VoxelDyn Labs was founded to eliminate the latency and complexity barriers of 3D geometry generation. We bridge physical reality and generative AI through sub-40ms spatial neural networks.
We believe that embodied robots, drones, and spatial computing headsets require perception and synthetic world models that respond at the speed of biological reflexes (< 40ms).
Every feature we design is exposed via clean, deterministic REST, gRPC, and Python SDKs. We believe AI infrastructure should integrate seamlessly into existing software architectures.
Customer data and intellectual property are sacrosanct. We maintain a zero-retention foundation model training policy and operate within isolated tenant environments.
Generative 3D and 4D Gaussian Splatting require immense GPU memory bandwidth and floating-point throughput. VoxelDyn Labs deploys its foundation models across distributed AWS EC2 P5 and G5 GPU clusters, leveraging AWS SageMaker HyperPod and AWS Inferentia2 silicon.
Pioneering the intersection of computer graphics, robotics kinematics, and generative deep learning.
AI systems researcher and engineer with deep expertise in 4D Gaussian Splatting, high-performance CUDA computing, and spatial foundation models.
Specializing in dual-marching-cubes polygonization, neural field deformation, and multi-view generative priors.
Former cloud systems architect focused on multi-AZ GPU orchestration, low-latency gRPC streaming, and AWS SageMaker scale.