NeuroTensor Labs Inc. was founded with a singular conviction: genuine robotic autonomy cannot rely on slow, remote cloud APIs. True physical intelligence must run locally, deterministically, and in real time on accelerated edge silicon.
Bridging the gap between frontier deep learning research and high-reliability industrial hardware systems.
Dr. Aryan Mehta holds a Ph.D. in Computer Science from the Stanford Artificial Intelligence Laboratory (SAIL), where his doctoral research focused on self-supervised 3D scene representation networks and real-time vision-action policies for embodied agents. Prior to co-founding NeuroTensor Labs, Dr. Mehta served as Senior Perception Scientist at leading autonomous robotics laboratories, leading the development of multi-camera obstacle detection systems deployed on over 10,000 industrial vehicles.
He has authored more than 12 peer-reviewed scientific papers across top computer vision and robotics venues including IEEE CVPR, ICCV, NeurIPS, and IROS, accumulating over 2,400 citations. At NeuroTensor Labs, Dr. Mehta directs the company’s scientific roadmap, foundation model pre-training regimens, and strategic commercial partnerships with global robotics OEMs.
"We are entering an era where robots will perceive geometry not as isolated bounding boxes, but as rich, actionable spatial affordances. Running that intelligence at 120 FPS on an embedded NVIDIA Jetson chip changes everything."
PhD CMU • ex-NVIDIA Fellow
Doctorate in High-Performance GPU Computing from Carnegie Mellon University. Renowned specialist in warp-level CUDA microkernels, low-precision FP8 quantization, and hardware-aware transformer scheduling. Former research intern in NVIDIA's Autonomous Machines group, where she optimized early versions of TensorRT inference graphs.
ex-AWS HPC Systems Architect
Over a decade of experience designing and scaling multi-thousand GPU clusters connected by 400Gbps NVIDIA Quantum-2 InfiniBand networks. Architected mission-critical HPC environments and leads our distributed Megatron-LM pre-training clusters, Triton serving mesh, and automated model deployment pipelines.
Professor of Robotics & Autonomous Perception
Advises on geometric scene graphs, formal safety verification in robotic trajectory planning, and sim-to-real transfer protocols.
Former VP of Industrial Automation Engineering
Guides commercial OEM pilot implementations, factory safety compliance (ISO 3691-4), and real-time deterministic ROS2 integrations.
