Explore Stanford technologies available for licensing:

Stanford researchers in the WE3 and S3 Labs developed a cloud-based computation and predictive control platform for wastewater treatment facilities energy storage and energy generation. Wastewater treatment is energy and cost intensive.

Stanford engineers have developed an optical modulator to enable low-cost and high spatial-resolution time-of-flight imaging and LiDAR with low-cost standard image sensors.

Researchers at Stanford have developed an imitation learning method, IQ-Learn, shown to surpass existing methods in some applications.

Stanford researchers have developed a self-affirmation web application to improve students' mental wellbeing.

Researchers at Stanford have developed a cloud-based behind-the-meter (BTM) system that can cut energy costs and reduce reliance on the grid close to 93% respectively.

Stanford researchers have developed new Fast Quick Error Detection (Fast QED) tests that are four orders of magnitude faster than standard QED tests while also preserving quick error detection properties.

During post-silicon validation and debug, manufactured integrated circuits (ICs) are tested in actual system environments to detect and fix design flaws (bugs). Existing techniques are costly due to ad hoc, manual methods.

Despite their compact form factor and low power consumption, resonant scanners have not been widely applied to LiDAR due to their scanning trajectory.

Researchers at Stanford have developed a low-power phase-change memory (PCM) technology with interfacial thermoelectric heating enhancement.

Near-infrared (NIR) imaging is a valuable research tool that produces quality images with high spatial and temporal resolution through millimeter tissue depths.

Researchers in Roger Kornberg's lab have developed a deep convolutional neural network algorithm that predicts the location and strength of transcription factor activation domains (ADs) in eukaryotes.

This invention is a machine learning algorithm for determining coronary artery calcification (CAC) scoring from routine CT scans.

Researchers at Stanford have developed a potentially best-in-class method for performing knowledge graph completion tasks.

Many industries rely on the ability to predict and understand changes over time. Such changes include understanding the economical trend, emergence of infectious disease, and patterns in human behavior.

Stanford researchers have an efficient method to measure gradients simultaneously and in parallel, as related to an optical network.