Researchers at Stanford are advancing a new class of nonlinear optical devices that operate with significantly lower energy requirements than previous platforms.
Researchers at Stanford have developed, for the first time, a component analysis algorithm that does not require any assumption on the data structure or data generation process to find out the important components or trends in data.
Multiple Sclerosis (MS) is a potentially disabling autoimmune disease whereby autoactivated T and B cells attack and destroy protective myelin sheaths of the central nervous system(CNS).
Stanford researchers in the Fan Lab have developed a photonic device optimizer that generates designs with hard geometric constraints to guarantee device fabricability.
Stanford researchers have developed a time efficient and safer algorithm for autonomous cars that combines game theory and risk awareness. This algorithm computes approximate feedback Nash equilibria where all agents are risk aware, a novel approach.
Researchers at Stanford have developed a distributed digital "black box" audit trail design for connected and automated vehicle data and software assurance.
Researchers at Stanford University have designed a scalable photonic quantum computer which does not require single-photon detectors and which uses minimal quantum resources: one coherently controlled atom.
Stanford researchers have developed a scanning mirror and method for Rhodonea (Rose) scanning patterns, which are superior to Lissajous patterns for almost all imaging and ranging applications.
This methodology computes the marginal energy utilization for supplying individual water users based on the existing topology of the water distribution network (WDN), pipe sizes and baseline flows.
Stanford researchers at Prof. Safavi-Naeini's laboratory have developed a high quality, scalable processor architecture using small, phononic crystal resonators for read-out and long-lived storage in superconducting circuit quantum computing.
The CheXbert labeler accurately detects the presence or absence of 14 common medical conditions in radiology reports, converting unstructured radiology text into a structured format.
The Murmann lab has developed a method for an extraction information from acoustic signals that utilizes low power consumption. N-path filters are used to decompose the original acoustic signals' waveform before downconverting to lower their Nyquist-rate bandwidth.