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.
PhD Stanford AI Lab • 12+ CVPR Publications
Tanaji Vishnu Yadav 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 founding NeuroTensor Labs, Mr. Yadav 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, Mr. Yadav 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 275 TOPS edge processor changes everything."
Proprietary continuous 3D tokenization networks mapping raw stereoscopic photon streams directly into 6-DoF manipulator and mobile robot actuation controls.
Sub-8ms deterministic latency achieved via custom warp-level CUDA microkernels, low-precision FP8 SmoothQuant, and asynchronous CUDA stream pipelining.
100,000+ photorealistic industrial simulation scenarios running concurrently on cloud physics digital twins to guarantee sub-millimeter sim-to-real transfer.
Doctoral foundation in geometric deep learning and embodied perception.
Peer-reviewed publications with 2,400+ international academic citations.
Applying for GPU compute cluster credits to accelerate 70B foundation models.
Formal legal structure, clean IP assignment, and institutional governance.
