype to production
• AV/Robotics Researchers building safety-critical apps with cutting-edge tech
• Product & Strategy leaders seeking to accelerate development while reducing infra costs and risks.

About the Speakers

Itai H Zadok is a Senior Product Manager l Autonomous Vehicles Simulation at NVIDIA

Daniel Gural is a Machine Learning Engineer and Evangelist at Voxel51

Join Voxel51 and NVIDIA as they unveil a breakthrough that’s changing how Physical AI systems are built. In this first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll learn how to create validated, simulation-ready data pipelines—cutting testing costs, eliminating manual data audits, and accelerating development from months to days.

Date and Location

Nov 5, 2025 9:00-10:30 AM Pacific Online. Register for the Zoom

Developing autonomous vehicles and humanoid robots requires rigorous simulations that capture real-world complexity. The critical barrier that keeps teams from achieving success isn’t the simulation engine itself, but the data that powers it.

As Physical AI systems ingest petabytes of multisensor data, converting this raw input into validated, simulation-ready data pipelines remains a hidden bottleneck. A camera-to-LiDAR projection off by a few pixels, timestamps misaligned by a few milliseconds, or inaccurate coordinate systems will cascade into flawed neural reconstructions and synthetic data.

Without a well-orchestrated data pipeline, even the most advanced simulation platforms end up consuming imperfect data, wasting weeks of effort and thousands of dollars in testing and compute costs.

In a first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll discover how to:

• Eliminate manual data audits with an automated workflow that calibrates, aligns, and ensures data integrity across cameras, LiDAR, radar, and other sensors
• Curate and enrich the data for neural reconstructions and synthetic data generation
• Reduce Physical AI testing and QA costs by up to 80%
• Accelerate Physical AI development from months to days

Who should attend:

• Data Engineers, MLOps & ML Engineers working with Physical AI data
• Technical leaders and Managers driving Physical AI projects from prototype to production
• AV/Robotics Researchers building safety-critical apps with cutting-edge tech
• Product & Strategy leaders seeking to accelerate development while reducing infra costs and risks.

About the Speakers

Itai H Zadok is a Senior Product Manager l Autonomous Vehicles Simulation at NVIDIA

Daniel Gural is a Machine Learning Engineer and Evangelist at Voxel51

Join Voxel51 and NVIDIA as they unveil a breakthrough that’s changing how Physical AI systems are built. In this first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll learn how to create validated, simulation-ready data pipelines—cutting testing costs, eliminating manual data audits, and accelerating development from months to days.

Date and Location

Nov 5, 2025 9:00-10:30 AM Pacific Online. Register for the Zoom

Developing autonomous vehicles and humanoid robots requires rigorous simulations that capture real-world complexity. The critical barrier that keeps teams from achieving success isn’t the simulation engine itself, but the data that powers it.

As Physical AI systems ingest petabytes of multisensor data, converting this raw input into validated, simulation-ready data pipelines remains a hidden bottleneck. A camera-to-LiDAR projection off by a few pixels, timestamps misaligned by a few milliseconds, or inaccurate coordinate systems will cascade into flawed neural reconstructions and synthetic data.

Without a well-orchestrated data pipeline, even the most advanced simulation platforms end up consuming imperfect data, wasting weeks of effort and thousands of dollars in testing and compute costs.

In a first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll discover how to:

• Eliminate manual data audits with an automated workflow that calibrates, aligns, and ensures data integrity across cameras, LiDAR, radar, and other sensors
• Curate and enrich the data for neural reconstructions and synthetic data generation
• Reduce Physical AI testing and QA costs by up to 80%
• Accelerate Physical AI development from months to days

Who should attend:

• Data Engineers, MLOps & ML Engineers working with Physical AI data
• Technical leaders and Managers driving Physical AI projects from prototype to production
• AV/Robotics Researchers building safety-critical apps with cutting-edge tech
• Product & Strategy leaders seeking to accelerate development while reducing infra costs and risks.

About the Speakers

Itai H Zadok is a Senior Product Manager l Autonomous Vehicles Simulation at NVIDIA

Daniel Gural is a Machine Learning Engineer and Evangelist at Voxel51

Join Voxel51 and NVIDIA as they unveil a breakthrough that’s changing how Physical AI systems are built. In this first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll learn how to create validated, simulation-ready data pipelines—cutting testing costs, eliminating manual data audits, and accelerating development from months to days.

Date and Location

Nov 5, 2025 9:00-10:30 AM Pacific Online. Register for the Zoom

Developing autonomous vehicles and humanoid robots requires rigorous simulations that capture real-world complexity. The critical barrier that keeps teams from achieving success isn’t the simulation engine itself, but the data that powers it.

As Physical AI systems ingest petabytes of multisensor data, converting this raw input into validated, simulation-ready data pipelines remains a hidden bottleneck. A camera-to-LiDAR projection off by a few pixels, timestamps misaligned by a few milliseconds, or inaccurate coordinate systems will cascade into flawed neural reconstructions and synthetic data.

Without a well-orchestrated data pipeline, even the most advanced simulation platforms end up consuming imperfect data, wasting weeks of effort and thousands of dollars in testing and compute costs.

In a first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll discover how to:

• Eliminate manual data audits with an automated workflow that calibrates, aligns, and ensures data integrity across cameras, LiDAR, radar, and other sensors
• Curate and enrich the data for neural reconstructions and synthetic data generation
• Reduce Physical AI testing and QA costs by up to 80%
• Accelerate Physical AI development from months to days

Who should attend:

• Data Engineers, MLOps & ML Engineers working with Physical AI data
• Technical leaders and Managers driving Physical AI projects from prototype to production
• AV/Robotics Researchers building safety-critical apps with cutting-edge tech
• Product & Strategy leaders seeking to accelerate development while reducing infra costs and risks.

About the Speakers

Itai H Zadok is a Senior Product Manager l Autonomous Vehicles Simulation at NVIDIA

Daniel Gural is a Machine Learning Engineer and Evangelist at Voxel51

Join Voxel51 and NVIDIA as they unveil a breakthrough that’s changing how Physical AI systems are built. In this first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll learn how to create validated, simulation-ready data pipelines—cutting testing costs, eliminating manual data audits, and accelerating development from months to days.

Date and Location

Nov 5, 2025 9:00-10:30 AM Pacific Online. Register for the Zoom

Developing autonomous vehicles and humanoid robots requires rigorous simulations that capture real-world complexity. The critical barrier that keeps teams from achieving success isn’t the simulation engine itself, but the data that powers it.

As Physical AI systems ingest petabytes of multisensor data, converting this raw input into validated, simulation-ready data pipelines remains a hidden bottleneck. A camera-to-LiDAR projection off by a few pixels, timestamps misaligned by a few milliseconds, or inaccurate coordinate systems will cascade into flawed neural reconstructions and synthetic data.

Without a well-orchestrated data pipeline, even the most advanced simulation platforms end up consuming imperfect data, wasting weeks of effort and thousands of dollars in testing and compute costs.

In a first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll discover how to:

• Eliminate manual data audits with an automated workflow that calibrates, aligns, and ensures data integrity across cameras, LiDAR, radar, and other sensors
• Curate and enrich the data for neural reconstructions and synthetic data generation
• Reduce Physical AI testing and QA costs by up to 80%
• Accelerate Physical AI development from months to days

Who should attend:

• Data Engineers, MLOps & ML Engineers working with Physical AI data
• Technical leaders and Managers driving Physical AI projects from prototype to production
• AV/Robotics Researchers building safety-critical apps with cutting-edge tech
• Product & Strategy leaders seeking to accelerate development while reducing infra costs and risks.

About the Speakers

Itai H Zadok is a Senior Product Manager l Autonomous Vehicles Simulation at NVIDIA

Daniel Gural is a Machine Learning Engineer and Evangelist at Voxel51

Join Voxel51 and NVIDIA as they unveil a breakthrough that’s changing how Physical AI systems are built. In this first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll learn how to create validated, simulation-ready data pipelines—cutting testing costs, eliminating manual data audits, and accelerating development from months to days.

Date and Location

Nov 5, 2025 9:00-10:30 AM Pacific Online. Register for the Zoom

Developing autonomous vehicles and humanoid robots requires rigorous simulations that capture real-world complexity. The critical barrier that keeps teams from achieving success isn’t the simulation engine itself, but the data that powers it.

As Physical AI systems ingest petabytes of multisensor data, converting this raw input into validated, simulation-ready data pipelines remains a hidden bottleneck. A camera-to-LiDAR projection off by a few pixels, timestamps misaligned by a few milliseconds, or inaccurate coordinate systems will cascade into flawed neural reconstructions and synthetic data.

Without a well-orchestrated data pipeline, even the most advanced simulation platforms end up consuming imperfect data, wasting weeks of effort and thousands of dollars in testing and compute costs.

In a first-ever demo featuring NVIDIA Omniverse NuRec and NVIDIA Cosmos with FiftyOne, you’ll discover how to:

• Eliminate manual data audits with an automated workflow that calibrates, aligns, and ensures data integrity across cameras, LiDAR, radar, and other sensors
• Curate and enrich the data for neural reconstructions and synthetic data generation
• Reduce Physical AI testing and QA costs by up to 80%
• Accelerate Physical AI development from months to days

Who should attend:

• Data Engineers, MLOps & ML Engineers working with Physical AI data
• Technical leaders and Managers driving Physical AI projects from prototype to production
• AV/Robotics Researchers building safety-critical apps with cutting-edge tech
• Product & Strategy leaders seeking to accelerate development while reducing infra costs and risks.

About the Speakers

Itai H Zadok is a Senior Product Manager l Autonomous Vehicles Simulation at NVIDIA

Daniel Gural is a Machine Learning Engineer and Evangelist at Voxel51